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A novel xylanase gene xynBCA , encoding a polypeptide of 439 residues (XynBCA), was cloned from Caldicoprobacter algeriensis genome and recombinantly expressed in Escherichia coli BL21(DE3). The amino acid sequence analysis showed that XynBCA belongs to the glycoside hydrolase family 10. The purified recombinant enzyme has a monomeric structure of 52 kDa. It is active and stable in a wide range of pH from 3 to 10 with a maximum activity at 6.5. Interestingly, XynBCA was highly thermoactive with an optimum temperature of 80 °C, thermostable with a half-life of 20 min at 80 °C. The specific activity was 117 U mg−1, while the K m and V max were 1.247 mg ml−1, and 114.7 μmol min−1 mg−1, respectively. The investigation of XynBCA in kraft pulp biobleaching experiments showed effectiveness in releasing reducing sugars and chromophores, with best achievements at 100 U g−1 of pulp and 1 h of incubation. The comparative molecular modeling studies with the less thermostable Xylanase B from Clostridium stercorarium , revealed extra charged residues at the surface of XynBCA potentially participating in the formation of intermolecular hydrogen bonds with solvent molecules or generating salt bridges, therefore contributing to the higher thermal stability. [ABSTRACT FROM AUTHOR]

Allergic reactions to vespid stings are one of the major causes of IgE-mediated anaphylaxis. Vespa and Vespula venoms are closely related; Polistes venom is more distantly related and its allergens are less well studied. There is limited cross-reactivity between Polistes and the other vespid venoms because of differences in the epitopes on the allergen molecules.In this study, the major allergens of Polistes gallicus are isolated and characterized. P. gallicus venom contains four major allergens: phospholipase, antigen 5 (Ag5), hyaluronidase and protease that were characterized by mass spectrometry and specific binding to IgE. The complete amino acid sequence of Ag5 and the sequence of the N-terminal region of phospholipase were also determined. The alignment of Ag5 from P. gallicus (European species) and Polistes annularis (American species) shows an 85% identity that increases to 98% within the same subgenus. This could suggest the presence of specific epitopes on Ag5 molecule being the variations on the superficial loops. The features of the P. gallicus allergens could explain the partial cross-reactivity found between the American and European Polistes venoms, and suggest that the use of European Polistes venoms would improve the diagnostic specificity and the therapy of European patients and of North American patients sensitized by European Polistes. [Copyright &y& Elsevier]

• Novel and efficient lignocellulosic biomass (LCB) processing enzymes can be mined from cow rumen. • Recombinant Cel-5M is a novel multifunctional cellulase of GH5 family and belongs to subfamily 4. • Cel-5M efficiently converts pretreated LCB into bioethanol. • CBP and SSF in a single vessel is an economical approach to get high yield ethanol. We are reporting the novel cellulase named Cel-5M from rumen metagenome. The deduced amino acid sequence and biochemical characterization suggested that Cel-5M is endoglucanase from the GH5 family with multifunctional potential. Cel-5M showed similarity to non-characterized proteins and the genus Prevotella as parental organism. The 957 bp ORF encoding Cel-5M was cloned and overexpressed in E. coli and purified. The recombinant Cel-5M showed maximum activity at pH 6.0 and 40 °C. It retained more than 80% activity between 4 and 7 pH and 65% thermostability between 30 and 70 °C. Cel-5M showed activity on various substrates like CMC, Filter paper, Avicel, Xylan, β-Mannan, and Glucopyranoside, which confirmed its multifunctional characteristics and classifies as member of subfamily 4 of GH5 (GH5_4). LCB hydrolysis potential of Cel-5M was studied using wheat straw (10% w/v). Alkali-treated and steam-exploded wheat straws were inoculated with 1 mg/g Cel-5M, 2% yeast in a reaction mixture and SSF at 10% w/v loading rate. The ethanol yield 0.46 g/g and 0.43 g/g of cellulose obtained after 72 h fermentation in alkali-treated and steam-exploded wheat straw, respectively. Cel-5M is novel multifunction cellulase belongs GH-5 endoglucanase from rumen origin can be employed for bioethanol based biofuel production. [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]

[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]

• 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]

The structure and function of dietary proteins, as well as their subcellular prediction, are critical for designing and developing new drug compositions and understanding the pathophysiology of certain diseases. As a remedy, we provide a subcellular localization method based on feature fusion and clustering for dietary proteins. Additionally, an enhanced PseAAC (Pseudo-amino acid composition) method is suggested, which builds upon the conventional PseAAC. The study initially builds a novel model of representing the food protein sequence by integrating autocorrelation, chi density, and improved PseAAC to better convey information about the food protein sequence. After that, the dimensionality of the fused feature vectors is reduced by using principal component analysis. With prediction accuracies of 99.24% in the Gram-positive dataset and 95.33% in the Gram-negative dataset, respectively, the experimental findings demonstrate the practicability and efficacy of the proposed approach. This paper is basically exploring pseudo-amino acid composition of not any clinical aspect but exploring a pharmaceutical aspect for drug repositioning. • Accurately predicting dietary protein subcellular composition using machine learning methods remains a challenging scientific inquiry. • This study provides a feature-fusion and clustering-based subcellular localization method for dietary proteins. • The construction of food protein feature expression models involves the use of autocorrelation and chi-density approaches. • An enhanced PseAAC (Pseudo-amino Acid Composition) method is suggested, which builds upon the conventional PseAAC. • Prediction accuracies of 99.24% on the Gram-positive dataset and 95.33% on the Gram-negative dataset, respectively, is observed. [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]

Lectins are proteins that can bind specifically and reversibly to carbohydrates. This capacity gives lectins multiple biological roles and biotechnological applications. Although lectins can be found in all organisms, plant lectins, especially legume lectins, are undoubtedly the most thoroughly studied. Among legume lectins, the lectin from Canavalia ensiformis (ConA) and Canavalia brasiliensis (ConBr), both from Diocleinae subtribe, are two of the most well-known lectins. It has been 100 years since the first report of ConA and 40 years since the first report of ConBr, making 2019 an important year for lectinology. Structural data of these lectins in combination with biological activity tests clearly indicate that even a small shift in amino acid sequence can affect the tertiary and quaternary structures, consequently affecting the biological activity of these proteins. It is in this context that the present paper aims to review the structural data of ConA and ConBr, focusing on the primary structure, crystallography, tertiary and quaternary structures of these lectins, as well as their binding sites. This paper also expands the structural data by employing molecular dynamics to evaluate carbohydrate-binding properties and structural stability. It is anticipated that these data will increase knowledge about the structure-function relationships of these proteins. [ABSTRACT FROM AUTHOR]

Discovering and accurately locating drug targets is of great significance for the research and development of new drugs. As a different approach to traditional drug development, the machine learning algorithm is used to predict the drug target by mining the data. Because of its advantages of short time and low cost, it has received more and more attention in recent years. In this paper, we propose a novel method for predicting druggable proteins. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC), dipeptide composition (DPC) and reduced sequence (RS), getting the 591 dimension of drug target dataset. Then, the feature information of druggable proteins dataset is selected by genetic algorithm (GA). Finally, we use Bagging ensemble learning to improve SVM classifier to get the final prediction model. The predictive accuracy rate reaches 93.78% by using 5-fold cross-validation and compared with other state-of-the-art predictive methods. The results indicate that the method proposed in this paper has a high reference value for the prediction of potential drug targets, which will successfully play a key role in the drug research and development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/GA-Bagging-SVM. [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]

• 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]

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]

• 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]

• 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]

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]

Amino acid misincorporation during protein synthesis occurs naturally at a low level. Protein sequence errors, depending on the level and the nature of the misincorporation, can have various consequences. When site-directed mutagenesis is used as a tool for understanding the role of a side chain in enzyme catalysis, misincorporation in a variant with intrinsically low activity may lead to misinterpretations concerning the enzyme mechanism. We report here one more example of such a problem, dealing with flavocytochrome b 2 (Fcb2), a lactate dehydrogenase, member of a family of FMN-dependent L -2-hydroxy acid oxidizing enzymes. Two papers have described the properties of the Fcb2 catalytic base H373Q variant, each one using a different expression system with the same base change for the mutation. The two papers found similar apparent kinetic parameters. But the first one demonstrated the existence of a low level of histidine misincorporation, which led to an important correction of the variant residual activity (Gaume et al. (1995) Biochimie, 77, 621). The second paper did not investigate the possibility of a misincorporation (Tsai et al. (2007) Biochemistry, 46, 7844). The two papers had different mechanistic conclusions. We show here that in this case the misincorporation does not depend on the expression system. We bring the proof that Tsai et al. (2007) were led to an erroneous mechanistic conclusion for having missed the phenomenon as well as for having misinterpreted the crystal structure of the variant. This work is another illustration of the caution one should exercise when characterizing enzyme variants with low activity. [ABSTRACT FROM AUTHOR]

The prediction of protein structures directly from amino acid sequences is one of the biggest challenges in computational biology. It can be divided into several independent sub-problems in which protein secondary structure (SS) prediction is fundamental. Many computational methods have been proposed for SS prediction problem. Few of them can model well both the sequence-structure mapping relationship between input protein features and SS, and the interaction relationship among residues which are both important for SS prediction. In this paper, we proposed a deep recurrent encoder–decoder networks called Secondary Structure Recurrent Encoder–Decoder Networks (SSREDNs) to solve this SS prediction problem. Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain. A series of techniques are also used in this paper to refine the model’s performance. The proposed model is applied to the open dataset CullPDB and CB513. Experimental results demonstrate that our method can improve both Q3 and Q8 accuracy compared with some public available methods. For Q8 prediction problem, it achieves 68.20% and 73.1% accuracy on CB513 and CullPDB dataset in fewer epochs better than the previous state-of-art method. [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]

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules. [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]

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]

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]

In this paper, a new method for remote protein homology detection is presented. Most discriminative methods concatenate the values extracted from physicochemical properties to build a model that separates homolog and non-homolog examples. Each discriminative method uses a specific strategy to represent the information extracted from the protein sequence and a different number of indices. After the vector representation is achieved, support vector machines (SVM) are usually used. Most classificat i on techniques are not suitable in remote homology detection because they do not address high dimensional datasets. In this paper, we propose a method that reduces the high dimensionality of the vector representation using models that are defined at the 3D level. Next, the models are mapped from the protein primary sequence. The new method, called remote-C3D, is presented and tested on the SCOP 1.53 and SCOP 1.55 datasets. The remote-C3D method achieves a higher accuracy than the compositionbased methods and a comparable performance with profile-based methods. [ABSTRACT FROM AUTHOR]

The emergence of antibiotic-resistant microbes has stimulated research worldwide seeking new biologically active molecules. In this respect, synthetic antimicrobial peptides (SAMPs) have been suggested to overcome this problem. Although there are some online servers that provide putative SAMPs from protein sequences, the choice of the best peptide sequences for further analysis is still difficult. Therefore, the goal of this paper is not to launch a new tool but to provide a friendly workflow to characterize and predict potential SAMPs by employing existing tools. Using this proposed workflow, two peptides (PepGAT and PepKAA) were obtained and extensively characterized. These peptides damaged microbial membranes and cell walls, and induced overproduction of reactive oxygen species (ROS). Both peptides were found to assume random coil secondary structure in aqueous solution, organic solvent, and upon binding to negatively charged lipid systems. Peptides were also able to degrade formed biofilms but not to prevent biofilm formation. PepGAT was not resistant to proteolysis, whereas PepKAA was resistant to pepsin but not to pancreatin. Furthermore, both presented no hemolytic activity against red blood cells, even at a 10-fold higher concentration than the antimicrobial concentration. The pipeline proposed here is an easy way to design new SAMPs for application as alternatives to develop new drugs against human pathogenic microorganisms. • Here is provided a workflow to facilitate the design of synthetic peptides. • Employing the workflow was possible to design two antimicrobial peptides. • PepGAT and PepKAA are potent antimicrobial peptides. • Both peptides have activity against human pathogenic bacteria and fungi. [ABSTRACT FROM AUTHOR]

Wet Lab methods used to resolve the native structure of a given protein such as X-ray Diffraction and NMR spectroscopy are time-consuming, expensive, and could be done multiple times due to failure. Those pitfalls led to the development of computationally automated prediction methods. Predicting the structure of a protein using its peptide sequence only — also known as ab initio Protein Structure Prediction "PSP"— is computationally challenging because of the large conformational space to be searched and the complexity of energy functions. Some successful predictive methods have been developed to solve PSP problem. In this paper, by using a set of peptide sequences, we compare a collection of PSP ab initio-based methods. Experiments show that using a metaheuristic-based search method that utilizes genetic algorithm can achieve same or better results than time consuming methods. [ABSTRACT FROM AUTHOR]

The protein sequence-structure gap results from the contrast between rapid, low-cost deep sequencing, and slow, expensive experimental structure determination techniques. Comparative homology modelling may have the potential to close this gap by predicting protein structure in target sequences using existing experimentally solved structures as templates. This paper presents the first use of force-directed graphs for the visualization of sequence space in two dimensions, and applies them to the choice of suitable RNA-dependent RNA polymerase (RdRP) target-template pairs within human-infective RNA virus genera. Measures of centrality in protein sequence space for each genus were also derived and used to identify centroid nearest-neighbour sequences (CNNs) potentially useful for production of homology models most representative of their genera. Homology modelling was then carried out for target-template pairs in different species, different genera and different families, and model quality assessed using several metrics. Reconstructed ancestral RdRP sequences for individual genera were also used as templates for the production of ancestral RdRP homology models. High quality ancestral RdRP models were consistently produced, as were good quality models for target-template pairs in the same genus. Homology modelling between genera in the same family produced mixed results and inter-family modelling was unreliable. We present a protocol for the production of optimal RdRP homology models for use in further experiments, e.g. docking to discover novel anti-viral compounds. (219 words) Image 1 • The first use of force-directed graphs for the visualization of multidimensional protein sequence space in two dimensions. • Measures of centrality in protein sequence space to identify sequences for production of homology models. • Homology modelling for RNA-dependent RNA polymerase (RdRP) target-template pairs in different species, genera and families. • A protocol for the production of optimal RdRP homology models for use in further experiments. [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]

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]

In the present paper, our main goal is focused in developing fast algorithms for human mtDNA sequence analyses, requiring minimum and explicit assumptions on mutation models and evolutionary pathways. We propose a new approach based on a construction of Dawson, a technique based on the ordering of the variable sites. In this approach, the first step corresponds to the computation of the order of the positions according to their capacity to separate the sequences into dichotomous groups. Aiming to avoid or at least to minimize the consideration of ambiguous evolutionary events such as insertions/deletions and recurrence, which cause well-known alignment problems, in the present study we only work with the protein coding sequence, the clearly more stable region in human mitochondrial genomes. This method was tested in a small set of 99 human mtDNA comprising representatives of all major haplogroups. The developed approach showed to be a choice to automate the clustering of human mtDNA sequences into broad groups, the output being in agreement with the canonical classification into macro-haplogroups deposited in the Phylotree database. [ABSTRACT FROM AUTHOR]

Abstract The study of disulfide linkage is a crucial part of the quality assessment of biopharmaceutical products because disulfide bonds stabilize the tertiary structure of proteins and maintain protein functions. Therefore, a suitable method is highly required for disulfide linkage assignment when nested disulfide bonds formed with closely spaced cysteine residues. A novel approach for disulfide linkage assignment of disulfide-rich peptides and proteins via electrochemical reduction on a lead electrode with mass spectrometry is presented in this paper. The method features partial electrochemical reduction and alkylation of peptides followed by alkylated peptide sequencing based on tandem mass spectrometry. Lead was chosen for the first time as an electrode material for disulfide bond reduction, because it has the advantages of maintenance free (only infrequent polishing needed), easy operation in DC mode, and reusability. Without any special sample preparation and any chemical reduction agents, disulfide bond in peptides can be cleaved rapidly. The new method was successfully tested with two peptides and one protein containing nested disulfide bonds. Graphical abstract fx1 Highlights • Lead was chosen for the first time as an electrode material for disulfide bond reduction. • The lead electrode is stable and easy operation in DC mode. • Disulfide linkage assignment of disulfide-rich peptides was performed by EC-MS. • The method had the advantages of high efficiency and high speed. • Special sample preparation and chemical reduction agents are not needed. [ABSTRACT FROM AUTHOR]

Ovalbumin peptides are food protein-derived fragments comprised of amino acid residues. The bioactive properties of the peptides are determined by the inherent amino acid composition and sequence of the peptide. The objective of this study was to evaluate the proteomics characteristics in Tibetan chicken egg ovalbumin peptides, compared to ordinary eggs, using isobaric tags for the relative and absolute quantification labelling technique (iTRAQ). The results showed that 165 proteins were identified and 56 proteins were significantly changed including 34 significantly upregulated and 22 significantly downregulated proteins. The more abundant proteins of the Tibetan ovalbumin peptides included vitellogenin-2, vitellogenin-1, vitellogenin, apolipoprotein A-I, and serum albumin, which were upregulated. A Gene Ontology (GO) analysis showed that these proteins mostly participated in biological processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that they may be associated with the peroxisome proliferator-activated receptor (PPAR) pathway, which is related to lipid metabolism. The Protein-Protein Interaction (PPI) analysis showed that these proteins significantly interacted. • The Tibetan egg ovalbumin peptide identification by proteome analysis was not reported before. • The difference of ovalbumin peptides of Tibetan egg and the ordinory in protein expression patterns and the difference of biochemical function on proteomic level are reported in this paper. • This study will help demonstrate molecular-level protein differences in ovalbumin peptides and may help reveal ovalbumin special effect on protein function of Tibetan egg. [ABSTRACT FROM AUTHOR]

• We take six physicochemical properties of amino acids into account. • A similarity matrix based on standardized Euclidean distance is obtained. • The discriminant and phylogenetic analysis are made. • The results of experiments show our measure efficiency. Sequence comparison is an important topic in bioinformatics. With the exponential increase of biological sequences, the traditional protein sequence comparison methods — the alignment methods become limited, so the alignment-free methods are widely proposed in the past two decades. In this paper, we considered not only the six typical physicochemical properties of amino acids, but also their frequency and positional distribution. A 51-dimensional vector was obtained to describe the protein sequence. We got a pairwise distance matrix by computing the standardized Euclidean distance, and discriminant analysis and phylogenetic analysis can be made. The results on the Influenza A virus and ND5 datasets indicate that our method is accurate and efficient for classifying proteins and inferring the phylogeny of species. [ABSTRACT FROM AUTHOR]

Thermobifida alba AHK119 exhibits sufficient filter paper-degradation activity in its culture supernatant. AHK119-bMs (1365 bp) and AHK119-E5 (1425 bp), which encode novel GH5 family endoglucanases, were cloned from the genomic DNA of T. alba AHK119. AHK119-bMs and AHK119-E5 consisted of 454 and 474 amino acid residues, respectively, in which the catalytic domain (CD) and carbohydrate-binding module (CBM) were connected by an accessary module (linker region). The amino acid sequences of CD and CBM of AHK119-bMs were most identical to those of endo-β-mannanases (Man5As) from T hermobifida fusca TM51, T. halotolerans YIM90462, and T. cellulosilytica TB100. In contrast, the amino acid sequences of CD and CBM of AHK119-E5 were most identical to those of endo-1,4-β-glucanases (cellulases; Cel5As) from T. fusca and T. halotolerans YIM90462. However, the linker region of both the genes shared low identities with those of Man5As and Cel5As. AHK119-bMs showed broader specificities toward cellulosic substrates than Man5As, whereas AHK119-E5 showed higher activity toward insoluble cellulosic substrates than toward soluble ones, which was conflicting when compared with other Cel5As. In addition, AHK119-bMs and AHK119-E5 showed different requirements for metal ions from those of Man5As and Cel5As, respectively. Therefore, both the enzymes were identified as novel GH5 endoglucanases, and the accessary modules seemed to play important roles in their enzymatic properties. [ABSTRACT FROM AUTHOR]

Abstract Frequent variations in influenza vaccines are necessary to match antigenic variants which appear in influenza epidemics. Antigenic variants of influenza viruses result from frequent mutations in amino acid residues located on their hemagglutinin (HA) proteins. Knowledge of specific changes in these amino acids helps to characterize distinct antigenic variants. In this paper, statistical models are developed and used to investigate changes in amino acids which accompany antigenic variants of epidemiological importance. Amino acid sequences of the HA proteins of influenza A (H3N2) strains isolated from 1968 to 2015 were obtained. The sequences were aligned using Clustal Omega and the number of differences in amino acid residues located on annotated positions of antigenic sites of the HA protein between pairs of strains were determined. These were linked in the statistical models and used to assess the relationship between any pair of influenza strains. The results revealed that both antigenic similarity between strains and the amino acid changes are affected by the time of isolation of the strains. Furthermore, the models predicted that rates of changes in amino acids located on the antigenic sites ranged between 5% and 6% per site per year. The findings of the study suggest that time-dependent antigenic variants of influenza A (H3N2) strains may occur as they evolve. The study has the potential to greatly improve influenza surveillance in as much as it supports vaccine designs. Highlights • Antigenic variants of influenza virus result from changes in relevant amino acids. • Proposed models link changes in amino acids to the years of isolation of strains. • Rates of amino acid changes per antigenic site per year ranged between 5% and 6%. • Antigenic relatedness between two strains is affected by the years of isolation. [ABSTRACT FROM AUTHOR]

Highlights • This article proposes a new method for Prediction of Cleavage of Amino Acid Sequences by HIV-1. • The proposed method relies on a new encoding and model for prediction of amino acid sequence cleavage by HIV protease. • In the encoding stage, a combination of amino acids' spatial and structural features are taken into account. • The Final prediction model is developed with the combination of genetic programming and support vector machine. Abstract The human immunodeficiency virus (HIV) is the cause of acquired immunodeficiency syndrome (AIDS), which has profound implications in terms of both economic burden and loss of life. Modeling and examination of the HIV protease cleavage of amino acid sequences can contribute to control of this disease and production of more effective drugs. The present paper introduces a new method for encoding and characterization of amino acid sequences and a new model for the prediction of amino acid sequence cleavage by HIV protease. The proposed encoding scheme utilizes a combination of amino acids' spatial and structural features in conjunction with 20 amino acid sequences to make sure that their physicochemical and sequencing features are all taken into account. The proposed HIV-1 amino acid cleavage prediction model is developed with the combination of genetic programming and support vector machine. The results of evaluations performed on various datasets demonstrate the superior performance of the proposed encoding and better accuracy of the proposed HIV-1 cleavage prediction model as compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: Protein structural motif detection has important applications in structural genomics. Compared with sequence motifs, structural motifs are more sensitive in revealing the evolutionary relationships among proteins. A variety of algorithms have been proposed to attack this problem. However, they are either heuristic without theoretical performance guarantee, or inefficient due to employing exhaustive search strategies. This paper studies a reasonably restricted version of this problem: the compact structural motif problem. We prove that this restricted version is still NP-hard, and we present a polynomial-time approximation scheme to solve it. This is the first approximation algorithm with a guaranteed ratio for the protein structural motif problem. 1 [1] A preliminary version of this paper appeared in CPM’2007. [Copyright &y& Elsevier]

Abstract: The activation of plasminogen plays a crucial role in a variety of extracellular proteolytic events such as, fibrinolysis, cell migration, ovulation, involution of the mammary gland and the activation of other protease classes and growth factors. In this paper we describe the isolation of the full-length cDNAs of ovine urokinase plasminogen activator (u-PA) and its receptor (u-PAR) using a polymerase chain reaction based strategy. The ovine u-PA cDNA comprised of 2350 bp and it is characterized by a coding region of 1302 bp, and 5′- and 3′-UTR regions of 129 and 919 bp, respectively. The deduced amino acid sequence consists of 433 amino acids. The ovine u-PAR cDNA is comprised of 1247 bp and it is characterized by a coding region of 957 bp and 5′- and 3′-UTR regions of 44 and 246 bp respectively. The deduced amino acid sequence consists of 318 amino acids. Three-dimensional models of the putative protein products of both cDNAs showed that the proteins bear a high similarity with their human counterparts. Real-time PCR revealed high levels of u-PA expression in the adipose tissue, followed by that in mammary gland and kidney. Lower levels of expression were detected in the adrenal glands, heart, ovaries, spleen, liver and cerebellum. A similar pattern was observed in u-PAR expression with noticeably lower levels of expression in heart, liver and cerebellum. To the best of our knowledge, this is the first paper reporting expression of u-PA and u-PAR in the adipose tissue. These data strengthen the suggestion that adipose tissue functions as an endocrine organ besides an energy storage organ. Furthermore, u-PA and u-PAR mRNA levels were 7 and 8.5 fold higher respectively in involuting mammary tissue obtained from non-lactating ewes compared to that detected in mammary tissue obtained from lactating ewes. These data are consistent with the notion that upregulation of u-PA and u-PAR expression may play a key role in the process of involution of the mammary gland. [Copyright &y& Elsevier]

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]

Abstract: In this paper we investigate the inverse protein folding (IPF) problem under the Canonical model on 3D and 2D lattices [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136; E.I. Shakhnovich, A.M. Gutin, Engineering of stable and fast-folding sequences of model proteins, Proc. Natl. Acad. Sci. 90 (1993) 7195–7199]. In this problem, we are given a contact graph of a protein sequence that is embeddable in a 3D (respectively, 2D) lattice and an integer . The goal is to find an induced subgraph of G of at most K vertices with the maximum number of edges. In this paper, we prove the following results: [•] An earlier proof of NP-completeness of the IPF problem on 3D lattices [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136] is based on the NP-completeness of the IPF problem on the 2D lattices. However, the reduction was not correct and we show that the IPF problem for 2D lattices can be solved in time. But, we show that the IPF problem on 3D lattices is indeed NP-complete by a providing a different reduction from a different NP-complete problem. [•] We design a polynomial-time approximation scheme for the IPF problem on 3D lattices using the shifted slice-and-dice approach in [P. Berman, B. DasGupta, S. Muthukrishnan, Approximation algorithms for MAX-MIN tiling, J. Algorithms 47(2) (2003) 122–134; D. Hochbaum, Approximation Algorithms for NP-hard Problems, PWS Publishing Company, MA, 1997; D.S. Hochbaum, W. Mass, Approximation schemes for covering and packing problems in image processing and VLSI, J. ACM 32(1) (1985) 130–136], thereby improving the previous best polynomial-time approximation algorithm which had a performance ratio of [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136]. [Copyright &y& Elsevier]