Search

A single amino acid in a peptide sequence can play an important role to tune the self‐assembly and hydrogelation behaviour. Here, a C‐terminal cysteine‐containing ultrashort peptide hydrogelator forms hydrogel through non‐covalent and covalent interactions. Interestingly, the hydrogel is insoluble in water and buffer solutions at different pH values (1‐13) and is thixotropic and injectable. In recent years, removing dyes from contaminated water has become a significant concern because of the shortage of freshwater resources. Therefore, the adsorption of dyes through a reliable, straightforward, nontoxic, cheap, and environmentally friendly adsorbent has become a popular topic. Hence, the hydrogelator was exploited to remove organic dyes from wastewater, harnessing its applicability in the gel phase and solid supports (filter paper and cotton). [ABSTRACT FROM AUTHOR]

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]

Thermoalkaliphilic GH11 xylanases are largely favored for paper pulp biobleaching process. The present work aimed to comparatively investigate the molecular phylogeny, amino acid sequences, molecular structure, and enzyme–substrate interaction of six thermoalkaliphilic GH11 xylanases from different bacterial species (Oxalobacteraceae bacterium xylanase = ObXyl, Sphingomonas sp. xylanase = SsXyl, Hymenobacter sp. xylanase = HsXyl, Amycolatopsis vastitatis xylanase = AvXyl, Lentzea deserti xylanase = LdXyl, Streptomyces rubellomurinus xylanase = SrXyl). For this purpose, six bacterial thermoalkaliphilic GH11 xylanase sequences derived from unreviewed protein entries of UniProt/TrEMBL database were analyzed for their phylogenetic relationships and sequence similarities. Also, 3D predicted structures of the enzymes were built and computationally validated by different bioinformatics tools. The enzyme–substrate interactions were investigated by molecular docking analysis using various substrates. Phylogenetic analysis showed that six enzymes were grouped into two different clusters: the first cluster included ObXyl, SsXyl, and HsXyl, whereas the second cluster had AvXyl, LdXyl, and SrXyl. Multiple sequence alignment showed that the second cluster xylanases possessed longer N-terminal regions indicating higher thermostability, compared to the first cluster xylanases. The structural analyses showed that six predicted structures were largely conserved. Molecular docking results indicated that binding efficiency to xylotriose, xylotetraose, and xylopentaose was higher in second cluster enzymes than that in first cluster enzymes, exhibiting mostly above -8.0 kCal/mol of binding energy. Arginine in B8 β-strand was commonly involved in substrate interactions in all the second cluster xylanases, different from the first cluster ones. Thus, the present work predicted that the thermoalkaliphilic xylanases in the second cluster might be greater potential candidates for the paper pulp bleaching process. [ABSTRACT FROM AUTHOR]

Lysine crotonylation is a newly discovered and reversible posttranslational modification involved in various biological processes, especially metabolism regulation. A total of 5159 lysine crotonylation sites in 2272 protein groups were identified. Twenty-seven motifs were found to be the preferred amino acid sequences for crotonylation sites. Functional annotation analyses revealed that most crotonylated proteins play important roles in metabolic processes and photosynthesis. Bioinformatics analysis suggested that lysine crotonylation preferentially targets a variety of important biological processes, including ribosome, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, proteasome and the TCA cycle, indicating lysine crotonylation is involved in the common mechanism of metabolic regulation. A protein interaction network analysis revealed that diverse interactions are modulated by protein crotonylation. These results suggest that lysine crotonylation is involved in a variety of biological processes. HSP70 is a crucial protein involved in protecting plant cells and tissues from thermal or abiotic stress responses, and HSP70 protein was found to be crotonylated in paper mulberry. This systematic analysis provides the first comprehensive analysis of lysine crotonylation in paper mulberry and provides important resources for further study on the regulatory mechanism and function of the lysine crotonylated proteome. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Protein ORIGAMI ( http://ibg.kit.edu/protein_origami) is a browser‐based web application that allows the user to create straightforward 3D paper models of folded peptides for research, teaching and presentations. An amino acid sequence can be turned into α‐helices, β‐strands and random coils that can be printed out and folded into properly scaled models, with a color code denoting the biophysical characteristics of each amino acid residue (hydrophobicity, charge, etc.). These models provide an intuitive visual and tactile understanding of peptide interactions with other partners, such as helix‐helix assembly, oligomerization, membrane binding, or pore formation. Helices can also be displayed as a helical wheel or helical mesh in 2D graphics, to be used in publications or presentations. The highly versatile programme Protein ORIGAMI is also suited to create less conventional helices with arbitrary pitch (e.g., 310‐helix, π‐helix, or left‐handed helices). Noncanonical amino acids, labels and different terminal modifications can be defined and displayed at will, and different protonation states can be shown. In addition to the web application, the program source code can be downloaded and installed locally on a PC. The printed paper models can be readily used for daily research and discussions, just as for educational purposes and teaching. © 2018 by The International Union of Biochemistry and Molecular Biology, 46:403–409, 2018. [ABSTRACT FROM AUTHOR]

Background: Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results: We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions: MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context. [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]

Background: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. Results: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called "cut") which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fβ for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. Conclusions: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. [ABSTRACT FROM AUTHOR]

Describes the circular DNA cloning and nucleotide sequences encoding of alpha-skeletal actin isoforms from walleye pollack. Amino acid sequence; Plaque hybridization; Reverse transcription-polymerase chain reaction; Use of terminal deoxynucleotidyl transferase.

Purpose: Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. Results: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction. [ABSTRACT FROM AUTHOR]

Protein structure prediction (PSP) is a grand challenge in bioinformatics, drug discovery, and related fields. PSP is computationally challenging because of an astronomically large conformational space to be searched and an unknown very complex energy function to be minimised. To obtain a given protein's structure, template-based PSP approaches adopt a similar protein's known structure, while template-free PSP approaches work when no similar protein's structure is known. Currently, proteins with known structures are greatly outnumbered by proteins with unknown structures. Template-free PSP has obtained significant progress recently via machine learning and search-based optimisation approaches. However, very accurate structures for complex proteins are yet to be achieved at a level suitable for effective drug design. Moreover, ab initio prediction of a protein's structure only from its amino acid sequence remains unsolved. Furthermore, the number of protein sequences with unknown structures is growing rapidly. Hence, to make further progress in PSP, more sophisticated and advanced artificial intelligence (AI) approaches are needed. However, getting involved in PSP research is difficult for AI researchers because of the lack of a comprehensive understanding of the whole problem, along with the background and the literature of all related sub-problems. Unfortunately, existing PSP review papers cover PSP research at a very high level and only some parts of PSP and only from a particular singular viewpoint. Using a systematic approach, this review paper provides a comprehensive survey of the state-of-the-art template-free PSP research to fill this knowledge gap. Moreover, covering required PSP preliminaries and computational formulations, this paper presents PSP research from AI perspectives, discusses the challenges, provides our commentaries, and outlines future research directions. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model's inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently. [ABSTRACT FROM AUTHOR]

The average content of casein in yak milk is 40.2 g/L. Casein can be degraded by enzymatic digestion or food processing to produce abundant degradation peptides. International researchers have studied the degradation peptides of yak milk casein by using multiple techniques and methods, such as in vitro activity tests, cellular experiments, proteomics, bioinformatics, etc., and found that the degradation peptides have a wide range of functional activities that are beneficial to the human body, such as angiotensin-converting enzyme (ACE) inhibitory, antioxidant, anti-inflammatory, antidiabetic, antimicrobial, anticancer, and immunomodulatory activities, etc., and it has been proved that the types and strengths of functional activities are closely related to the structural characteristics of the peptides. This paper describes the characteristics of yak milk proteins, the functional activities, and mechanism of action of degraded peptides. Based on the types of functional activities of yak milk casein degradation peptides, we classified and elucidated the effects of structural factors, such as peptide molecular weight, peptide length, amino acid sequence, physicochemical properties, electrical charge, hydrophobicity, spatial conformation, chain length, and the type of enzyme on these activities. It reveals the great potential of yak milk casein degradation peptides as functional active peptide resources and as auxiliary treatments for diseases. It also provides important insights for analyzing yak casein degradation peptide activity and exploring high-value utilization. [ABSTRACT FROM AUTHOR]

Most of the current nonclassical proteins prediction methods involve manual feature selection, such as constructing features of samples based on the physicochemical properties of proteins and position‐specific scoring matrix (PSSM). However, these tasks require researchers to perform some tedious search work to obtain the physicochemical properties of proteins. This paper proposes an end‐to‐end nonclassical secreted protein prediction model based on deep learning, named DeepNCSPP, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long‐ and short‐term memory and convolutional neural networks, respectively. Among the experiments conducted on the independent test dataset, DeepNCSPP achieved excellent results with an accuracy of 88.24%, Matthews coefficient (MCC) of 77.01%, and F1‐score of 87.50%. Independent test dataset testing and 10‐fold cross‐validation show that DeepNCSPP achieves competitive performance with state‐of‐the‐art methods and can be used as a reliable nonclassical secreted protein prediction model. A web server has been constructed for the convenience of researchers. The web link is https://www.deepncspp.top/. The source code of DeepNCSPP has been hosted on GitHub and is available online (https://github.com/xiaoliu166370/DEEPNCSPP). This paper proposes an end‐to‐end nonclassical secreted protein prediction model DeepNCSPP based on deep learning, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long‐ and short‐term memory and convolutional neural networks, respectively. [ABSTRACT FROM AUTHOR]

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function. [ABSTRACT FROM AUTHOR]

Summary Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementation The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. [ABSTRACT FROM AUTHOR]

This paper presents the first in-depth research on the biological and genomic properties of lytic rhizobiophage AP-J-162 isolated from the soils of the mountainous region of Dagestan (North Caucasus), which belongs to the centers of origin of cultivated plants, according to Vavilov N.I. The rhizobiophage host strains are nitrogen-fixing bacteria of the genus Sinorhizobium spp., symbionts of leguminous forage grasses. The phage particles have a myovirus virion structure. The genome of rhizobiophage AP-J-162 is double-stranded DNA of 471.5 kb in length; 711 ORFs are annotated and 41 types of tRNAs are detected. The closest phylogenetic relative of phage AP-J-162 is Agrobacterium phage Atu-ph07, but no rhizobiophages are known. The replicative machinery, capsid, and baseplate proteins of phage AP-J-162 are structurally similar to those of Escherichia phage T4, but there is no similarity between their tail protein subunits. Amino acid sequence analysis shows that 339 of the ORFs encode hypothetical or functionally relevant products, while the remaining 304 ORFs are unique. Additionally, 153 ORFs are similar to those of Atu_ph07, with one-third of the ORFs encoding different enzymes. The biological properties and genomic characteristics of phage AP-J-162 distinguish it as a unique model for exploring phage–microbe interactions with nitrogen-fixing symbiotic microorganisms. [ABSTRACT FROM AUTHOR]

Background: Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. Results: In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. Conclusion: GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite. [ABSTRACT FROM AUTHOR]

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future. Author summary: Proteomic analyses frequently rely on mass spectrometry, a method characterized by its limited dynamic range, potentially overlooking low-abundant proteins. To address this limitation, single-molecule protein sequencing methods offer a solution. Fluorosequencing is a cutting-edge single-molecule protein sequencing method, which can distinguish peptides or protein molecules massively parallelly. This method has attracted interest from investors, as evidenced by the recent funding of Erisyon, a company developing this technology. This technique contains a challenging classification task: determining the original peptide sequence from light-intensity observations obtained after several Edman cycles. A classifier based on a combination of k Nearest Neighbors (kNN) with Hidden Markov Models (HMM) had been shown to have close-to-optimal accuracy with tractable complexity. We propose in this paper a new algorithm that reduces computation time significantly at the expense of a slight reduction in accuracy compared to state-of-the-art method. [ABSTRACT FROM AUTHOR]

LncRNA–protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA–protein interaction prediction. However, the experimental techniques to detect lncRNA–protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA–protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features. [ABSTRACT FROM AUTHOR]

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design. [ABSTRACT FROM AUTHOR]

Background: The initial version of SEDA assists life science researchers without programming skills with the preparation of DNA and protein sequence FASTA files for multiple bioinformatics applications. However, the initial version of SEDA lacks a command-line interface for more advanced users and does not allow the creation of automated analysis pipelines. Results: The present paper discusses the updates of the new SEDA release, including the addition of a complete command-line interface, new functionalities like gene annotation, a framework for automated pipelines, and improved integration in Linux environments. Conclusion: SEDA is an open-source Java application and can be installed using the different distributions available (https://www.sing-group.org/seda/download.html) as well as through a Docker image (https://hub.docker.com/r/pegi3s/seda). It is released under a GPL-3.0 license, and its source code is publicly accessible on GitHub (https://github.com/sing-group/seda). The software version at the time of submission is archived at Zenodo (version v1.6.0, http://doi.org/10.5281/zenodo.10201605). [ABSTRACT FROM AUTHOR]

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties, putting them on a new path in medicine. Their conformational rigidity improves proteolytic stability and leads to rapid penetration into tissues via any possible route of administration. Moreover, elimination of renal metabolism is of great importance, for example, for people with a history of liver diseases. In addition, each ring can function independently, making bicyclic peptides extremely versatile molecules for further optimization. In this paper, we compared the potentiometric and spectroscopic properties studied by UV–vis, MCD, and EPR of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH) (BCL). In particular, we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL1 (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position, i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position, i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively; and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2). Potentiometric results have shown that the bicyclic structure promotes the involvement of the side chain imidazole donors in Cu(II) binding. On the other hand, the results obtained for the mono-cyclic analogues lead to the conclusion that the coordination of the histidine moiety as an anchoring group is promoted by its location in the peptide sequence further from the nonbinding cycle, strongly influencing the involvement of the amide donors in Cu(II) coordination. [ABSTRACT FROM AUTHOR]

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution Statement This study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions. [ABSTRACT FROM AUTHOR]

RAMOSA1 (RA1) is a Cys2-His2-type (C2H2) zinc finger transcription factor that controls plant meristem fate and identity and has played an important role in maize domestication. Despite its importance, the origin of RA1 is unknown, and the evolution in plants is only partially understood. In this paper, we present a well-resolved phylogeny based on 73 amino acid sequences from 48 embryophyte species. The recovered tree topology indicates that, during grass evolution, RA1 arose from two consecutive SUPERMAN duplications, resulting in three distinct grass sequence lineages: RA1-like A, RA1-like B, and RA1; however, most of these copies have unknown functions. Our findings indicate that RA1 and RA1-like play roles in the nucleus despite lacking a traditional nuclear localization signal. Here, we report that copies diversified their coding region and, with it, their protein structure, suggesting different patterns of DNA binding and protein–protein interaction. In addition, each of the retained copies diversified regulatory elements along their promoter regions, indicating differences in their upstream regulation. Taken together, the evidence indicates that the RA1 and RA1-like gene families in grasses underwent subfunctionalization and neofunctionalization enabled by gene duplication. [ABSTRACT FROM AUTHOR]

CRNDE is considered an oncogene expressed as long non-coding RNA. Our previous paper is the only one reporting CRNDE as a micropeptide-coding gene. The amino acid sequence of this micropeptide (CRNDEP) has recently been confirmed by other researchers. This study aimed at providing a mass spectrometry (MS)-based validation of the CRNDEP sequence and an investigation of how the differential expression of CRNDE(P) influences the metabolism and chemoresistance of ovarian cancer (OvCa) cells. We also assessed cellular localization changes of CRNDEP, looked for its protein partners, and bioinformatically evaluated its RNA-binding capacities. Herein, we detected most of the CRNDEP sequence by MS. Moreover, our results corroborated the oncogenic role of CRNDE, portraying it as the gene impacting carcinogenesis at the stages of DNA transcription and replication, affecting the RNA metabolism, and stimulating the cell cycle progression and proliferation, with CRNDEP being detected in the centrosomes of dividing cells. We also showed that CRNDEP is located in nucleoli and revealed interactions of this micropeptide with p54, an RNA helicase. Additionally, we proved that high CRNDE(P) expression increases the resistance of OvCa cells to treatment with microtubule-targeted cytostatics. Furthermore, altered CRNDE(P) expression affected the activity of the microtubular cytoskeleton and the formation of focal adhesion plaques. Finally, according to our in silico analyses, CRNDEP is likely capable of RNA binding. All these results contribute to a better understanding of the CRNDE(P) role in OvCa biology, which may potentially improve the screening, diagnosis, and treatment of this disease. [ABSTRACT FROM AUTHOR]

Protein structure expectation is a particularly mind boggling issue that it is frequently assaulted and disintegrated using four distinct levels and they are: 1-D forecast of under- lying highlights along the essential succession of amino acids sequences, 2-D forecast of spatial connections between the sequence of amino acids, 3-D forecast of a tertiary structure of protein and quaternary structure of protein. This paper also try to introduce some assessment tools for finding the accuracy of result from applying ML and DL tools. And try to analyses and compare various algorithms based on deep learning methods verses machine learning methods used for sequence prediction. This paper also examines the turn of events and utilization of concealed Markov model, uphold vector machines, Bayesian techniques, and grouping strategies. This investigation will be helpful in creating future strategies to improve the exactness of protein auxiliary structure expectation. In this paper, also introduce and summarize the problem of quantum essential elements of: (1) Variational auto-encoder (2) GAN, generative adversarial network (3) RNN, recurrent neural (4) CNN, convolutional neural networks protein structure prediction. Later on also summarizes the evolution of predictive algorithms for 1-4D structure of protein from Amino Acid Sequences and summarize the deep learning ideas to prediction of structure of protein and learned algorithms of the last decade. [ABSTRACT FROM AUTHOR]

The design of binder proteins for specific target proteins using deep learning is a challenging task that has a wide range of applications in both designing therapeutic antibodies and creating new drugs. Machine learning-based solutions, as opposed to laboratory design, streamline the design process and enable the design of new proteins that may be required to address new and orphan diseases. Most techniques proposed in the literature necessitate either domain knowledge or some appraisal of the target protein's 3-D structure. This paper proposes an approach for designing binder proteins based solely on the amino acid sequence of the target protein and without recourse to domain knowledge or structural information. The sequences of the binders are generated with two new transformers, namely the AppendFormer and MergeFormer architectures. Because, in general, there is more than one binder for a given target protein, these transformers employ a binding score and a prior on the sequence of the binder to obtain a unique targeted solution. Our experimental evaluation confirms the strengths of this novel approach. The performance of the models was determined with 5-fold cross-validation and clearly indicates that our architectures lead to highly accurate results. In addition, scores of up to 0.98 were achieved in terms of Needleman-Wunsch and Smith-Waterman similarity metrics, which indicates that our solutions significantly outperform a seq2seq baseline model. [ABSTRACT FROM AUTHOR]

Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

Background: Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive. Results: Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA. Conclusions: The experimental results show that DGDTA can predict DTA more accurately than some other methods. [ABSTRACT FROM AUTHOR]

Background: Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation. Results: In this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods. Conclusions: The proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening methods. [ABSTRACT FROM AUTHOR]

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance. [ABSTRACT FROM AUTHOR]

The purification and the amino acid sequence of the peptide containing the essential sulphydryl group of arginine kinase from Homarus vulgaris muscle is described. The fractionation of the tryptic digest of arginine kinase labelled with N-[1-14C]ethylmaleimide has been caused out using gel filtration, ion-exchange chromatography and paper chromatography. The composition of the radioactive peptide, isolated from the digest, is reported together with its amino acid sequence. A structural comparison is made between the peptide of arginine kinase and the corresponding active-cysteine containing peptide isolated from rabbit muscle creatine kinase. [ABSTRACT FROM AUTHOR]

Background: The stability of protein sequence structure plays an important role in the prevention and treatment of diseases. Results: In this paper, particle swarm optimization and tabu search are combined to propose a new method for protein structure prediction. The experimental results show that: for four groups of artificial protein sequences with different lengths, this method obtains the lowest potential energy value and stable structure prediction results, and the effect is obviously better than the other two comparison methods. Taking the first group of protein sequences as an example, our method improves the prediction of minimum potential energy by 127% and 7% respectively. Conclusions: Therefore, the method proposed in this paper is more suitable for the prediction of protein structural stability. [ABSTRACT FROM AUTHOR]

Protein sequence similarity analysis (PSSA) is a significant task in bioinformatics, which can obtain information about unknown sequences such as protein structures and homology relationships. Protein sequence refers to the series of amino acids with rich physical and chemical properties, namely the basic structure of proteins. However, sequence similarity analysis and phylogenetic analysis between different species which have complex amino acid sequences is a challenging problem. In this paper, nine properties of amino acids were considered and the sequence was converted into numerical values by principal component analysis (PCA); with Haar Wavelet Transform, and Higuchi fractal dimension (HFD), a new feature vector is constructed to represent the sequence; Spearman distance was selected to calculate the distance matrix and the phylogenetic tree was constructed. In this paper, two representative protein sequences (9 ND5 (NADH dehydrogenase 5) and 8 ND6 (NADH dehydrogenase 6)) were selected for similarity analysis and phylogenetic analysis, and compared with MEGA software and other existing methods. The extensive results show that our method is outperforming and results consistent with the known facts. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate identification of potential targets for drugs to interact with can accelerate drug development. The identification of drug–target interactions can provide insights into hidden drug efficacy. This paper presents a prediction model based on feature similarity fusion that can identify crucial features of drugs and targets to help predict drug–target interactions. The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions. [ABSTRACT FROM AUTHOR]

Photographs are considered composite objects with complex chemical and physical structures. Therefore they are more prone to damage as compared to other objects. Chemical degradation is by far the most common decay form found among photographic collections. This study investigates the chemical degradation of silver gelatin prints (DOP) and the reaction of the image, silver, gelatin, and paper to accelerated aging, to the action of light, and oxidizing gases, in terms of their physical and chemical nature. The test materials used are properly washed and poorly washed grayscale, black-and-white processed images on photographic paper (Black & White Photographic Paper BH 0 Bromofort 6P0661 Tropical from Forte Photochemical Company Vác, Hungary). After exposure, the results were studied by means of visual inspection, amino acid analyzer, Fourier transform infrared and transmission electron microscope. The results were compared with those of the control samples. Our study revealed that the image, silver, gelatin and photographic paper are greatly affected by oxidizing agents and that the effect increased if the photographic prints were inadequately washed at the time of their processing. Furthermore, our results indicated that an increased amount of ammonia and amino acid in the silver gelatin print is a reliable indicator of the degradation of its gelatine emulsion. [ABSTRACT FROM AUTHOR]

The article presents a list of articles related to biochemistry. The articles include "The Reaction of Hemin With H2O2," by M. L. Kremer, "Amino Acid Sequence of the Monomer Subunit of the Giant Multisubunit Hemoglobin From the Earthworm Pheretima sieboldi," by T. Suzuki, "Purification and Properties of Saccharomyces cerevisiae Acetolactate Synthase From Recombinant Escherichia coli," by C. Poulsen and P. Stougaard, "The Mitochondrial Inner-Membrane Anion Channel Possesses Two Mercurial-Reactive Regulatory Sites," by A. D. Beavis.

Motivation Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. Results To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm , which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. Availability and implementation The manual and source code are available at https://github.com/pachterlab/gget. [ABSTRACT FROM AUTHOR]

In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species. Author summary: Understanding the characteristics of data is very important in deep learning methods. When predicting sentences, the transformer model naturally predicts from the first word. For this reason, previous methods predicted peptide sequences from the first amino acid. However, in tandem mass spectra, it is possible to predict peptide sequences bidirectionally. This method shows better results than previous approaches because it can better encode tandem mass spectra. We have demonstrated that good results can be achieved simply by understanding the characteristics of such data and using the model appropriately. We hope that this paper will help various readers improve the performance capabilities of their models. Furthermore, given that bidirectional peptide sequence prediction is crucial in de novo peptide sequence analysis, we hope that this approach will be applied to both existing and future methods utilizing deep learning techniques. [ABSTRACT FROM AUTHOR]

Cytokinins (CKs) are among the hormones that regulate plants' growth and development, and the CKX and IPT genes, which are CK degradation and biosynthesis genes, respectively, play important roles in fine-tuning plants' cytokinin levels. However, the current research on the function of IPT and CKX in cucumber's growth, development, and response to abiotic stress is not specific enough, and their regulatory mechanisms are still unclear. In this study, we focused on the IPT and CKX genes in cucumber, analyzed the physiological and biochemical properties of their encoded proteins, and explored their expression patterns in different tissue parts and under low light, salt stress, and drought stress. Eight CsCKX and eight CsIPT genes were identified from the cucumber genome. We constructed a phylogenetic tree from the amino acid sequences and performed prediction analyses of the cis-acting elements of the CsCKX and CsIPT promoters to determine whether CsCKXs and CsIPTs are responsive to light, abiotic stress, and different hormones. We also performed expression analysis of these genes in different tissues, and we found that CsCKXs and CsIPTs were highly expressed in roots and male flowers. Thus, they are involved in the whole growth and development process of the plant. This paper provides a reference for further research on the biological functions of CsIPT and CsCKX in regulating the growth and development of cucumber and its response to abiotic stress. [ABSTRACT FROM AUTHOR]

Background: The Drug–Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. Results: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. Conclusions: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein–protein interaction networks and drug–drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides' datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active. [ABSTRACT FROM AUTHOR]

Interpreting biological phenomena at the molecular and cellular levels reveals the ways in which information that is specific to living organisms is processed: from the genetic record contained in a strand of DNA, to the translation process, and then to the construction of proteins that carry the flow and processing of information as well as reveal evolutionary mechanisms. The processing of a surprisingly small amount of information, i.e., in the range of 1 GB, contains the record of human DNA that is used in the construction of the highly complex system that is the human body. This shows that what is important is not the quantity of information but rather its skillful use—in other words, this facilitates proper processing. This paper describes the quantitative relations that characterize information during the successive steps of the "biological dogma", illustrating a transition from the recording of information in a DNA strand to the production of proteins exhibiting a defined specificity. It is this that is encoded in the form of information and that determines the unique activity, i.e., the measure of a protein's "intelligence". In a situation of information deficit at the transformation stage of a primary protein structure to a tertiary or quaternary structure, a particular role is served by the environment as a supplier of complementary information, thus leading to the achievement of a structure that guarantees the fulfillment of a specified function. Its quantitative evaluation is possible via using a "fuzzy oil drop" (FOD), particularly with respect to its modified version. This can be achieved when taking into account the participation of an environment other than water in the construction of a specific 3D structure (FOD-M). The next step of information processing on the higher organizational level is the construction of the proteome, where the interrelationship between different functional tasks and organism requirements can be generally characterized by homeostasis. An open system that maintains the stability of all components can be achieved exclusively in a condition of automatic control that is realized by negative feedback loops. This suggests a hypothesis of proteome construction that is based on the system of negative feedback loops. The purpose of this paper is the analysis of information flow in organisms with a particular emphasis on the role of proteins in this process. This paper also presents a model introducing the component of changed conditions and its influence on the protein folding process—since the specificity of proteins is coded in their structure. [ABSTRACT FROM AUTHOR]

Antibodies are important biomolecules that are often designed to recognize target antigens. However, they are expensive to produce and their relatively large size prevents their transport across lipid membranes. An alternative to antibodies is aptamers, short (∼ 15 − 60 bp) oligonucleotides (and amino acid sequences) with specific secondary and tertiary structures that govern their affinity to specific target molecules. Aptamers are typically generated via solid phase oligonucleotide synthesis before selection and amplification through Systematic Evolution of Ligands by EXponential enrichment (SELEX), a process based on competitive binding that enriches the population of certain strands while removing unwanted sequences, yielding aptamers with high specificity and affinity to a target molecule. Mathematical analyses of SELEX have been formulated in the mass action limit, which assumes large system sizes and/or high aptamer and target molecule concentrations. In this paper, we develop a fully discrete stochastic model of SELEX. While converging to a mass-action model in the large system-size limit, our stochastic model allows us to study statistical quantities when the system size is small, such as the probability of losing the best-binding aptamer during each round of selection. Specifically, we find that optimal SELEX protocols in the stochastic model differ from those predicted by a deterministic model. [ABSTRACT FROM AUTHOR]

Chemicals in the environment play a critical role in the etiology of many human diseases. Despite their prevalence, the molecular mechanisms of action and the effects of chemicals on susceptibility to disease are not well understood. To promote understanding of these mechanisms, the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org/) presents scientifically reviewed and curated information on chemicals, relevant genes and proteins, and their interactions in vertebrates and invertebrates. CTD integrates sequence, reference, species, microarray, and general toxicology information to provide a unique centralized resource for toxicogenomic research. The database also provides visualization capabilities that enable cross-species comparisons of gene and protein sequences. These comparisons will facilitate understanding of structure-function correlations and the genetic basis of susceptibility. Manual curation and integration of cross-species chemical-gene and chemical-protein interactions from the literature are now underway. These data will provide information for building complex interaction networks. New CTD features include (1) cross-species gene, rather than sequence, query and visualization capabilities; (2) integrated cross-links to microarray data from chemicals, genes, and sequences in CTD; (3) a reference set related to chemical-gene and protein interactions identified by an information retrieval system; and (4) a “Chemicals in the News” initiative that provides links from CTD chemicals to environmental health articles from the popular press. Here we describe these new features and our novel cross-species curation of chemical-gene and chemical-protein interactions. [ABSTRACT FROM AUTHOR]

The paper was to explore the biological function of the antimicrobial peptide Caerin 2.1, and to analyze the characteristics of the antimicrobial peptide Caerin 2.1 by the online bioinformatics software. The results showed that Caerin 2.1, an antimicrobial peptide derived from bombesin, was composed of 25 amino acids and was a cationic polypeptide. Its secondary structure mainly contained an α -helix, a few β structures and irregular curls. Its theoretical isoelectric point was 9.99, and its net charge was +2. It was stable, hydrophobic, expressed intracellularly and had three phosphorylation sites. The study indicates that the prediction analysis of Caerin 2.1 can provide information basis for modification of natural antimicrobial peptides to obtain stable and safe alternatives to antibiotics. [ABSTRACT FROM AUTHOR]

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies. [ABSTRACT FROM AUTHOR]