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Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides. This paper presents BitterPep-GCN, a feature-agnostic graph convolution network for bitter peptide prediction. The graph-based model learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. BitterPep-GCN was benchmarked using BTP640, a publicly available bitter peptide dataset. The latent peptide embeddings generated by the trained model were used to analyze the activity of sequence motifs responsible for the bitter taste of the peptides. Particularly, we calculated the activity for individual amino acids and dipeptide, tripeptide, and tetrapeptide sequence motifs present in the peptides. Our analyses pinpoint specific amino acids, such as F, G, P, and R, as well as sequence motifs, notably tripeptide and tetrapeptide motifs containing FF, as key bitter signatures in peptides. This work not only provides a new predictor of bitter taste for a more efficient identification of bitter peptides in various food products but also gives a hint into the molecular basis of bitterness. Scientific Contribution Our work provides the first application of Graph Neural Networks for the prediction of peptide bitter taste. The best-developed model, BitterPep-GCN, learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. The embeddings were used to analyze the sequence motifs responsible for the bitter taste. [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]

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]

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]

Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein which is dependent on its secondary structure. In this paper, we focus on improving the accuracy of protein secondary structure prediction. To do so, a Multi-scale convolutional neural network with a Gated recurrent neural network (MCNN-GRNN) is proposed. The novel amino acid encoding method along with layered convolutional neural network and Gated recurrent neural network blocks helps to retrieve local and global relationships between features, which in turn effectively classify the input protein sequence into 3 and 8 states. We have evaluated our algorithm on CullPDB, CB513, PDB25, CASP10, CASP11, CASP12, CASP13, and CASP14 datasets. We have compared our algorithm with different state-of-the-art algorithms like DCNN-SS, DCRNN, MUFOLD-SS, DLBLS_SS, and CGAN-PSSP. The Q3 accuracy of the proposed algorithm is 82–87% and Q8 accuracy is 69–77% on different datasets. [ABSTRACT FROM AUTHOR]

This document is a letter addressing errors in a published paper on van der Hoeve's syndrome, a form of osteogenesis imperfecta (OI). The authors of the paper incorrectly assumed that a single-nucleotide deletion in the COL1A1 gene would result in a stable mutant mRNA and a truncated protein. They also presented inaccurate descriptions of the causative variant and the classification of OI types. The letter corrects these errors and provides the appropriate terminology and classification. The document emphasizes the need for accurate scientific reporting and adherence to current practices in the field. [Extracted from the article]

Insufficient research on the material basis and functional product development of horse bones has led to the waste of resources. This paper, as the by-product of horse meat bone marrow was the object, aimed to discover the flavor and antioxidant activity of novel collagen peptides. Two peptides, HBMP-1-1 and HBMP-2-1, were isolated and purified from horse bone marrow collagen protein (HBMP) hydrolysates. The combination of spectroscopic and chromatographic methods including SDS-PAGE, UV, FT-IR, CD, and SEM identified the structures of the peptides. The amino acid composition, flavor characteristics, and antioxidant activity were also evaluated. SDS-PAGE analysis showed that the molecular weight of the two peptides was concentrated under 4.1 kDa. The total amino acid contents of the HBMP-1-1 was up to 570.779 mg/g. Leucine, alanine, and glutamicacid were the predominant amino acids in HBMP-1-1 with high nutritional value, and it contained 10.07% β-folding, 46.39% β-turn, and 43.54% random coil structure. The peptide sequences IDDPTDSKPE, LNGKLTGM, ELDEGYVPK, AFQEDPDKF, and FVGKVVDPTQK with molecular weights of 1116.51, 833.45, 1049.51, 1096.49, and 1217.69 Da were detected using LC-MS/MS. The results of the antioxidant activity test showed that separation and purification significantly improved the bioactivity of the protein. The half-inhibition rate of HBMP-1-1 against DPPH, ABTS, and hydroxyl radicals were 0.054, 0.050, and 0.137 mg/mL, respectively. This article provides a scientific basis for clarifying the material basis of horse bone marrow collagen peptides. Also, it provides new approaches and ideas for exploring efficient and safe bone-derived lead compounds and products. [ABSTRACT FROM AUTHOR]

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications. [ABSTRACT FROM AUTHOR]

Model organism databases are used extensively for knowledge retrieval and knowledge sharing among biologists. With the invention of genome sequencing and protein profiling technologies, large amount of molecular data provides practical insights into the molecular study of model organisms. The knowledge-intensive characteristic of model organism databases provides a reference point for the comparative study of other species. In this paper, I argue that algorithms could be used to facilitate cross-species research. I emphasize the epistemic significance of algorithms in the integration of data for cross-species research. I examine (1) how algorithms guide data integration in model organism databases; and (2) the importance of algorithms for the use of model organism database in the cross-species research. I argue that an extrapolation from the stored data to other species is possible in virtue of the fact that algorithms can facilitate two modes of data integration—viz., inter-level and cross-species integration. Lastly, I examine the implication of the data integration role of an algorithm in light of mechanistic explanation. [ABSTRACT FROM AUTHOR]

The antibody response to vaccination and infection is a key component of the immune response to pathogens. Sequencing of peripheral B cells may not represent the complete B cell receptor repertoire. Here we present a method for sequencing human plasma-derived polyclonal IgG using a combination of mass spectrometry and B-cell sequencing. We investigate the IgG response to the Moderna Spikevax COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generate 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo protein sequencing, exhibit similar or higher binding affinities than the original natural polyclonal antibody. Neutralization tests reveal that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies and the potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Directly examining the circulating IgG pool is crucial due to potential misrepresentations by B-cell analysis alone. The antibody response to infection and vaccination is an essential component of the anti-infective immune response. Here the authors present a de novo protein sequencing method for antibody discovery from polyclonal IgG from human plasma and characterise the antibody response to the Moderna Spikevax COVID-19 vaccine. [ABSTRACT FROM AUTHOR]

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research. Machine learning has been used to reveal the relationship between protein sequences and biocatalytic enantioselectivity, but the establishment of substrate fitness space is challenging. Herein, the authors build random forest classification models for predicting the enantioselectivity of amidase toward different substrates, adopting chemistry and geometry descriptors. [ABSTRACT FROM AUTHOR]

Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the hemolytic activity of therapeutic peptides is essential for the development of peptide therapies. In this study, a multi-feature cross-fusion model, HemoFuse, for hemolytic peptide identification is proposed. The feature vectors of peptide sequences are transformed by word embedding technique and four hand-crafted feature extraction methods. We apply multi-head cross-attention mechanism to hemolytic peptide identification for the first time. It captures the interaction between word embedding features and hand-crafted features by calculating the attention of all positions in them, so that multiple features can be deeply fused. Moreover, we visualize the features obtained by this module to enhance its interpretability. On the comprehensive integrated dataset, HemoFuse achieves ideal results, with ACC, SP, SN, MCC, F1, AUC, and AP of 0.7575, 0.8814, 0.5793, 0.4909, 0.6620, 0.8387, and 0.7118, respectively. Compared with HemoDL proposed by Yang et al., it is 3.32%, 3.89%, 5.93%, 10.6%, 8.17%, 5.88%, and 2.72% higher. Other ablation experiments also prove that our model is reasonable and efficient. The codes and datasets are accessible at https://github.com/z11code/Hemo. [ABSTRACT FROM AUTHOR]

Identifying anti-inflammatory peptides (AIPs) and antimicrobial peptides (AMPs) is crucial for the discovery of innovative and effective peptide-based therapies targeting inflammation and microbial infections. However, accurate identification of AIPs and AMPs remains a computational challenge mainly due to limited utilization of peptide sequence information. Here, we propose PepNet, an interpretable neural network for predicting both AIPs and AMPs by applying a pre-trained protein language model to fully utilize the peptide sequence information. It first captures the information of residue arrangements and physicochemical properties using a residual dilated convolution block, and then seizes the function-related diverse information by introducing a residual Transformer block to characterize the residue representations generated by a pre-trained protein language model. After training and testing, PepNet demonstrates great superiority over other leading AIP and AMP predictors and shows strong interpretability of its learned peptide representations. A user-friendly web server for PepNet is freely available at http://liulab.top/PepNet/server. PepNet achieves accurate identification of both AIPs and AMPs by developing an interpretable neural network and applying a pre-trained protein language model. [ABSTRACT FROM AUTHOR]

Symmetry in nature often emerges from self-assembly processes and serves a wide range of functions. Cell surface layers (S-layers) form symmetrical lattices on many bacterial and archaeal cells, playing essential roles such as facilitating cell adhesion, evading the immune system, and protecting against environmental stress. However, the experimental structural characterization of these S-layers is challenging due to their self-assembly properties and high sequence variability. In this study, we introduce the SymProFold pipeline, which utilizes the high accuracy of AlphaFold-Multimer predictions to derive symmetrical assemblies from protein sequences, specifically focusing on two-dimensional S-layer arrays and spherical viral capsids. The pipeline tests all known symmetry operations observed in these systems (p1, p2, p3, p4, and p6) and identifies the most likely symmetry for the assembly. The predicted models were validated using available experimental data at the cellular level, and additional crystal structures were obtained to confirm the symmetry and interfaces of several SymProFold assemblies. Overall, the SymProFold pipeline enables the determination of symmetric protein assemblies linked to critical functions, thereby opening possibilities for exploring functionalities and designing targeted applications in diverse fields such as nanotechnology, biotechnology, medicine, and materials and environmental sciences. Here, the authors present SymProFold, a method that predicts the structure of symmetrical protein assemblies such as bacterial surface layers and viral capsids by utilising AlphaFold Multimer predictions. [ABSTRACT FROM AUTHOR]

How complex are the rules by which a protein's sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence—which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis—or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird's-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins' genetic architecture. Understanding protein sequence-function relationships is complicated by high order epistatic interactions among residues, although the extent of these interactions remains uncertain. Here, the authors present a reference-free method which suggests that sequence-function relationships are relatively simple, with little influence from high order epistatic interactions. [ABSTRACT FROM AUTHOR]

DNA-protein interactions exert the fundamental structure of many pivotal biological processes, such as DNA replication, transcription, and gene regulation. However, accurate and efficient computational methods for identifying these interactions are still lacking. In this study, we propose a method ESM-DBP through refining the DNA-binding protein sequence repertory and domain-adaptive pretraining based the general protein language model. Our method considers the lacking exploration of general language model for DNA-binding protein domain-specific knowledge, so we screen out 170,264 DNA-binding protein sequences to construct the domain-adaptive language model. Experimental results on four downstream tasks show that ESM-DBP provides a better feature representation of DNA-binding protein compared to the original language model, resulting in improved prediction performance and outperforming the state-of-the-art methods. Moreover, ESM-DBP can still perform well even for those sequences with only a few homologous sequences. ChIP-seq on two predicted cases further support the validity of the proposed method. DNA-protein interactions form the fundamental structure of many pivotal biological processes. Here, authors propose ESM-DBP, which refines the DNA-binding protein sequence repertoire and domain-adaptive pretraining on general protein language models to predict DNA-binding protein-related tasks. [ABSTRACT FROM AUTHOR]

The inversion of substrate size specificity is an evolutionary roadblock for proteins. The Duf4243 dioxygenases GedK and BTG13 are known to catalyze the aromatic cleavage of bulky tricyclic hydroquinone. In this study, we discover a Duf4243 dioxygenase PaD that favors small monocyclic hydroquinones from the penicillic-acid biosynthetic pathway. Sequence alignments between PaD and GedK and BTG13 suggest PaD has three additional motifs, namely motifs 1-3, distributed at different positions in the protein sequence. X-ray crystal structures of PaD with the substrate at high resolution show motifs 1-3 determine three loops (loops 1-3). Most intriguing, loops 1-3 stack together at the top of the pocket, creating a lid-like tertiary structure with a narrow channel and a clearly constricted opening. This drastically changes the substrate specificity by determining the entry and binding of much smaller substrates. Further genome mining suggests Duf4243 dioxygenases with motifs 1-3 belong to an evolutionary branch that is extensively involved in the biosynthesis of natural products and has the ability to degrade diverse monocyclic hydroquinone pollutants. This study showcases how natural enzymes alter the substrate specificity fundamentally by incorporating new small motifs, with a fixed overall scaffold-architecture. It will also offer a theoretical foundation for the engineering of substrate specificity in enzymes and act as a guide for the identification of aromatic dioxygenases with distinct substrate specificities. The Duf4243 dioxygenases GedK and BTG13 are known to catalyze the aromatic cleavage of bulky tricyclic hydroquinone. Here, the authors discover a distinct Duf4243 dioxygenase PaD that favors small monocyclic hydroquinones from the penicillic-acid biosynthetic pathway and determine the structural basis for its changed substrate specificity. [ABSTRACT FROM AUTHOR]

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations. [ABSTRACT FROM AUTHOR]

Recent work suggests that AlphaFold (AF)–a deep learning-based model that can accurately infer protein structure from sequence–may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF's training sets. AF2's confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations. Studies on fold-switching proteins using AlphaFold reveal that the tool associates sequences of some proteins with structures it memorized during training, explaining its failure to predict correct structures of some recently characterized proteins. [ABSTRACT FROM AUTHOR]

Protein sequence diversification significantly impacts physiological traits. In this study, using medaka fish (Oryzias latipes), we identify a novel protein variant affecting shape preference behavior. Re-analysis of sequencing data reveals that LOC101156433 encodes a unique Hmgn2 variant with unusual subnuclear localization, clustered separately from the Hmgn2 clades of other species. Medaka mutants with this variant showed reduce telencephalic regions and altered shape preference, suggesting a link between protein sequence variation and behavioral changes. Additionally, this Hmgn2 variant is common in Acanthopterygii fishes, which are adapted to a variety of environments, indicating its potential evolutionary significance. Our findings highlight the relationship between amino acid sequence variation and the development of new molecular and behavioral adaptations, providing insights into the visual shape perception system in fish. In medaka, a unique Hmgn2 variant showed unusual subnuclear localization and mutants had abnormal shape preference. This variant is common in Acanthopterygii, indicating evolutionary significance and linking sequence variation to adaptations. [ABSTRACT FROM AUTHOR]

Lipopeptides can self-assemble into diverse nanostructures which can be programmed to incorporate peptide sequences to achieve a remarkable range of bioactivities. Here, the influence of peptide sequence and chirality on micelle structure and interactions is investigated in a series of lipopeptides bearing two lysine or D-lysine residues and tyrosine or tryptophan residues, attached to a hexadecyl lipid chain. All molecules self-assemble into micelles above a critical micelle concentration (CMC). Small-angle x-ray scattering (SAXS) is used to probe micelle shape and structure from the form factor and to probe inter-micellar interactions via analysis of structure factor. The CMC is obtained consistently from surface tension and electrical conductivity measurements. We introduce a method to obtain the zeta potential from the SAXS structure factor which is in good agreement with directly measured values. Atomistic molecular dynamics simulations provide insights into molecular packing and conformation within the lipopeptide micelles which constitute model self-assembling colloidal systems and biomaterials. Lipopeptides show great potential as green surfactants but the aggregation behavior of designed lipopeptides bearing linear peptide sequences remains understudied. Here, the authors examine the micellization of chiral lipopeptides bearing lysine-rich tripeptide sequences and investigate structural effects as well as the surface tension and electrical conductivity. [ABSTRACT FROM AUTHOR]

Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model's final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets. PTMGPT2, which leverages prompt-based fine-tuning on protein sequences, predicts post-translational modifications (PTMs) with higher accuracy. It interprets attention scores as motifs and identifies pathogenic mutations, providing insights into protein functionality. [ABSTRACT FROM AUTHOR]

Introduction: Understanding the differences between respiratory syncytial virus (RSV) subgroups A and B provides insights for the development of prevention strategies and public health interventions. We aimed to describe the structural differences of RSV subgroups, their epidemiology, and genomic diversity. The associated immune response and differences in clinical severity were also investigated. Methods: A literature review from PubMed and Google Scholar (1985–2023) was performed and extended using snowballing from references in captured publications. Results: RSV has two major antigenic subgroups, A and B, defined by the G glycoprotein. The RSV F fusion glycoprotein in the prefusion conformation is a major target of virus neutralizing antibodies and differs in surface exposed regions between RSV A and RSV B. The subgroups co-circulate annually, but there is considerable debate as to whether clinical severity is impacted by the subgroup of the infecting RSV strain. Large variations between the studies reporting RSV subgroup impact on clinical severity were observed. A tendency for higher disease severity may be attributed to RSV A but no consensus could be reached as to whether infection by one of the subgroup caused more severe outcomes. RSV genotype diversity decreased over the last two decades, and ON and BA have become the sole lineages detected for RSV A and RSV B, since 2014. No studies with data obtained after 2014 reported a difference in disease severity between the two subgroups. RSV F is relatively well conserved and highly similar between RSV A and B, but changes in the amino acid sequence have been observed. Some of these changes led to differences in F antigenic sites compared to reference F sequences (e.g., RSV/A Long strain), which are more pronounced in antigenic sites of the prefusion conformation of RSV B. Initial results from the second season after vaccination suggest specific RSV B efficacy wanes more rapidly than RSV A for RSV PreF-based monovalent vaccines. Conclusions: RSV A and RSV B both contribute substantially to the global RSV burden. Both RSV subgroups cause severe disease and none of the available evidence to date suggests any differences in clinical severity between the subgroups. Therefore, it is important to implement measures effective at preventing disease due to both RSV A and RSV B to ensure impactful public health interventions. Monitoring overtime will be needed to assess the impact of waning antibody levels on subgroup-specific efficacy. [ABSTRACT FROM AUTHOR]

The article discusses the historical and ongoing relationship between science and technology. It highlights how science has been used throughout history to understand and improve various aspects of human life. The article also explores the fear of automation and the potential for artificial intelligence (AI) to replace human labor. It discusses the role of AI in scientific research and the potential for AI to augment and transform the scientific method. The article concludes by raising questions about the future of science and the impact of AI on human identity. [Extracted from the article]

Photosystem I (PSI) from Fittonia albivenis, an Acanthaceae ornamental plant, is notable among green plants for its red-shifted emission spectrum. Here, we solved the structure of a PSI–light harvesting complex I (LHCI) supercomplex from F. albivenis at 2.46-Å resolution using cryo-electron microscopy. The supercomplex contains a core complex of 14 subunits and an LHCI belt with four antenna subunits (Lhca1–4) similar to previously reported angiosperm PSI–LHCI structures; however, Lhca3 differs in three regions surrounding a dimer of low-energy chlorophylls (Chls) termed red Chls, which absorb far-red beyond visible light. The unique amino acid sequences within these regions are exclusively shared by plants with strongly red-shifted fluorescence emission, suggesting candidate structural elements for regulating the energy state of red Chls. These results provide a structural basis for unraveling the mechanisms of light harvest and transfer in PSI–LHCI of under canopy plants and for designing Lhc to harness longer-wavelength light in the far-red spectral range. Fittonia albivenis is shade-adapted ornamental plant that can efficiently use far-red light for photosynthesis. Here the authors describe the structure of the red-shifted F. albivenis photosystem I to give insights into how plants can use far-red light to drive photochemistry. [ABSTRACT FROM AUTHOR]

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information—de novo peptide sequencing—is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome. Identification of the peptide that generates each acquired tandem mass spectrum is a fundamental challenge in mass spectrometry-based proteomics. Here, the authors present Casanovo, a machine learning model that translates the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. [ABSTRACT FROM AUTHOR]

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral–host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition–transition–distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein–human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage. [ABSTRACT FROM AUTHOR]

Having multiple rounds of translation of the same mRNA creates dynamic complexities along with opportunities for regulation related to ribosome pausing and stalling at specific sequences. Yet, mechanisms controlling these critical processes and the principles guiding their evolution remain poorly understood. Through genetic, genomic, physiological, and biochemical approaches, we demonstrate that regulating ribosome pausing at specific amino acid sequences can produce ~2-fold changes in protein expression levels which strongly influence cell growth and therefore evolutionary fitness. We demonstrate, both in vivo and in vitro, that the ABC-F protein EttA directly controls the translation of mRNAs coding for a subset of enzymes in the tricarboxylic acid (TCA) cycle and its glyoxylate shunt, which modulates growth in some chemical environments. EttA also modulates expression of specific proteins involved in metabolically related physiological and stress-response pathways. These regulatory activities are mediated by EttA rescuing ribosomes paused at specific patterns of negatively charged residues within the first 30 amino acids of nascent proteins. We thus establish a unique global regulatory paradigm based on sequence-specific modulation of translational pausing. The physiological function of the ABC-F translation factor has remained elusive. Here, the authors show that one of them, EttA, regulates the translation of mRNAs encoding early acidic residues, and that impacts the physiology of central metabolism. [ABSTRACT FROM AUTHOR]

Prior to the formation of amyloid fibrils, the pathological hallmark in tau-related neurodegenerative disease, tau monomers aggregate into a diverse range of oligomers. Granular tau oligomers, consisting of approximately 40 tau protein molecules, are present in the prefrontal cortex of patients at Braak stages I-II, preclinical stages of Alzheimer's disease (AD). Antibodies to granular tau oligomers as antigens have not been reported. Therefore, we generated new rat monoclonal antibodies by immunization with granular tau oligomers. Three antibodies from different hybridoma clones showed stronger immunoreactivity to granular tau oligomers and tau fibrils compared with monomeric tau. Of the three antibodies, 2D6-2C6 showed 3000-fold greater immunoreactivity in P301L-tau transgenic (rTg4510) mice than in non-transgenic mice, while MC1 antibody, which detects pathological conformations of tau, showed a 5.5-fold increase. These results suggest that 2D6-2C6 recognizes aggregates more specifically than MC1. In AD subjects, 2D6-2C6 recognized neurofibrillary tangles and pretangles, and co-localized within AT8-positive cells containing phosphorylated tau aggregates. The epitope of 2D6-2C6 is the 423–430 amino acid (AA) sequence of C-terminal regions. Taken together, a novel monoclonal antibody, 2D6-2C6, generated by immunization with granular tau oligomers binds to tau aggregates at the 423–430 AA sequence. [ABSTRACT FROM AUTHOR]

Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions. Advances in deep learning are transforming protein design. Here, authors introduce a method using geometric transformers to predict protein sequences, resulting in highly thermostable and catalytically active enzymes with high success rates. [ABSTRACT FROM AUTHOR]

HP1 proteins are essential for establishing and maintaining transcriptionally silent heterochromatin. They dimerize, forming a binding interface to recruit diverse chromatin-associated factors. Although HP1 proteins are known to rapidly evolve, the extent of variation required to achieve functional specialization is unknown. To investigate how changes in amino acid sequence impacts heterochromatin formation, we performed a targeted mutagenesis screen of the S. pombe HP1 homolog, Swi6. Substitutions within an auxiliary surface adjacent to the HP1 dimerization interface produce Swi6 variants with divergent maintenance properties. Remarkably, substitutions at a single amino acid position lead to the persistent gain or loss of epigenetic inheritance. These substitutions increase Swi6 chromatin occupancy in vivo and altered Swi6-protein interactions that reprogram H3K9me maintenance. We show how relatively minor changes in Swi6 amino acid composition in an auxiliary surface can lead to profound changes in epigenetic inheritance providing a redundant mechanism to evolve HP1-effector specificity. HP1 proteins are rapidly evolving heterochromatin factors that are essential for silencing. Here, the authors identify an interface where subtle amino acid changes can lead to novel epigenetic inheritance phenotypes, acting as an effector toggle switch to regulate the persistence of epigenetic memory. [ABSTRACT FROM AUTHOR]

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates. Biomolecular condensates help cells organise their content in space and time. Here the authors report a machine learning driven methodology to predict the composition of biomolecular condensates and they then validate their predictions against the composition of known biomolecular condensates. [ABSTRACT FROM AUTHOR]

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires. Artificial intelligence holds great promise to improve diagnosis of numerous immune-related or infectious diseases. Here, the authors show that machine learning can be used to identify HIV-1 specific broad neutralising antibody. [ABSTRACT FROM AUTHOR]

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications. A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Here the authors use the evolutionary model EVcouplings to computationally design highly mutated variants of TEM-1 ß-lactamase, and characterise these designs experimentally. [ABSTRACT FROM AUTHOR]

Type IV pili (T4P) represent one of the most common varieties of surface appendages in archaea. These filaments, assembled from small pilin proteins, can be many microns long and serve diverse functions, including adhesion, biofilm formation, motility, and intercellular communication. Here, we determine atomic structures of two distinct adhesive T4P from Saccharolobus islandicus via cryo-electron microscopy (cryo-EM). Unexpectedly, both pili were assembled from the same pilin polypeptide but under different growth conditions. One filament, denoted mono-pilus, conforms to canonical archaeal T4P structures where all subunits are equivalent, whereas in the other filament, the tri-pilus, the same polypeptide exists in three different conformations. The three conformations in the tri-pilus are very different from the single conformation found in the mono-pilus, and involve different orientations of the outer immunoglobulin-like domains, mediated by a very flexible linker. Remarkably, the outer domains rotate nearly 180° between the mono- and tri-pilus conformations. Both forms of pili require the same ATPase and TadC-like membrane pore for assembly, indicating that the same secretion system can produce structurally very different filaments. Our results show that the structures of archaeal T4P appear to be less constrained and rigid than those of the homologous archaeal flagellar filaments that serve as helical propellers. Type IV pili (T4P) are long surface appendages assembled from small pilin proteins that participate in diverse functions such as adhesion and biofilm formation in archaea. Here, the authors show that the same pilin polypeptide can adopt four conformations and assemble two distinct T4P structures under different growth conditions. [ABSTRACT FROM AUTHOR]

Human type 1 diabetes (T1D) is caused by autoimmune attack on the insulin-producing pancreatic beta cells by islet antigen-reactive T cells. How human islet antigen-reactive (IAR) CD4+ memory T cells from peripheral blood affect T1D progression in the pancreas is poorly understood. Here, we aim to determine if IAR T cells in blood could be detected in pancreas. We identify paired αβ (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new-onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from healthy, at risk and T1D organ donors. We report extensive TRA junction sharing between IAR T cells and pancreas-infiltrating T cells (PIT), with perfect-match or single-mismatch TRA junction amino acid sequences comprising ~29% total unique IAR TRA junctions (942/3,264). PIT-matched TRA junctions were largely public and enriched for TRAV41 usage, showing significant nucleotide sequence convergence, increased use of germline-encoded versus non-templated residues in epitope engagement, and a potential for cross-reactivity. Our findings thus link T cells with distinctive germline-like TRA chains in the peripheral blood with T cells in the pancreas. Most efforts to examine islet antigen-reactive T cells in type-1 diabetes have been limited to peripheral blood. In this study, the authors examine the T cell receptor (TCR) repertoire of insulin antigen-reactive T cells from peripheral blood from patients with type-1 diabetes as well as pancreata from deceased donors. [ABSTRACT FROM AUTHOR]

Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features. Sustainable chemistry can benefit from biocatalysis, but a high diversity of enzymes is needed. Here, the authors screen billions of protein sequences to provide an overview of the native amine dehydrogenase family for amine synthesis. [ABSTRACT FROM AUTHOR]

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences. [ABSTRACT FROM AUTHOR]

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452. We implement SOFB, an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method to characterize protein sequences by learning the semantics of biological dynamics contexts. [ABSTRACT FROM AUTHOR]

Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository. [ABSTRACT FROM AUTHOR]

Titin N2B unique sequence (N2B-us) is a 572 amino acid sequence that acts as an elastic spring to regulate muscle passive elasticity. It is thought to lack stable tertiary structures and is a force-bearing region that is regulated by mechanical stretching. In this study, the conformation of N2B-us and its interaction with four-and-a-half LIM domain protein 2 (FHL2) are investigated using AlphaFold2 predictions and single-molecule experimental validation. Surprisingly, a stable alpha/beta structural domain is predicted and confirmed in N2B-us that can be mechanically unfolded at forces of a few piconewtons. Additionally, more than twenty FHL2 LIM domain binding sites are predicted to spread throughout N2B-us. Single-molecule manipulation experiments reveals the force-dependent binding of FHL2 to the N2B-us structural domain. These findings provide insights into the mechano-sensing functions of N2B-us and its interactions with FHL2. Titin N2B unique sequence (N2B-us) is a 572 amino acid sequence that acts as an elastic spring to regulate muscle passive elasticity. Here the authors identify a mechanosensitive structural domain within the titin (N2B-us), and a force-dependent interaction between (N2B-us) and the protein FHL2. [ABSTRACT FROM AUTHOR]

Background: A major challenge of evolutionary biology is making underlying concepts accessible to wide audiences. One method for doing so is to utilise multi-media formats that have potential to engage and inform through entertainment. This pilot study outlines and discusses a sonification concert that integrated musical performance with a range of evolutionary concepts and ideas fundamental to an understanding of evolution, such as protein sequences. We aimed to showcase sound-art objects and live-coding performances created using sonification as a mechanism for presenting complex biological processes to both researcher and non-researchers. We sought to evaluate the effectiveness of this art-adjacent practice for public engagement with evolutionary biology research, and also to gather feedback to guide future events. Toward this end, we held a live concert showcasing biologically-based algorithmic music exploring links between evolutionary biology research, sound art, and musical performance. The event had three main acts: a generative audio-visual piece giving an artistic representation of SARS coronavirus based on a parameter-mapping sonification of protein sequence of the replicase polyprotein; a pre-recorded string ensemble demonstrating the effects of codon selection on translation speed using parameter-mapping sonification; and a live-coded music piece interactively sonifying protein structures. Results: Our event attracted 90 attendees. We evaluated success using direct observation and written feedback forms with a 58% response rate: 95% of respondents stated they had enjoyed the event and 63% indicated they were inspired by it. Conclusions: Presenting the sonic outputs of sonification research in a concert format showed good potential for the pursuit of public engagement with evolutionary biology research, demonstrating the ability to engage curiosity and inspire an audience while also conveying scientific content alongside the nuanced and complex world of modern evolutionary biology. [ABSTRACT FROM AUTHOR]

β-N-Acetylgalactosamine-containing glycans play essential roles in several biological processes, including cell adhesion, signal transduction, and immune responses. β-N-Acetylgalactosaminidases hydrolyze β-N-acetylgalactosamine linkages of various glycoconjugates. However, their biological significance remains ambiguous, primarily because only one type of enzyme, exo-β-N-acetylgalactosaminidases that specifically act on β-N-acetylgalactosamine residues, has been documented to date. In this study, we identify four groups distributed among all three domains of life and characterize eight β-N-acetylgalactosaminidases and β-N-acetylhexosaminidase through sequence-based screening of deep-sea metagenomes and subsequent searching of public protein databases. Despite low sequence similarity, the crystal structures of these enzymes demonstrate that all enzymes share a prototype structure and have diversified their substrate specificities (oligosaccharide-releasing, oligosaccharide/monosaccharide-releasing, and monosaccharide-releasing) through the accumulation of mutations and insertional amino acid sequences. The diverse β-N-acetylgalactosaminidases reported in this study could facilitate the comprehension of their structures and functions and present evolutionary pathways for expanding their substrate specificity. Four β-N-acetylgalactosaminidase gene families were identified via deep-sea metagenome analysis. The biochemical and structural characterization of the aforementioned enzymes revealed their functional diversity and monophyletic evolutionary history. [ABSTRACT FROM AUTHOR]

While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems. Reynolds and colleagues examine a biochemically-mediated epistatic interaction between metabolic enzymes involved in folate metabolism and show that biochemical coupling shapes the range of enzyme activities sufficient to rescue cell growth. [ABSTRACT FROM AUTHOR]

Rab5A play important roles in regulating trafficking organelles, especially in phagosome formation. In the present study, full-length cDNA sequences of Rab5A were cloned from Yellow River Carp Cyprinus carpio, which was designated as CcRab5A. The full-length cDNA of the CcRab5A cDNA sequence is 2434 bp and included an open reading frame (ORF) encoding 216 amino acids polypeptide with an estimated molecular weight of 23.47 kDa. Bioinformatics analysis showed that the CcRab5A protein was highly conserved during evolution. CcRab5A's deduced amino acid sequence showed high identity to Cyprinus carpio (99.54%) in comparison. The guanine-base binding motif (G), phosphate/magnesium-binding motif (PM), and Rab family motif (Rab F) of CcRab5A are highly conserved among various species, but the N- and C-terminal regions were hypervariable, according to the results of multiple sequence alignment and phylogenetic analysis. Additionally, 11 tissues of Yellow River Carp were examined using Real-time Fluorescence Quantitative PCR (qRT-PCR) to determine the expression levels, with the highest expression levels in head kidney and blood. Followed by heart, liver, muscle, brain, gill, skin, spleen and intestine; The expression level in body kidney was the lowest. Yellow River Carp was immunized with Aeromonas hydrophila and Spring viremia of carp virus (SVCV) respectively, and the expression changes of CcRab5A in gill, spleen, liver, intestine and skin of Yellow River Carp were detected. The results showed that the expression level of the gene was obviously up-regulated at different time points. The eukaryotic recombinant plasmids of CcRab5A, pEGFP-N3 were constructed and transfected into GCO cells for subcellular localization. The results showed that CcRab5A were mainly distributed in nuclear membrane and various endosome membranes. These results showed that CcRab5 were involved in viral and bacterial infection in the immune response of Yellow River Carp. [ABSTRACT FROM AUTHOR]

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods. [ABSTRACT FROM AUTHOR]

The presence of oropharyngeal human papillomavirus (HPV)-18 E6 and E7 oncoproteins is highly significant in the progression of oropharyngeal cancer. Natural flavonoid compounds have potential as photosensitizers for light-activated antimicrobial therapy against HPV-associated oropharyngeal cancer. This study evaluated five natural flavonoid glycosides including Fisetin, Kaempferol, Morin, Myricetin, and Quercetin as photosensitizers against HPV-18 E6 and E7 oncoproteins using computational methods. After obtaining the amino acid sequences of HPV-18 E6 and E7, various tools were used to predict and verify their properties. The PubChem database was then examined to identify potential natural flavonoid glycosides, followed by predictions of their drug-likeness and ADMET properties. Subsequently, molecular docking was conducted to enhance the screening accuracy and to gain insights into the interactions between the natural compounds and the active sites of HPV-18 E6 and E7 oncoproteins. The protein structures of E6 and E7 were predicted and validated to be reliable. The results of molecular docking demonstrated that Kaempferol exhibited the highest binding affinity to both E6 and E7. All compounds satisfied Lipinski's rules of drug-likeness, except Myricetin. They showed high absorption, distribution volume and similar ADMET profiles with no toxicity. In summary, natural flavonoid glycosides, especially Kaempferol, show potential as photosensitizers for antimicrobial photodynamic therapy against HPV-associated oropharyngeal cancer through inhibition of E6 and E7 oncoproteins. These findings provide insights into the development of novel therapeutic strategies based on antimicrobial photodynamic therapy. [ABSTRACT FROM AUTHOR]

Material properties of phase-separated biomolecular condensates, enriched with disordered proteins, dictate many cellular functions. Contrary to the progress made in understanding the sequence-dependent phase separation of proteins, little is known about the sequence determinants of condensate material properties. Using the hydropathy scale and Martini models, we computationally decipher these relationships for charge-rich disordered protein condensates. Our computations yield dynamical, rheological, and interfacial properties of condensates that are quantitatively comparable with experimentally characterized condensates. Interestingly, we find that the material properties of model and natural proteins respond similarly to charge segregation, despite different sequence compositions. Molecular interactions within the condensates closely resemble those within the single-chain ensembles. Consequently, the material properties strongly correlate with molecular contact dynamics and single-chain structural properties. We demonstrate the potential to harness the sequence characteristics of disordered proteins for predicting and engineering the material properties of functional condensates, with insights from the dilute phase properties. How material properties affect the functional state of biocondensates is a long-standing question. Here the authors demonstrate how such properties are encoded in a disordered protein sequence and can be predicted from dilute phase conformations. [ABSTRACT FROM AUTHOR]

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design. Here the authors report a computational approach which integrates deep learning and structural modelling to design target-specific peptides. They apply this to β-catenin and NF-κB essential modulator, resulting in improved binding, highlighting the efficacy of this strategy. [ABSTRACT FROM AUTHOR]