Search

[Display omitted] • Reviews the latest advances in solid-state nanopore technology and its challenges. • Exploring methodology to fabricate high precision nanopores to improve resolution. • Providing insights in the preferable technique for solid-state nanopore DNA sequencing. • The potential of solid-state nanopore DNA sequencing for single biomolecule applications. Solid-state nanopore technology has been widely used for the analysis of a variety of biomolecules and will be applied to more aspects of analysis in life sciences and clinical applications. Providing a label-free, polymerase chain reaction-free approach, solid-state nanopore DNA sequencing is anticipated to become a promising next-generation technology for deciphering DNA sequences, alongside attributes such as longer read lengths, high throughput, and cost-effectiveness. It could also play a significant role in the future of disease diagnosis and treatment, and genetic disease prevention applications. This paper reviews the latest advances in the field of solid-state nanopore technology, and discusses its challenges and prospects in DNA sequencing applications. We explore methodologies to fabricate high precision nanopores, as well as to improve spatial and temporal resolution for solid-state nanopores. We also explore the potential for optimizing and improving the feasibility of solid-state nanopore DNA sequencing, with a view toward the use of these devices for single biomolecule detection and single nucleotide recognition applications. This paper aims to provide insights into the preferable technique for researching on solid-state nanopore DNA sequencing technology and its clinical applications, even for RNA and protein sequencing applications. [ABSTRACT FROM AUTHOR]

Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mathematics, medicines, and computer science are teaming up for getting better solutions in the said field. In this paper, we have discussed different methods of secondary and tertiary protein structure prediction (PSP), along with the limitations of different approaches. Different types of datasets used in PSP are also discussed here. This paper also tells about different performance measures to evaluate the prediction accuracy of PSP methods. Different software's/servers are available for download, which are used to find the protein structures for the input protein sequence. These softwares will also help to compare the performance of any new algorithm with other available methods. Details of those softwares are also mentioned in this paper. [ABSTRACT FROM AUTHOR]

• This review paper underscores how recent advances in artificial intelligence, particularly deep learning models, have revolutionized antibody design by accurately predicting antibody structures from amino acid sequences, addressing historical challenges in antibody structure prediction. • This paper discusses how AI techniques have significantly improved the accuracy of predicting antibody-antigen interactions, enhancing our understanding of antibody recognition. It emphasizes the importance of combining computational tools with experimental methods for more precise predictions. • This review paper sheds light on how AI methods, especially deep learning, have streamlined the process of antibody affinity maturation by predicting mutation effects. It emphasizes the need for considering environmental factors and molecular water in mutation prediction models. Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody–antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field. [ABSTRACT FROM AUTHOR]

The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this paper, we construct an interpretable model, ECA-PHV, to predict human-virus protein-protein interactions based on an effective channel attention mechanism. First, we utilize five coding modalities, namely AAC, DDE, MMI, CT, and GTPC, to extract the hidden biological information in protein sequences. Individual feature weights are then learned by using a differential evolutionary algorithm that employs weighted combinations to adequately represent various protein sequence information. Next, irrelevant features in multi-information fusion are removed by Group Lasso. Finally, the prediction model is constructed by combining effective channel attention, BiGRU, and 1D-CNN. Compared with existing models, the interpretability framework ECA-PHV proposed in this paper has competitive and stable predictive performance. This shows that our model can efficiently focus on important information about protein sequences. In conclusion, this study accelerates the exploration of human-virus protein-protein interactions and provides some insights of practical value for probing human-virus relationships. • A novel method (ECA-PHV) to predict human-virus protein-protein interactions (human-virus PPIs). • The AAC, DDE, CT, MMI, and GTPC methods are fused to extract protein sequence feature information. • We first constructed an interpretable deep learning framework that combines BiGRU, 1D-CNN, and efficient channel attention to predict human-virus protein-protein interactions. • ECA-PHV increases the prediction performance compared with other methods. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) and proteins containing intrinsically disordered protein regions (IDPRs) often affect protein structure determination. IDP/IDPRs cause failures in the protein structure determination pipeline leading to enhanced experimental cost and time. Hence the primary sequence of proteins is often analyzed using disorder prediction servers so that manipulations can be made to the protein sequence to aid its expression, purification, and crystallization. Assessment of existing IDP/IDPR prediction methods with CASP 10 targets shows the scope of improvement of prediction. In this paper, an attempt has been made to develop a hydrophobicity and net charge based artificial neural network model for protein disorder prediction. Subsequently, a prediction tool has been developed using Java using the Neural Network Schema. The prediction tool has been tested with CASP 10 targets and the developed predictor is found to outperform other predictors i.e RONN, PONDR VLXT, DisEMBL, FOLDINDEX, and GLOBPLOT in Sensitivity (0.983), Precision (0.854), Accuracy (0.937), MCC (0.840), and AUC (0.937). DisEMBL and GLOBPLOT are found to have better specificity i.e 0.909 and 0.979 respectively than the proposed predictor i.e 0.890. [ABSTRACT FROM AUTHOR]

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space X as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on X between the prior distribution of data and the empirical distribution of the sample. A number of distances on X are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on X based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on X , with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families. [ABSTRACT FROM AUTHOR]

• We describe a repository of FDA-approved protein- or peptide-based drugs. • We discuss the various routes used to administer therapeutic proteins. • The manually curated THPdb2 database contains comprehensive information describing 894 unique therapeutic proteins and peptides. • User-friendly search and similarity search tools are integrated into the database. During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders. [ABSTRACT FROM AUTHOR]

The tremendous growth the pharmaceutical field witnessed especially concerning drug-development, caused drug-related experiments such as those concerned with estimating their interactions with targets harder to conduct. This issue led researchers to transition from vivo and vitro to selico experimenting methods, which deploy artificial intelligence (AI) techniques such as recommender systems (RS) to solve problems at hand. However, most RSs depend on global features such as indices, which render them incapable of fully understanding the behaviors of individuals. In this paper, we introduce our Convolutional Recurrent Deep Learning model-based RS approach, or 'RSCRDL', which tackles the interaction prediction problem from a natural language processing approach's (NLP) point of view. RSCDRL processes the textual representations of both protein sequences and drugs in order to learn the implicit features of the tokens forming them, rendering it able to process unseen and non-reactive individuals indirectly. We also present a greedier variant of RSCRDL that can exploit additional drugs explicit features for better predictive abilities. Experiments performed on two real-world datasets show significant outperformance achieved by our models over two general-purpose state-of-the-art RSs, while other experiments conducted on orphan protein sequences helped deduce drug candidates that are assumed to be capable of inhibiting their activities. [ABSTRACT FROM AUTHOR]

• A novel model-based clustering approach for sequential data, nTreeClus, is proposed. • nTreeClus introduces a Decision Tree Path encoder in an autoregressive manner. • The method's robustness to its only parameter (window size) has been examined. • nTreeClus shows competitive results compared to existing methods in sequence mining. The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a better understanding of their (dis) similarities. This paper proposes a new Model-based approach for clustering sequence data, namely nTreeClus. The proposed method deploys Tree-based Learners, k -mers, and autoregressive models for categorical time series, culminating with a novel numerical representation of the categorical sequences. Adopting this new representation, we cluster sequences, considering the inherent patterns in categorical time series. Accordingly, the model showed robustness to its parameter. Under different simulated scenarios, nTreeClus improved the baseline methods for various internal and external cluster validation metrics for up to 10.7% and 2.7%, respectively. The empirical evaluation using synthetic and real datasets, protein sequences, and categorical time series showed that nTreeClus is competitive or superior to most state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In computational biology, biological database search has been playing a very important role. Since the COVID-19 outbreak, it has provided significant help in identifying common characteristics of viruses and developing vaccines and drugs. Sequence alignment, a method finding similarity, homology and other information between gene/protein sequences, is the usual tool in the database search. With the explosive growth of biological databases, the search process has become extremely time-consuming. However, existing parallel sequence alignment algorithms cannot deliver efficient database search due to low utilization of the resources such as cache memory and performance issues such as load imbalance and high communication overhead. In this paper, we propose an efficient sequence alignment algorithm on Sunway TaihuLight, called ESA, for biological database search. ESA adopts a novel hybrid alignment algorithm combining local and global alignments, which has higher accuracy than other sequence alignment algorithms. Further, ESA has several optimizations including cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. They are implemented in a heterogeneous processor SW26010 adopted in the world's 6th fastest supercomputer, Sunway TaihuLight. The implementation of ESA is evaluated with the Swiss-Prot database on Sunway TaihuLight and other platforms. Our experimental results show that ESA has a speedup of 34.5 on a single core group (with 65 cores) of Sunway TaihuLight. The strong and weak scalabilities of ESA are tested with 1 to 1024 core groups of Sunway TaihuLight. The results show that ESA has linear weak scalability and very impressive strong scalability. For strong scalability, ESA achieves a speedup of 338.04 with 1024 core groups compared with a single core group. We also show that our proposed optimizations are also applicable to GPU, Intel multicore processors, and heterogeneous computing platforms. • In this paper, we propose and implement an efficient sequence alignment algorithm, ESA, for biological database search on SW26010 heterogeneous processors. This algorithm adopts both local and global alignments for biological database search with several optimizations. ESA achieves high computational performance without sacrificing accuracy. To the best of our knowledge, this is the first attempt to parallelize hybrid sequence alignment on Sunway TaihuLight using multi-level optimizations. • We propose three optimization strategies in ESA: cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. Cache-aware sequence alignment effectively reduces the size of the data structure for sequence alignment and fully utilizes the vectorization of the slave cores of SW26010. With capacity-aware load balancing, we distribute the workload evenly among the cores of SW26010. With bandwidth-aware data transfer, ESA reduces the communication overhead by using asynchronous DMA transmission and RLC. • We evaluate the performance of ESA using the Swiss-Prot database on Sunway TaihuLight. Our experimental results show that ESA achieves a speedup of 34.5 times on a single CG over the manager core. Compared with a serial implementation on Intel (R) Xeon (R) CPU E5-2620 v4 processor, ESA achieves a speedup of 21.6 on a single CG. We also demonstrate that ESA has linear weak scalability and very competitive strong scalability. Finally, we compare ESA with mainstream algorithms on the CPU+GPU platform and achieve the highest GCUPS of 228.91. [ABSTRACT FROM AUTHOR]

Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature. [ABSTRACT FROM AUTHOR]

In recent years, there is a growing interest in the role of bioactive peptides obtained from natural food resources such as animal muscle proteins. Bioactive peptides/enzymatic protein hydrolysates are biomolecules broken down from proteins due to enzymatic hydrolysis, which usually contain 2–20 amino acids units with significant biological properties including antioxidant, anti-diabetic, and anti-cancer activities. Protein hydrolysates or peptides provide a natural therapeutics for controlling diabetes (type-II) by reducing amylase enzymatic activity and degrade the natural incretin hormone involved in blood glucose regulation. Such biological properties of peptides are highly correlated to sequence of amino acids and size. These bioactive molecules can be isolated, purified, and identified following different approaches such as gel filtration chromatography, Mass spectrometry approaches among others. In general, the animal muscles could be used as sustainable protein sources in the production of bioactive peptide-based functional foods and natural drugs for preventing chronic diseases. This review article aims to summarize the antioxidant, anti-diabetic, and anti-cancer activities of muscle proteins hydrolysates and purified bioactive peptides. [Display omitted] • This review paper discusses on functionality and bioactive characteristics of peptides. • Bioactive peptides can be produced through several enzymatic hydrolysis. • Specific bioactive peptides can be purified through different novel approaches. • Polar and non-polar peptides has anti-cancer and anti-diabetic properties. [ABSTRACT FROM AUTHOR]

• Structure and properties of β-casein mutants in dairy products were summarized. • Impact of A1/A2 β-casein on gastrointestinal health in vivo studies was analyzed. • The detection method of β-casomorhin7 and A1/A2 β-casein was summarized. • Limitations and future directions in this promising field were presented. β-casein is the second most abundant form of casein in milk. Changes in amino acid sequence at specific positions in the primary structure of β-casein in milk will produce gene mutations that affect the physicochemical properties of dairy products and the hydrolysis site of digestive enzymes. The screening method of β-casein allele frequency detection in dairy products also has attracted the extensive attention of scientists and farmers. The A1 and A2 β-casein is the two usual mutation types, distinguished by histidine and proline at position 67 in the peptide chain. This paper summarizes the effects of A1 and A2 β-casein on the physicochemical properties of dairy products and evaluates the effects on human health, and the genotyping methods were also concluded. Impressively, this review presents possible future opportunities and challenges for the promising field of A2 β-casein, providing a valuable reference for the development of the functional dairy market. [ABSTRACT FROM AUTHOR]

• Virus variants are represented by 4-letter sequence based on AA mutations on spike. • An n -distance algorithm in UPGMA is utilized to derive variant phylogenetic tree. • Discovering new strain involves union of mutated sites and randomly generated set X. • New macro-lineage of SARS-CoV-2 is predicted when X reaches a sufficient scale. • Demarcation values that differentiate between various macro-lineages are derived. With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n -distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities. [Display omitted] • Machine learning and virtual digestion were combined for the first time to enzymatic acquisition of naturally active peptides. • The SSA-LSTM machine learning model exhibited high accuracy performance in identifying neuroprotective peptides. • The neuroprotective efficacy of walnut protein hydrolysate obtained by SSA-LSTM-VD was verified by the mice model. [ABSTRACT FROM AUTHOR]

The usage of antigen-functionalized nanoparticles has become a major focus in the field of experimental HIV-1 vaccine research during the last decade. Various molecular mechanisms to couple native-like trimers of the HIV-1 envelope protein (Env) onto nanoparticle surfaces have been reported, but many come with disadvantages regarding the coupling efficiency and stability. In this study, a short amino acid sequence ("aldehyde-tag") was introduced at the C-terminus of a conformationally stabilized native-like Env. The post-translational conversion of a tag-associated cysteine to formylglycine creates a site-specific aldehyde group without alteration of the Env antigenicity. This aldehyde group was further utilized for bioconjugation of Env trimers. We demonstrated that the low acidic environment necessary for this bioconjugation is not affecting the trimer conformation. Furthermore, we developed a two-step coupling method for pH-sensitive nanoparticles. To this end, we conjugated aldehyde-tagged Env with Propargyl-PEG 3 -aminooxy linker (oxime ligation; Step-one) and coupled these conjugates by copper-catalyzed azide-alkyne cycloaddition (Click reaction; Step-two) to calcium phosphate nanoparticles (CaPs) functionalized with terminal azide groups. CaPs displaying orthogonally arranged Env trimers on their surface (o-CaPs) were superior in activation of Env-specific B-cells (in vitro) and induction of Env-specific antibody responses (in vivo) compared to CaPs with Env trimers coupled in a randomly oriented manner. Taken together, we present a reliable method for the site-specific, covalent coupling of HIV-1 Env native-like trimers to the surface of nanoparticle delivery systems. This method can be broadly applied for functionalization of nanoparticle platforms with conformationally stabilized candidate antigens for both vaccination and diagnostic approaches. During the last decade antigen-functionalized nanoparticles have become a major focus in the field of experimental HIV-1 vaccines. Rational design led to the production of conformationally stabilized HIV-1 envelope protein (Env) trimers - the only target for the humoral immune system. Various molecular mechanisms to couple Env trimers onto nanoparticle surfaces have been reported, but many come with disadvantages regarding the coupling efficiency and stability. In this paper, we describe a highly selective bio-conjugation of Env trimers to the surface of medically relevant calcium phosphate nanoparticles. This method maintains the native-like protein conformation and has a broad potential application in functionalization of nanoparticle platforms with stabilized candidate antigens (including stabilized spike proteins of coronaviruses) for both vaccination and diagnostic approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The study and identification of protein post-translational modification(PTM) sites in rice seeds plays an important role in breeding and yield increasing in intelligent agriculture. Deep learning, especially deep convolutional neural networks(CNN) become more and more popular in intelligent agriculture because of its excellent image processing and data analysis capabilities. In this paper, the Denoised-Oversampling is adopted to denoise the samples and balance the data. The protein sequences are transformed into corresponding numerical feature vectors. Then, the processed feature subset is input to the deep convolution network. In fully connected layers, we used the L2 regularization method to prevent the overfitting problem. The phosphorylation sites are predicted by the sigmoid classifier. Finally, in the experimental part, in order to truly reflect the predictive power of the model, we divided the dataset into training set, validation set and independent test set. The prediction accuracy of the independent test set is 83.20%. The test result shows that the proposed algorithm has better prediction performance than the existing phosphorylation prediction algorithms. [ABSTRACT FROM AUTHOR]

• The Gouy-Chapman-Stern layer model combines the Helmholtz and the diffuse double layer of the Gouy-Chapman model to explain the development of the double-layer capacitance on the sensor's surface. • The Site-binding model is used to describe the surface charge developed at the oxide/electrolyte interface. • This paper presents a new methodology that combines these two theories, and our new method is applied to AAs (amino acids) immobilized on the sensor surface. • Moreover, our simulation work shows that each AA has a unique signature (fingerprints) that can be used to distinguish and identify each AA. In this paper, we present simulation results of a BioFET using methodology based on the Gouy-Chapman-Stern and the Site-binding models. The derived simulation approach is used to sense different amino acids, such as Arginine (R), Aspartic Acid (D) and Proline (P), functionalized with the help of a linker connected to the gate-oxide. The performance of the BioFETs is optimized while analyzing the effect of high-k dielectrics as the gate oxide. In general, the channel oxides are responsible for tuning the parameters such as sensitivity, surface potential and intrinsic buffer capacity. The variation of total surface capacitance, the second derivative of drain current and surface potential are used to uniquely identify the signatures of different amino acids. The proposed method can be helpful in defining an efficient method for label-free protein sequencing. [ABSTRACT FROM AUTHOR]

• We have studied the cross-correlation behaviour among 32 mitochondrial genomes. • Integrative CGR and MF-X-DFA approach was used to quantify the cross-correlations. • We found the existence of multifractal behaviour between all these time series. • Cluster analysis was performed to find the class affiliation. We characterized the multifractality and power-law cross-correlation of mitochondrial genomes of various species through the recently developed method which combines the chaos game representation theory and 2D-multifractal detrended cross-correlation analysis. In the present paper, we analyzed 32 mitochondrial genomes of different species and the obtained results show that all the analyzed data exhibit multifractal nature and power-law cross-correlation behaviour. Further, we performed a cluster analysis from the calculated scaling exponents to identify the class affiliation and its outcome is represented as a dendrogram. We suggest that this integrative approach may help the researchers to understand the phylogeny of any kingdom with their varying genome lengths and also this approach may find applications in characterizing the protein sequences, mRNA sequences, next-generation sequencing, and drug development, etc. [ABSTRACT FROM AUTHOR]

Accidentally found, two poisoned brown rats from Hungary were surveyed for presence of circoviral DNA, using specific nested primers, designed against the rep gene of the virus. Both specimens were positive. The whole genomes were amplified using inverse PCR based on the Rep sequence parts and sequenced by the primer walking method. Genomic analyses revealed that these novel rat viruses, together with tawny owl-associated circovirus reported by Italian researchers in 2022, are sequence variations of the same virus from genus Circovirus. In phylogenetic reconstructions, these circovirus strains detected from brown rats clustered closest to circoviruses derived from faeces samples of various predatory mammals. Molecular data as well as the phylogenetic analyses of the complete derived replication-associated protein and the capsid protein, as well as the prey preference of the host species of the recently described tawny owl-associated virus suggest that brown rat could be the evolutionary adapted host of the viruses described in this paper (brown rat circovirus types 1 and 2) and the previously reported tawny owl-associated virus. Possible pathogenic and zoonotic role of these viruses need further studies. • Circoviruses were detected by PCR from organs of free-ranging brown rats. • Full genomes of these viruses were sequenced. • The DNA and protein sequences of the two viruses were compared to each other. • Phylogenetic studies indicated similar circoviruses from predatory mammals and birds. • These viruses are probably genuine circoviruses of rats. [ABSTRACT FROM AUTHOR]

Most intracellular activities of living organisms are performed by proteins that have unique and complex 3-Dimensional (3D) structures, playing very important roles in their operations. Due to importance and challenges of predicting 3D structures for proteins, laboratory methods with limitations such as time consuming and high cost have been developed. Computational methods use a sequence of amino acids to obtain the 3D structure. They encounter with a nondeterministic problem having polynomial completion time (NP-Complete problem); with a chain of amino acids as input, and a protein, with 3D structure, as output. In this paper, a new population-based algorithm, under Predatory Search Strategy-Particle Swarm Optimization (PSS-PSO) framework, is presented for predicting the 3D structure; using Hydrophobic-Polar (HP) model on Face-Centered Cubic (FCC) lattice. In this approach, name TRL-PSSPSO, two new moves are proposed to direct each solution toward the native structure: Local Move for reaching a dense hydrophobic core and large H-H contacts and Meta Move for reaching optimal structure, by using Transfer learning. Two datasets are considered for evaluating and the results on some set of standard protein benchmarks show outperforming the state-of-the-art approaches. They show that the proposed approach can improve the accuracy of template-free prediction in an acceptable manner. [ABSTRACT FROM AUTHOR]

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges. [ABSTRACT FROM AUTHOR]

[Display omitted] • UMP789 database was collected in this paper. • The Umami-BERT model comprehensively outperformed other deep learning models in terms of umami peptides prediction. • The contribution of amino acids to umami peptide was ranked. • The motif pattern concerning A, D, E, and G were analyzed. Umami peptides have received extensive attention due to their ability to enhance flavors and provide nutritional benefits. The increasing demand for novel umami peptides and the vast number of peptides present in food call for more efficient methods to screen umami peptides, and further exploration is necessary. Therefore, the purpose of this study is to develop deep learning (DL) model to realize rapid screening of umami peptides. The Umami-BERT model was devised utilizing a novel two-stage training strategy with Bidirectional Encoder Representations from Transformers (BERT) and the inception network. In the pre-training stage, attention mechanisms were implemented on a large amount of bioactive peptides sequences to acquire high-dimensional generalized features. In the re-training stage, umami peptide prediction was carried out on UMP789 dataset, which is developed through the latest research. The model achieved the performance with an accuracy (ACC) of 93.23% and MCC of 0.78 on the balanced dataset, as well as an ACC of 95.00% and MCC of 0.85 on the unbalanced dataset. The results demonstrated that Umami-BERT could predict umami peptides directly from their amino acid sequences and exceeded the performance of other models. Furthermore, Umami-BERT enabled the analysis of attention pattern learned by Umami-BERT model. The amino acids Alanine (A), Cysteine (C), Aspartate (D), and Glutamicacid (E) were found to be the most significant contributors to umami peptides. Additionally, the patterns of summarized umami peptides involving A, C, D, and E were analyzed based on the learned attention weights. Consequently, Umami-BERT exhibited great potential in the large-scale screening of candidate peptides and offers novel insight for the further exploration of umami peptides. [ABSTRACT FROM AUTHOR]

A protein is a linear chain containing a set of amino acids, which folds on itself to create a specific native structure, called the minimum energy conformation. It is the native structure that determines the functionality of each protein. The Protein Folding Problem (PFP) remains one of the most strenuous computational and chemical biology. The principal challenge of PFP is to predict the optimal conformation of a given protein by considering only its amino acid sequence. Since the conformational space contains a colossal number of possibilities, even when considering short sequences, different simplified models have been developed and applied to make the PFP less complex. Experimental methods can be used to predict the native structure of small and specific proteins. Given the limitations of experimental methods, in the last few years many computational approaches have been proposed to solve the PFP. Based on the folding process, the PFP was formulated as an optimization problem. They are based on simplified lattice models such as the hydrophobic-polar model. In this paper, we present a new Hybrid Cuckoo Search Algorithm (HCSA) to solve the 3D-HP protein folding optimization problem. Our proposed algorithm consists of combining the Cuckoo Search Algorithm (CSA) with the Hill Climbing (HC) algorithm. Simulation results on different benchmark sequences are presented and compared to the state-of-the-art algorithms. • This paper deals with a novel approach for the 3D-HP Protein Folding Problem. • The study uses an algorithm named HCSA, which combines the Cuckoo Search (CS) with the Hill Climbing (HC) algorithms. • Experimental results on different benchmark sequences are presented and compared with state-of-the-art results. [ABSTRACT FROM AUTHOR]

Carbonylation is an irreversible post-translational modification of proteins and regulates various cellular physiological processes. Due to the limitations of experimental methods, it is necessary to predict carbonylation sites by computational methods. In this paper, a new prediction model of carbonylation, Precar_Deep, is proposed. First, six feature extraction methods are used to obtain the original feature space from the protein sequences. Then, the Group LASSO method is used to remove redundant information and the oversampling Borderline-SMOTE method is employed to balance the data to obtain a new feature space. Finally, the processed data is input into the deep learning framework constructed in this paper to predict the carbonylation sites, and the performance of the model is evaluated by using 10-fold cross-validation and independent test datasets. The AUC values of the four datasets are all more than 90%. The experimental results show that PreCar_Deep is superior to other existing models and is helpful to identify protein carbonylation sites. The source code and all datasets are available at https://github.com/QUST-SHULI/PreCar_Deep/. • A novel method (PreCar_Deep) is used to predict carbonylation sites. • The AAC, DC, AAindex, EGAAC, CT and KNN methods are fused to extract protein sequence features information. • Borderline-SMOTE algorithm is utilized to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the CNN and BiLSTM to predict the carbonylation sites. [ABSTRACT FROM AUTHOR]

· A novel method (RPI-CapsuleGAN) to predict RNA-protein interactions (RPI). · Use the elastic net feature selection method to screen high-correlation features as model input features to reduce the complexity of the model operation and avoid overfitting. · The convolutional attention mechanism is introduced into a generative adversarial capsule network to construct a classifier model for the first time. · Predict and analyze RPI network, and analyze the interpretability of RNA-protein interaction network. · RPI-CapsuleGAN increases the prediction performance on independent testing datasets compared with other state-of-the-art methods. RNA-protein interactions (RPI) play a crucial regulatory role in cellular physiological processes. The study and prediction of RPIs can be insightful for exploring disease mechanisms and drug target design. Traditional RPI prediction methods relied mainly on tedious and expensive biological experiments, and there is an increasing interest in developing more cost-effective computational methods to predict RPIs. This work proposes an interpretable RPI-CapsuleGAN method for RPI prediction based on a generative adversarial capsule network with a convolutional block attention module. First, RPI-CapsuleGAN extracts and fuses multiple features to characterize RNA and protein sequences. Subsequently, the elastic net feature selection method is used to retain features that are highly informative to RPI prediction. Finally, we introduce a convolutional attention mechanism into the generative adversarial capsule network for the first time in order to construct the RPI prediction framework, which is shown to improve the model feature learning of interpretable and expression ability, and effectively solves the problem of the disappearance of the model spatial structure hierarchy. Based on a five-fold cross-validation test, the prediction accuracy of the RPI-CapsuleGAN method reaches 97.1%, 88.8%, 92.5%, 97.3%, and 87.8% for datasets RPI488, RPI369, RPI2241, RPI1807, and RPI1446. The RPI-CapsuleGAN method has higher accuracy than state-of-the-art RPI prediction methods that use the same datasets. In the test dataset NPInter227 constructed in this paper, five groups of test sets are composed of positive samples and five groups of negative samples, the prediction accuracy reaches 97.38%, 96.48%, 97.38%, 97.81%, and 97.15%, respectively, outperforming other mainstream deep learning algorithms. In addition, RPI-CapsuleGAN obtained better results for the prediction of independent test datasets. Extensive experiments detailed here show that RPI-CapsuleGAN can provide an efficient, accurate, and stable method for RPI prediction. [ABSTRACT FROM AUTHOR]

A review regarding the studies of light-sensitive systems based on bacteriorhodopsin is presented. Briefly given are modern ideas about bacteriorhodopsin and its molecular properties, about the photocycle of its transformation. The possibilities and ways of bacteriorhodopsin modifications are shown, in particular, such as dehydration, modification using chemical additives, changing the primary protein sequence by use of genetic mutants of bacteriorhodopsin, replacing the chromophore with its synthesized analogues. Such modifications can optimize the use of bacteriorhodopsin to create photosensitive recording media. Particular attention is paid to various areas of possible applications of light-sensitive materials of this type, in particular, polymer films based on bacteriorhodopsin and its derivatives, the so-called Biochrome films. The possibilities of using BR-based polymer films not only as a photochromic material for multiple recording, but also as a material for write-once recording and permanent memory (the so-called material for write-once recording of optical information) are also considered. • Structure and function of bacteriorhodopsin – general conceptions. • Bacteriorhodopsin as a light-sensitive recording medium. • Possible technical applications. • Bacteriorhodopsin as a photochromic material. • Biochrome films based on bacteriorhodopsin and its derivatives. [ABSTRACT FROM AUTHOR]

• To understand dysregulated biological processes, predicting mRNA and protein levels is crucial in clinical applications. • A transformer-based architecture with asymmetric attention (Perceiver) is exploited for mRNA and protein level prediction. • The Perceiver architecture attends to longer range interactions compared to Transformer, CNN, and LSTM. • The proposed model achieves state-of-the-art performance for mRNA level prediction on glioblastoma and lung cancer tissues. • To the best of our knowledge, the protein level prediction task is addressed. [Display omitted] Background and Objective: The functions of an organism and its biological processes result from the expression of genes and proteins. Therefore quantifying and predicting mRNA and protein levels is a crucial aspect of scientific research. Concerning the prediction of mRNA levels, the available approaches use the sequence upstream and downstream of the Transcription Start Site (TSS) as input to neural networks. The State-of-the-art models (e.g., Xpresso and Basenjii) predict mRNA levels exploiting Convolutional (CNN) or Long Short Term Memory (LSTM) Networks. However, CNN prediction depends on convolutional kernel size, and LSTM suffers from capturing long-range dependencies in the sequence. Concerning the prediction of protein levels, as far as we know, there is no model for predicting protein levels by exploiting the gene or protein sequences. Methods: Here, we exploit a new model type (called Perceiver) for mRNA and protein level prediction, exploiting a Transformer-based architecture with an attention module to attend to long-range interactions in the sequences. In addition, the Perceiver model overcomes the quadratic complexity of the standard Transformer architectures. This work's contributions are 1. DNAPerceiver model to predict mRNA levels from the sequence upstream and downstream of the TSS; 2. ProteinPerceiver model to predict protein levels from the protein sequence; 3. Protein&DNAPerceiver model to predict protein levels from TSS and protein sequences. Results: The models are evaluated on cell lines, mice, glioblastoma, and lung cancer tissues. The results show the effectiveness of the Perceiver-type models in predicting mRNA and protein levels. Conclusions: This paper presents a Perceiver architecture for mRNA and protein level prediction. In the future, inserting regulatory and epigenetic information into the model could improve mRNA and protein level predictions. The source code is freely available at https://github.com/MatteoStefanini/DNAPerceiver [ABSTRACT FROM AUTHOR]

We have developed a Triangular Spatial Relationship (TSR)-based computational method for protein structure comparison and motif discovery that is both sequence and structure alignment-free. A protein 3D structure is modeled by all possible triangles that are constructed with every three C α atoms of amino acids as vertices. Every triangle is represented using an integer (a key). The keys are calculated by a rule-based formula which is a function of a representative length, a representative angle, and the vertex labels associated with amino acids. A 3D structure is thereby represented by a vector of integers (TSR keys). Global or local structure comparisons are achieved by computing all keys or a set of keys, respectively. Many enzymatic reactions and notable marketed drugs are highly stereospecific. Thus, in this paper, we propose a modified key calculation formula by including a mechanism for discriminating mirror-image keys to capture stereo geometry. We assign a positive or a negative sign to the integers representing mirror-image keys. Applying the new key calculation function provides the ability to further discriminate mirror-image keys that were previously considered identical. As the result, applying the mirror-image discrimination capability (i) significantly increases the number of distinct keys; (ii) decreases the number of common keys; (iii) decreases structural similarity; (iv) increases the opportunity to identify specific keys for each type of the receptors. The specific keys identified in this study for the cases of without (not applying) and with (applying) mirror-image discrimination can be considered as the structure signatures that exclusively belong to a certain type of receptors. Applying mirror-image discrimination introduces stereospecificity to keys for allowing more precise modeling of ligand - target interactions. The development of mirror-image TSR keys of C α atom, in conjunction with the integration of C α TSR keys with all-atom TSR keys for amino acids and drugs, will lead to a new and promising computational method for aiding drug design and discovery. [Display omitted] • Mirror-image discrimination capability is introduced into the Triangular Spatial Relationship (TSR)-based key generation formula to capture stereo geometry. • Applying mirror images decreases structural similarity which can be partially explained by the observation of a decrease in common keys. • For a hierarchically organized structure dataset, applying mirror images enhances the opportunity to discover specific keys that exclusively belong to certain types of proteins. • Different types of receptors share little sequence similarity and are functionally diverse. However, similar substructures are identified within each protein of a receptor family. • Unique substructures are identified for the majority of the receptor types, which build a foundation for designing new inhibitors specifically for certain types of receptors. • The TSR-based method has its advantage in studying protein sequence, structure and function relationships. [ABSTRACT FROM AUTHOR]

The discovery of protein tertiary structure is the basis of current genetic engineering, medicinal design, and other biological applications. Protein structural class plays a significant role in the tertiary structure folding and function analysis of protein. However, the growth rate of new amino acid sequence far exceeds the tertiary structure. Existing research methods of confirming protein folding cannot satisfy massive sequences and protein engineering. A high-accuracy prediction result of low-similarity protein dataset is particularly critical to generate the corresponding tertiary structure from the primary structure. In this paper, we construct a novel super-large-scale feature of the primary structure based on secondary structure, evolutionary information, chemical properties, and global descriptors. The diversified and massive features are utilized to predict the protein class based on a novel feature selection algorithm and a gradient boosting decision tree model. To testify the effectiveness and robustness of our proposed method, namely IDEGBM, we choose the 10-fold cross-validation for evaluating four benchmark datasets 25PDB, FC699, D1189 and D640. Experimental results exhibit that our method improves the accuracy in comparison with other state-of-the-art prediction models in terms of both accuracy and efficiency. Furthermore, a representative protein is used to validate that our proposed IDEGBM can be applied to improve the conformation prediction of protein tertiary structure. • A novel super-large-scale feature for protein structural class is conducted. • A novel feature selection and a gradient boosting decision tree model is proposed. • Extensive experimental results show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Bioactive peptide (BP) is short sequence of amino acids, which can play different biological activities depending on its structural characteristics and amino acid sequence. As health promoters, bioactive peptides (BPs) have attracted more and more attention in recent years. Separation and purification methods of BPs such as pressure-driven and electrically-driven membrane processes were reviewed in this paper. Especially, cell configuration and application of electrodialysis with filtration membrane (EDFM), a new emerging electro-driven membrane technology, in separating anionic and cationic BPs were introduced in detail. It is difficult to separate and purify BPs due to their excellent water solubility as well as uncertain composition and structure. Thus, further development in efficient and economical technologies for the separation and purification of BPs is urgent to improve human health. • Multistage UF process can be used to obtain BPs with high purity and activity. • EDFM is a prospective technology due to its good flexibility and selectivity. • EDFM has potential to large-scale separation of BPs in the future. [ABSTRACT FROM AUTHOR]

Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of pathogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors. [Display omitted] • Many computational studies have investigated protein-protein interactions (PPIs) until now. However, the majority of the previous approaches consider intra-species interactions and not the inter-species interactions between pathogen and host proteins. • The main contribution of this study is the novelty of the Graph Convolutional Network (GCN) based architecture to predict virus-human protein-protein interactions. • To the best of our knowledge, this is the first study that utilizes graph convolutional networks for PHI prediction. • Different viruses, including Sars-Cov-2, are utilized as holdout sets to evaluate the performance of the proposed method for the novel viruses. • Predicting interactions for emerging viruses can be challenging due to the lack of available graph structures utilized by GCNs. In this paper, to overcome this challenge, a preliminary phase is introduced to enrich the graph before the GCN training, which leads to a significant improvement. • The presented method is compared with the state-of-art methods on benchmark datasets to evaluate its performance. The performance result of the comparative test shows that the proposed method is better than its closest competitor by 1–21% in all the seven evaluation metrics. [ABSTRACT FROM AUTHOR]

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp. [Display omitted] [ABSTRACT FROM AUTHOR]

Distinguishing sequential patterns are sequential patterns that have much higher frequencies in one target group of sequences (concerning a given phenomenon of interest) than in a contrasting group of sequences. Distinguishing sequential patterns are useful for many machine learning tasks, as well as for the explanation and characterization of the phenomenon underlying the target group of sequences. However, previous studies on mining distinguishing sequential patterns did not consider the hierarchical relationship among elements in sequences. To fill the gap, this paper investigates the mining of distinguishing sequential patterns in the presence of concept hierarchies among sequence elements. The associated patterns will be called concept-hierarchy aware distinguishing sequential patterns (hDSPs). After presenting the challenges on mining hDSPs, we present hDSP-Miner, a method with effective pruning techniques, for mining hDSPs. Our empirical study using real-world protein sequences demonstrates that hDSP-Miner is effective and efficient, and it can discover more novel distinguishing sequential patterns than previous algorithms for mining distinguishing sequential patterns. • It is firstly proposed to consider concept hierarchies in DSP mining. • Designing an efficient algorithm called hDSP-Miner for discovering hDSPs. • A hybrid strategy combining breadth-first search and depth-first search. • Extensive experimental results show that hDSP-Miner can find interesting patterns. • hDSP-Miner effectively balances computational efficiency and memory cost. [ABSTRACT FROM AUTHOR]

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects. According to recent advancements, peptide-based therapies have attracted the attention of scientists because of their high selectivity. Peptide therapy can efficiently treat the targeted cells, without affecting the normal cells. Due to the rapid increase of peptide sequences, an accurate prediction model has become a challenging task. Keeping the significance of anticancer peptides (ACPs) in cancer treatment, an intelligent and reliable prediction model is highly indispensable. In this paper, a FastText-based word embedding strategy has been employed to represent each peptide sample via a skip-gram model. After extracting the peptide embedding descriptors, the deep neural network (DNN) model was applied to accurately discriminate the ACPs. The optimized parameters of DNN achieved an accuracy of 96.94 %, 93.41 %, and 94.02 % using training, alternate, and independent samples, respectively. It was observed that our proposed cACP-DeepGram model outperformed and reported ~10 % highest prediction accuracy than existing predictors. It is suggested that the cACP-DeepGram model will be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and the datasets are publicly available at https://github.com/shahidakbarcs/cACP-DeepGram. [ABSTRACT FROM AUTHOR]

A message such as mRNA, which consists of continuous characters without separators (such as commas or spaces), can easily be decoded incorrectly if it is read in the wrong reading frame. One construct to theoretically avoid these reading frame errors is the class of block codes. However, the first hypothesis of Watson and Crick (1953) that block codes are used as a tool to avoid reading frame errors in coding sequences already failed because the four periodical codons A A A , C C C , G G G and U U U seem to play an important role in protein coding sequences. Even the class of circular codes later discovered by Arquès and Michel (1996) in coding sequences cannot contain a periodic codon. However, by incorporating the interpretation of the message into the robustness of the reading frame, the extension of circular codes to include periodic codons is theoretically possible. In this work, we introduce the new class of I -circular codes. Unlike circular codes, these codes allow frame shifts, but only if the decoded interpretation of the message is identical to the intended interpretation. In the following, the formal definition of I -circular codes is introduced and the maximum and the maximal size of I -circular codes are given based on the standard genetic code table. These numbers are calculated using a new graph-theoretic approach derived from the classical one for the class of circular codes. Furthermore, we show that all 216 maximum self-complementary C 3 -codes (see Fimmel et al., 2015) can be extended to larger I -circular codes. We present the increased code coverage of the 216 newly constructed I -circular codes based on the human coding sequences in chromosome 1. In the last section of this paper, we use the polarity of amino acids as an interpretation table to construct I -circular codes. In an optimization process, two maximum I -circular codes of length 30 are found. [ABSTRACT FROM AUTHOR]

COVID-19 has threatened the whole world since December 2019 and has also infected millions of people around the globe. It has been transmitted through the SARS CoV-2 virus. Various proteins of the SARS CoV-2 virus have an important role in its interaction with human cells. Specifically, the interaction of S-protein with human ACE-2 protein helps in entering of SARS CoV-2 virus into a human cell. This interaction take-place at some specific amino-acid locations called as hot-spots. Understanding of this interaction is helpful for drug designing and vaccine development for new variants of COVID-19 disease. An attempt has been made in this paper for understanding this interaction by finding the characteristics frequency of SARS-related protein families using the resonance recognition model (RRM). Hardware implementation of Bandpass notch (BPN) lattice IIR filter system architecture is also carried out, which is used for hot-spots identification in SARS CoV-2 proteins. Various signal processing techniques like retiming, pipelining, etc. are explored for performance improvement. Synthesis of proposed BPN filter system has been done using Xilinx ISE EDA tool on Zynq-series (Zybo-board) FPGA family. It is found that retimed and pipelined architecture of hardware-implemented BPN lattice IIR filter-based hot-spots detection system improves the speed (computational time) by 14 to 31 times for different SARS CoV2 related proteins as compared to its MATLAB simulation with similar functionality. • Proposed BPN lattice IIR filter architecture for hot-spots identification in SARS CoV-2. • Hot-spot identification is useful for vaccine development of new variants of COVID-19. • Synthesis is done using Xilinx ISE and implemented on Zynq-series FPGA. • Experimentation is carried out on five proteins and identified hot-spots are reported. • Comparative analysis of proposed filter is done with MATLAB simulation. [ABSTRACT FROM AUTHOR]

Protein contact maps capture coevolutionary interactions between amino acid residue pairs that are spatially within certain proximity threshold. Predicted contact maps are used in many protein related problems that include drug design, protein design, protein function prediction, and protein structure prediction. Contact map prediction has achieved significant progress lately but still further challenges remain with prediction of contacts between residues that are separated in the amino acid residue sequence by large numbers of other residues. In this paper, with experimental results on 5 standard benchmark datasets that include membrane proteins, we show that contact map prediction could be significantly enhanced by using ensembles of various state-of-the-art short distance predictors and then by converting predicted distances into contact probabilities. Our program along with its data is available from https://gitlab.com/mahnewton/ecp. [Display omitted] • Enhanced contact maps from average contact probabilities obtained from inter-residue distances predicted by recent methods. • Protein 3D Structure Construction: More accurate 3D protein structures are constructed by using the predicted enhanced contact maps. • The program for the proposed ensemble-based protein contact map predictor is available from https://gitlab.com/mahnewton/ecp. [ABSTRACT FROM AUTHOR]

Profiles are used to model protein families and domains. They are built by multiple sequence alignments obtained by mapping a query sequence against a database to generate a profile based on the substitution scoring matrix. The profile applications are very dependent on the alignment algorithm and scoring system for amino acid substitution. However, sometimes there are no similar sequences in the database with the query sequence based on the scoring schema. In these cases, it is not possible to make a profile. This paper proposes a method named PA_SPP, based on pre-trained ProtAlbert transformer to predict the profile for a single protein sequence without alignment. The performance of transformers on natural languages is impressive. Protein sequences can be viewed as a language; we can benefit from these models. We analyze the attention heads in different layers of ProtAlbert to show that the transformer can capture five essential protein characteristics of a single sequence. This assessment shows that ProtAlbert considers some protein properties when suggesting amino acids for each position in the sequence. In other words, transformers can be considered an appropriate alternative for alignment and scoring schema to predict a profile. We evaluate PA_SPP on the Casp13 dataset, including 55 proteins. Meanwhile, one thermophilic and two mesophilic proteins are used as case studies. The results display high similarity between the predicted profiles and HSSP profiles. [Display omitted] • Introducing PA_SPP method for profile prediction using a single protein sequence without alignment. • Using pre-trained ProtAlbert transformer in PA_SPP method instead of alignment and scoring systems for profile prediction. • Assessing the attention heads of transformer to capture five essential protein characteristics from a single sequence. • According to five protein characteristic, ProtAlbert transformer suggests appropriate amino acids for each position. • The predicted profile by the transformer shows high quality compared to the HSSP profile. [ABSTRACT FROM AUTHOR]

COVID-19 disease causes serious respiratory illnesses. Therefore, accurate identification of the viral infection cycle plays a key role in designing appropriate vaccines. The risk of this disease depends on proteins that interact with human receptors. In this paper, we formulate a novel model for COVID-19 named "amino acid encoding based prediction" (AAPred). This model is accurate, classifies the various coronavirus types, and distinguishes SARS-CoV-2 from other coronaviruses. With the AAPred model, we reduce the number of features to enhance its performance by selecting the most important ones employing statistical criteria. The protein sequence of SARS-CoV-2 for understanding the viral infection cycle is analyzed. Six machine learning classifiers related to decision trees, k-nearest neighbors, random forest, support vector machine, bagging ensemble, and gradient boosting are used to evaluate the model in terms of accuracy, precision, sensitivity, and specificity. We implement the obtained results computationally and apply them to real data from the National Genomics Data Center. The experimental results report that the AAPred model reduces the features to seven of them. The average accuracy of the 10-fold cross-validation is 98.69%, precision is 98.72%, sensitivity is 96.81%, and specificity is 97.72%. The features are selected utilizing information gain and classified with random forest. The proposed model predicts the type of Coronavirus and reduces the number of extracted features. We identify that SARS-CoV-2 has similar physicochemical characteristics in some regions of SARS-CoV. Also, we report that SARS-CoV-2 has similar infection cycles and sequences in some regions of SARS CoV indicating the affectedness of vaccines on SARS-CoV-2. A comparison with deep learning shows similar results with our method. • We formulate a novel model for COVID-19 that classifies the coronavirus types and differentiates SARS-CoV-2 from other coronaviruses, reducing the number of features of the model to enhance its performance. • We use machine learning techniques for evaluating the model in terms of accuracy, precision, sensitivity, and specificity. • We analyze the protein sequence of SARS-CoV-2 for understanding the viral infection cycle and obtain results that are implemented computationally and compared to deep learning tools. • We implement the obtained results computationally and apply them to real-world data. [ABSTRACT FROM AUTHOR]

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git. [Display omitted] • PyProtModel is a new interface to handle different homology modeling tasks. • Different features of PyProtModel with other interfaces were discussed. • A workflow to all modules of PyProtModel was depicted for the users. [ABSTRACT FROM AUTHOR]

• ComProM -QSRR methodology has been developed for the prediction of retention time of peptides in liquid chromatography. • It identifies physicochemical properties at specific positions in the peptide sequences that modulate the retention time. • The prediction works well for all classes of peptides irrespective of size or sequence. • It is simple and straightforward approach to execute for retention time prediction. Peptide therapeutics plays a prominent role in medical practice. Both peptides and proteins have been used in several disease conditions like diabetes, cancer, bacterial infections etc. The optimization of a peptide library is a time consuming and expensive chore. The tools of computational chemistry offer a way to optimize the properties of peptides. Quantitative Structure Retention (Chromatographic) Relationships (QSRR) is a powerful tool which statistically derives relationships between chromatographic parameters and descriptors that characterize the molecular structure of analytes. In this paper, we show how Com parative Pro tein M odeling Q uantitative S tructure R etention R elationship (acronym ComProM -QSRR) can be used to predict the retention time of peptide sequences. This formalism is founded on our earlier published QSAR methodology Homo SAR. ComProM -QSRR can recognize and distinguish the contribution of amino acids at specific positions in the peptide sequences to the retention phenomena through their related physicochemical properties. This study firmly establishes the fact that this approach can be pragmatically used to predict the retention time to all classes of peptides regardless of size or sequence. [ABSTRACT FROM AUTHOR]

• Candidate endophytic fungus Diaporthe longicolla exhibits oxidase activity. • Oxidase activity is linked to secreted laccase enzyme capable of degrading BPA. • BPA-inducible laccase enzyme exhibits broad temperature and pH stability. • D. longicolla laccase enzyme efficiently degrades BPA and select BPA analogues. With recent initiatives to ban bisphenol A (BPA) in certain commercial products, manufacturers shifted to the production and use of BPA analogues. However, some of these BPA alternatives still possess endocrine disruptive activities. Many fungal enzymes are known to biodegrade phenolic compounds, such as BPA. However, the activity of these enzymes on BPA analogues remains unexplored. This study reports a secreted laccase from the endophytic fungus Diaporthe longicolla capable of degrading an impressive range of bisphenol analogues. The secreted crude enzymes are optimally active at pH 5 from 39 °C to 60 °C, efficiently degrading BPA as well as BPA analogues BPB, BPC, BPE and BPF. A purified form of laccase was identified from the crude fungal extract using FPLC and peptide sequencing. Furthermore, BPA induced the expression of this D. longicolla laccase gene. Overall, this paper demonstrated that the crude laccase enzyme from D. longicolla metabolizes BPA and select analogues, implicating the potential role of this fungus to remove environmental bisphenols. [ABSTRACT FROM AUTHOR]

The histidine-rich peptides of the LAH4 family were designed using cationic antimicrobial peptides such as magainin and PGLa as templates. The LAH4 amphipathic helical sequences exhibit a multitude of interesting biological properties such as antimicrobial activity, cell penetration of a large variety of cargo and lentiviral transduction enhancement. The parent peptide associates with lipid bilayers where it changes from an orientation along the membrane interface into a transmembrane configuration in a pH-dependent manner. Here we show that LAH4 adopts a transmembrane configuration in fully saturated DMPC membranes already at pH 3.5, i.e. much below the pK a of the histidines whereas the transition pH in POPC correlates closely with histidine neutralization. In contrast in POPG membranes the in-planar configuration is stabilized by about one pH unit. The differences in pH can be converted into energetic contributions for the in-plane to transmembrane transition equilibrium, where the shift in the transition pH due to lipid saturation corresponds to energies which are otherwise obtained by the exchange of several cationic with hydrophobic residues. A similar dependence on lipid saturation has also been observed when the PGLa and magainin antimicrobial peptides interact within lipid bilayers suggesting that the quantitative evaluation presented in this paper also applies to other membrane polypeptides. [Display omitted] • Lipid saturation strongly drives the interface-to-transmembrane realignment. • The energetic contribution of lipid saturation corresponds to removing numerous charges from the amino acid sequence. • Anionic lipids stabilize the interfacial localization of cationic amphipathic peptides. [ABSTRACT FROM AUTHOR]

Malonylation is a new protein post-translational modification and regulates a variety of cellular physiological processes. However, it is costly and time-consuming to identify malonylation sites through traditional experiments. Therefore, the prediction of malonylation sites by computational methods plays an important role in experimental design. In this paper, a new prediction model of malonylation sites, Malsite-Deep, is proposed. First, the seven feature extraction methods are used to extract feature information of protein sequences. Then, the under-sampling NearMiss-2 method is applied to handle imbalance data, and the update gate and reset gate of gated recurrent units (GRU) are used to select the optimal feature subset. Finally, the data from GRU layer is input into deep neural networks (DNN) to predict the malonylation sites, and the model performance is evaluated by 10-fold cross-validation and independent test sets. The 10-fold cross-validation shows that the AUC value on the training dataset reaches 0.99. The AUC values on the four independent test datasets all reach above 0.95. Results suggest that Malsite-Deep presented here facilitates the identification of protein malonylation sites. • A novel method (Malsite-Deep) is used to predict malonylation sites. • The BE, PWAA, BPB, EAAC, DC, EBGW and BLOSUM62 methods are fused to extract protein sequence features information. • NearMiss-2 algorithm is used to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the GRU and DNN to predict the malonylation sites. [ABSTRACT FROM AUTHOR]

• FLAT algorithm was improved based on an enhanced version of BAT algorithm. • Proposed model integrates BAT with PSO using a novel infection propagation mechanism. • Performance of developed model was evaluated on a huge lengths protein sequence. • FLAT based on BPINF was used to detect longest consecutive subsequences of COVID19. • Performance of FLAT was evaluated using real dataset and compared with other methods. The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta -heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Quantitative physicochemical perspective on life processes has been a great asset, in bioengineering and biotechnology. The quantitative physicochemical approach can be applied to practically all organisms, including viruses, if their chemical composition and thermodynamic properties are known. In this paper, a new method is suggested for determining elemental composition of viruses, based on atom counting. The atom counting method requires knowledge of genetic sequence, protein sequences and protein copy numbers. An algorithm was suggested for a program that finds elemental composition of various viruses (DNA or RNA, enveloped or non-enveloped). Except for the nucleic acid, capsid proteins, lipid bilayer and carbohydrates, this method includes membrane proteins, as well as spike proteins. The atom counting method has been compared with the existing molecular composition and geometric methods on 5 viruses of different morphology, as well as experimentally determined composition of the poliovirus. The atom counting method was found to be more accurate in most cases. The three methods were found to be complementary, since they require different kind of input information. Moreover, since the 3 methods rest on different assumptions, results of one model can be compared to those of the other two. [Display omitted] • A new atom counting method is introduced for determining virus elemental composition. • The new method is applicable to DNA and RNA, enveloped and non-enveloped viruses. • The method is in good agreement with experimental results. • A computer algorithm is proposed for implementation of the method. • The model uses widely available nucleic acid and protein sequence data. [ABSTRACT FROM AUTHOR]

The reason for low- or non-immunogenicity of fish collagens is still in doubt, which, to some extent, bottlenecks their production and clinical application as biomaterials. Employing bovine or porcine type I collagens (BCI or PCI) as controls in this paper, we intensively investigate the influence of tilapia type I collagens (TCI) on the function of dendritic cells (DCs) and T cells. From bio-informatic analyses, as well as data obtained in vitro and in vivo , we find the variations in amino acid sequences lead to only one calcium binding motif in the secondary structure of TCI, compared with three in BCI or PCI. So when TCI (together with the minor amount of Ca2+ they take) are uptaken, intracellular [Ca2+] remains stable and DCs maintain immature. On the contrary, those that have uptaken PCI or BCI experience not only increased [Ca2+] in the plasma but also phosphorylation of p65, resulting in activation of STIM1-Orai1/NF-кB signaling pathway and DC maturation. We fully prove our results on mice models, with no obvious cellular and humoral immune reactions. Our study primarily reveal the underlying mechanisms why TCI, different from BCI or PCI, show almost non-immunogenicity. Our findings are of great importance for the promotion and wide application of TCI in biomedicine. [Display omitted] • Tilapia type I collagens (TCI) do not induce DC maturation. • TCI do not enhance intracellular [Ca2+] by not activating STIM1-Orai1/NF-кB pathway. • Variations in amino acid sequences result in different numbers of calcium ligands. • TCI show lower immunogenicity than terrestrial mammals both in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learning have been shown to predict the proteins' functions. This paper proposes a hybrid deep neural network model to predict an unknown protein's functions from sequences. The proposed model is named Deep_CNN_LSTM_GO. Deep_CNN_LSTM_GO is an Integration between Convolutional Neural network (CNN) and Long Short-Term Memory (LSTM) Neural Network to learn features from amino acid sequences and outputs the three different Gene Ontology (GO). The gene ontology represents the protein functions in the three sub-ontologies: Molecular Functions (MF), Biological Process (BP), and Cellular Component (CC). The proposed model has been trained and tested using UniProt-SwissProt's dataset. Another test has been done using Computational Assessment of Function Annotation (CAFA) on the three sub-ontologies. The proposed model outperforms different methods proposed in the field with better performance using three different evaluation metrics (Fmax, Smin, and AUPR) in the three sub-ontologies (MF, BP, CC). [Display omitted] [ABSTRACT FROM AUTHOR]