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Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors' performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations. [ABSTRACT FROM AUTHOR]

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 µM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery. [ABSTRACT FROM AUTHOR]

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics. [ABSTRACT FROM AUTHOR]

This article discusses the challenges and importance of accurately annotating transporters in metabolic modeling. It emphasizes the need for investment in accurate functional characterization to improve predictive tools and benefit fields such as personalized medicine, metabolic engineering, and microbial ecology. The text also discusses the types of errors that can occur in transporter annotations and the databases and tools available for transporter annotation. The document is a list of references to various scientific articles and databases related to protein subcellular localization, phenotypic testing, gene function, transporter systems, metabolic modeling, and microbial communities. These resources provide valuable information for researchers studying these areas of biology and can be used as references for further exploration. [Extracted from the article]

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three‐dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D‐AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D‐AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/. [ABSTRACT FROM AUTHOR]

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS in EA in ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS in EA in ANN performs significantly better than a canonical genetic algorithm (GA in ANN) and the evolutionary algorithm without reinforcement learning (EA in ANN). Analyses of the parameter's frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS in EA in ANN outperforms other approaches considered the state of the art for the addressed datasets. [ABSTRACT FROM AUTHOR]

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns. Author summary: Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function, and expand drug discovery and development for this important family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning, like AlphaFold2 and ESMFold, have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the multiple sequence alignment depth used in the AlphaFold2 algorithm can help explore the kinase conformational space more broadly. Through a series of quantitative and visual analyses of the models, we also offer a critique of AlphaFold2-generated models, highlighting their potential utility in drug discovery, but also underscoring the need for further refinement to enhance their suitability for rational drug design. Furthermore, many of the high-quality and high-enriching models we make available may represent starting points for novel drug discovery campaigns. [ABSTRACT FROM AUTHOR]

Sedum plumbizincicola is a renowned hyperaccumulator of cadmium (Cd), possesses significant potential for eco-friendly phytoremediation of soil contaminated with Cd. Nevertheless, comprehension of the mechanisms underpinning its Cd stress response remains constrained, primarily due to the absence of a comprehensive genome sequence and an established genetic transformation system. In this study, we successfully identified a novel protein that specifically responds to Cd stress through early comparative iTRAQ proteome and transcriptome analyses under Cd stress conditions. To further investigate its structure, we employed AlphaFold, a powerful tool for protein structure prediction, and found that this newly identified protein shares a similar structure with Arabidopsis AtSIZ1. Therefore, we named it Sedum plumbizincicola SIZ1 (SpSIZ1). Our study revealed that SpSIZ1 plays a crucial role in positively regulating Cd tolerance through its coordination with SpABI5. Overexpression of SpSIZ1 significantly enhanced plant resistance to Cd stress and reduced Cd accumulation. Expression pattern analysis revealed higher levels of SpSIZ1 expression in roots compared to stems and leaves, with up-regulation under Cd stress induction. Importantly, overexpressing SpSIZ1 resulted in lower Cd translocation factors (Tfs) but maintained relatively constant Cd levels in roots under Cd stress, leading to enhanced Cd stress resistance in plants. Protein interaction analysis revealed that SpSIZ1 interacts with SpABI5, and the expression of genes responsive to abscisic acid (ABA) through SpABI5-dependent signaling was significantly up-regulated in SpSIZ1-overexpressing plants with Cd stress treatment. Collectively, our results illustrate that SpSIZ1 interacts with SpABI5, enhancing the expression of ABA downstream stressrelated genes through SpABI5, thereby increasing Cd tolerance in plants. [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]

The article discusses the crystal structure of the MAGEA4 MHD-RAD18 R6BD complex and compares it to the AlphaFold2 prediction. The authors found that the binding mode of the RAD18 R6BD peptide in the crystal structure is flipped compared to the AlphaFold2 model. They also highlight the limitations of AlphaFold2, emphasizing that it is a statistical method based on existing experimental structures and may be biased by those structures. The authors conclude that their structural elucidation provides molecular evidence to clarify the confusion caused by the AlphaFold2 modeling and can aid in the development of MAGEA4-RAD18 antagonists for cancer treatment. [Extracted from the article]

Soybean (Glycine max) and mung bean (Vigna radiata) are key legumes with global importance, but their mechanisms for coping with cold stress—a major challenge in agriculture—have not been thoroughly investigated, especially in a comparative study. This research aimed to fill this gap by examining how these two major legumes respond differently to cold stress and exploring the role of uniconazole, a potential stress mitigator. Our comprehensive approach involved transcriptomic and metabolomic analyses, revealing distinct responses between soybean and mung bean under cold stress conditions. Notably, uniconazole was found to significantly enhance cold tolerance in mung bean by upregulating genes associated with photosynthesis, while its impact on soybean was either negligible or adverse. To further understand the molecular interactions, we utilized advanced machine learning algorithms for protein structure prediction, focusing on photosynthetic pathways. This enabled us to identify LOC106780309 as a direct binding target for uniconazole, confirmed through isothermal titration calorimetry. This research establishes a new comparative approach to explore how soybean and mung bean adapt to cold stress, offers key insights to improve the hardiness of legumes against environmental challenges, and contributes to sustainable agricultural practices and food security. [ABSTRACT FROM AUTHOR]

Motivation The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Results Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. Availability and implementation The source code of ProteStAr is available at https://github.com/refresh-bio/protestar. [ABSTRACT FROM AUTHOR]

TIR1/AFB proteins are a class of auxin receptors with key roles in plant development and biotic and abiotic stress responses; several have been identified as targets of the auxin-mimicking herbicide picloram. In this study, we identified five putative TIR1/AFB gene family members in the important vegetable crop Solanum melongena (eggplant) and characterized them using bioinformatics tools and gene expression analyses. Phylogenetic analysis of the TIR1/AFBs classified them into three subgroups based on their Arabidopsis and Solanum lycopersicum homologs. AFB6 homologs were present only in S. melongena and S. lycopersicum, whereas AFB2/3 homologs were found only in Arabidopsis. One pair of S. melongena TIR1 homologs were located in syntenic regions in the genome and appeared to have arisen by segmental duplication. Promoter analysis revealed 898 cis-elements in the TIR1/AFB promoters, 125 of which were related to hormones, stress, light, or growth responses, but only SmAFB5 had a cis-acting regulatory element involved in auxin responsiveness (AuxRR-core). RNA sequencing and expression profiling showed that the TIR1/AFB genes were differentially expressed at different growth stages and in response to light, temperature, and drought. Only SmTIR1A expression was significantly induced by picloram treatment and different growth stages. TIR1/AFB expression is regulated by microRNAs (miRNAs) in other plant species, and we identified 6 or 29 miRNAs that potentially targeted the five TIR1/AFB genes on the basis of comparisons with S. lycopersicum and S. tuberosum miRNAs, respectively. Three-dimensional protein structure predictions revealed that all the TIR1/AFB proteins were very similar in structure, differing only in the numbers of alpha helices and in one angle linking an α helix and a β sheet. For measuring the function of TIR1/AFB genes in response to drought, SmAFB5 was selected, and knockdown by virus-induced gene silence (VIGS) 35S::SmAFB5 lines showed resistance to drought compared to controls. These analyses provide insight into the potential functions of TIR1/AFBs during growth and in response to stress; they highlight differences among the SmTIR1/AFBs that may be useful for eggplant breeding. [ABSTRACT FROM AUTHOR]

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response. [ABSTRACT FROM AUTHOR]

We report the discovery of a satellite-helper phage system with a novel type of dependence on a tail donor. The Acinetobacter baumannii satellite podovirus Aci01-2- Phanie (short name Phanie) uses a phage phi29-like DNA replication and packaging mode. Its linear 11,885 bp dsDNA genome bears 171 bp inverted terminal repeats (ITR). Phanie is related to phage DU-PP-III from Pectobacterium and to members of the Astrithrvirus from Salmonella enterica. Together, they form a new clade of phages with 27% to 30% identity over the whole genome. Detailed 3D protein structure prediction and mass spectrometry analyses demonstrate that Phanie encodes its capsid structural genes and genes necessary to form a short tail. However, our study reveals that Phanie virions are non-infectious unless they associate with the contractile tail of an unrelated phage, Aci01-1, to produce chimeric myoviruses. Following the coinfection of Phanie with myovirus Aci01-1, hybrid viral particles composed of Phanie capsids and Aci01-1 contractile tails are assembled together with Phanie and Aci01-1 particles. IMPORTANCE There are few reported cases of satellite-helper phage interactions but many more may be yet undiscovered. Here we describe a new mode of satellite phage dependence on a helper phage. Phanie, like phage phi29, replicates its linear dsDNA by a protein primed-mechanism and protects it inside podovirus-like particles. However, these particles are defective, requiring the acquisition of the tail from a myovirus helper for production of infectious virions. The formation of chimeras between a phi29-like podovirus and a helper contractile tail reveals an unexpected association between very different bacterial viruses. [ABSTRACT FROM AUTHOR]

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold. [ABSTRACT FROM AUTHOR]

Background: Deafness autosomal dominant 2A (DFNA2A) is related to non‐syndromic genetic hearing impairment. The KCNQ4 (Potassium Voltage‐Gated Channel Subfamily Q Member 4) can lead to DFNA2A. In this study, we report a case of autosomal dominant non‐syndromic hearing loss with six family members as caused by a novel variant in the KCNQ4 gene. Methods: The whole‐exome sequencing (WES) and pure tone audiometry were performed on the proband of the family. Sanger sequencing was conducted on family members to determine if the novel variant in the KCNQ4 gene was present. Evolutionary conservation analysis and computational tertiary structure protein prediction of the wild‐type KCNQ4 protein and its variant were then performed. In addition, voltage‐gated channel activity of the wild‐type KCNQ4 protein and its variant were tested using whole‐cell patch clamp. Results: It was observed that the proband had inherited autosomal dominant, non‐syndromic sensorineural hearing loss as a trait. A novel co‐segregating heterozygous missense variant (c.902C>A, p.Ala301Asp) of the KCNQ4 gene was identified in the proband and other five affected family members. This variant was predicted to cause an alanine‐to‐aspartic acid substitution at position 301 in the KCNQ4 protein. The alanine at position 301 is well conserved across different species. Whole‐cell patch clamp showed that there was a significant difference between the WT protein currents and the mutant protein currents in the voltage‐gated channel activity. Conclusion: In the present study, performing WES in conjunction with Sanger sequencing enhanced the detection of a novel, potentially causative variant (c301 A>G; p.Ala301Asp) in exon 6 of the KCNQ4 gene. Therefore, our findings contributed to the mutation spectrum of the KCNQ4 gene and may be useful in the diagnosis and gene therapy of deafness autosomal dominant 2A. [ABSTRACT FROM AUTHOR]

Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue–residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions. [ABSTRACT FROM AUTHOR]

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs. [ABSTRACT FROM AUTHOR]

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. [ABSTRACT FROM AUTHOR]

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure. [ABSTRACT FROM AUTHOR]

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology, for its groundbreaking contribution to solving the "protein folding problem". In this perspective, we explore the connection between protein structure studies and environmental research, delving into the potential for addressing specific environmental challenges. Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity. However, structural studies on proteins with environmental significance remain scarce. Here, we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods. Specifically, the latest progress in environmental research via cryogenic electron microscopy is highlighted. It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution, revealing environmentally-associated structural dynamics. With the remarkable advancements in computational power and experimental resolution, the study of protein structure and dynamics has reached unprecedented depth and accuracy. These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases. Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications, such as the degradation of persistent contaminants, and the recovery of valuable metals as well as rare earth elements. [ABSTRACT FROM AUTHOR]

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms. Author summary: AlphaFold2 is software that combines sequence similarity and structure templating with the power of Artificial Intelligence (AI) to bridge the connection between the primary protein structure (amino acid sequence) and higher-level 3D structure (secondary, tertiary, and quaternary). AlphaFold2's impressive and easily accessible nature makes it a bioinformatics tool that has been seeing a wide range of applications in biochemical research. Given this large-scale application, we examined whether Alphafold2 could be integrated into an undergraduate curriculum. We developed a novel module for a senior-level undergraduate biochemistry laboratory class. Our goal was to lay a solid foundation for other undergraduate instructors to be able to adapt this module to fit their classroom needs. While we implemented and ran all predictions on an internal university computing cluster, we recommend ColabFold for those instructors who do not have access to large-scale computational clusters or whose internal clusters cannot scale to their classroom sizes. We have outlined 3 metrics to be quantitatively investigated in the module to give both instructors and students metrics to evaluate model confidence. We have also included a template worksheet, lecture slides, and example scripts to enable instructors to rapidly develop a similar module. We hope that more departments and programs will integrate AlphaFold2 into their undergraduate curriculums, giving students a highly in-demand skill to prepare them for their transition into a career or graduate school. [ABSTRACT FROM AUTHOR]

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems. Author summary: In this paper, we propose a new architecture that aims to improve upon protein structure prediction algorithms like AlphaFold or RoseTTAFold by considering the combinatorial nature of protein structure formation. Such an architecture, reflecting the physical principles of nature, is expected to yield beneficial results, particularly in scenarios with limited structure and sequence information. Named ComMat, this architecture does not focus on a single structure but rather on a set of multiple structures—a community—simultaneously. In this process, combinatorial exploration of protein structure is encouraged through information exchange among community members. ComMat is an instance that integrates this idea within the structure module of AlphaFold. Applying ComMat to antibody CDR H3 loop structure prediction yielded outstanding results in structure sampling and prediction when tested on the IgFold set and compared with IgFold and AlphaFold-Multimer. It confirmed that improved structure sampling stems from effective structural exploration. The proposed concept here could potentially be used in the development of various other combinatorial protein structure prediction and protein design methods. [ABSTRACT FROM AUTHOR]

Occult hepatitis B virus infection (OBI) is characterized by the presence of HBV DNA in the absence of detectable HBsAg. OBI is an important risk factor for cirrhosis and hepatocellular carcinoma, but its pathogenesis has not been fully elucidated. Mutations in the HBV preS/S genes can lead to impaired secretion of either HBsAg or S-protein resulting in the accumulation of defective viruses or S protein in cells. In our previous work, the M133S mutation was present in the HBV S gene of maintenance hemodialysis (MHD) patients with OBI. In this study, we investigated the potential role of amino acid substitutions in S proteins in S protein production and secretion through the construction of mutant S gene plasmids, structural prediction, transcriptome sequencing analysis, and in vitro functional studies. Protein structure prediction showed that the S protein M133S mutant exhibited hydrophilic modifications, with greater aggregation and accumulation of the entire structure within the membrane phospholipid bilayer. Differential gene enrichment analysis of transcriptome sequencing data showed that differentially expressed genes were mainly concentrated in protein processing in the endoplasmic reticulum (ER). The expression of heat shock family proteins and ER chaperone molecules was significantly increased in the wild-type and mutant groups, whereas the expression of mitochondria-associated proteins was decreased. Immunofluorescence staining and protein blotting showed that the endoplasmic reticulum-associated protein PDI, the autophagy marker LC3, and the lysosome-associated protein LAMP2 co-localized with the S proteins in the wild-type and mutant strains, and their expression was increased. The mitochondria-associated TOMM20 protein was also co-expressed with the S protein, but expression was significantly reduced in the mutant. The M133S mutation in the S gene is expressed as a defective and misfolded protein that accumulates in the endoplasmic reticulum causing secretion-impaired endoplasmic reticulum stress, which in turn triggers mitochondrial autophagy and recruits lysosomes to fuse with the autophagosome, leading to mitochondrial clearance. This study preliminarily demonstrated that the mutation of M133S in the S gene can cause OBI and is associated with disease progression, providing a theoretical basis for the diagnosis and treatment of OBI. [ABSTRACT FROM AUTHOR]

The polymorphic membrane proteins (Pmps) are a family of autotransporters that play an important role in infection, adhesion and immunity in Chlamydia trachomatis. Here we show that the characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence, which correspond to the coils of a large beta-helical domain in high quality structure predictions. Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins. While the tetrapeptide motifs themselves are predicted to play a structural role in folding and close stacking of the beta-helical backbone of the passenger domain, we found many of the interesting features of Pmps are localized to the side loops jutting out from the beta helix including protease cleavage, host cell adhesion, and B-cell epitopes; while T-cell epitopes are predominantly found in the beta-helix itself. This analysis more accurately defines the Pmp family of Chlamydia and may better inform rational vaccine design and functional studies. [ABSTRACT FROM AUTHOR]

Aims: Colony stimulating factor 1 receptor (CSF1R)‐related leukoencephalopathy is a rapidly progressing neurodegenerative disease caused by CSF1R gene mutations. This study aimed to identify and investigate the effect of a novel intronic mutation (c.1754‐3C>G) of CSF1R on splicing. Methods: A novel intronic mutation was identified using whole‐exome sequencing. To investigate the impact of this mutation, we employed various bioinformatics tools to analyze the transcription of the CSF1R gene and the three‐dimensional structure of its encoded protein. Furthermore, reverse transcription polymerase chain reaction (RT‐PCR) was performed to validate the findings. Results: A novel mutation (c.1754‐3C>G) in CSF1R was identified, which results in exon 13 skipping due to the disruption of the 3′ splice site consensus sequence NYAG/G. This exon skipping event was further validated in the peripheral blood of the mutation carrier through RT‐PCR and Sanger sequencing. Protein structure prediction indicated a disruption in the tyrosine kinase domain, with the truncated protein showing significant structural alterations. Conclusions: Our findings underscore the importance of intronic mis‐splicing mutations in the diagnosis and management of CSF1R‐related leukoencephalopathy. [ABSTRACT FROM AUTHOR]

Google DeepMind's protein structure prediction algorithm, AlphaFold, has received an update that expands its predictive capabilities to include a broad range of biomolecular interactions. The update has sparked controversy among researchers due to the lack of open-source code accompanying the publication in Nature. Many scientists have expressed frustration at the limited accessibility of AlphaFold 3, as it is only available through a web server with restrictions on inputs and usage. The scientific community is calling for greater transparency and reproducibility in the field of protein structure prediction. [Extracted from the article]

Grain shape is one of the most important factors that affects rice yield. Cloning novel grain shape genes and analyzing their genetic mechanisms are crucial for high yield breeding. In this study, a slender grain CSSL-Z485 with 3-segments substitution in the genetic background of Nipponbare was constructed in rice. Cytological analysis showed that the longer grain length of Z485 was related to the increase in glume cell numbers, while the narrower grain width was associated with the decrease in cell width. Three grain shape-related quantitative trait locus (QTLs), including qGL12, qGW12, and qRLW12, were identified through the F2 population constructed from a cross between Nipponbare and Z485. Furthermore, four single segment substitution lines (SSSLs, S1-S4) carrying the target QTLs were dissected from Z485 by MAS. Finally, three candidate genes of qGL12 for grain length and qGW12 for grain width located in S3 were confirmed by DNA sequencing, RT-qPCR, and protein structure prediction. Specifically, candidate gene 1 encodes a ubiquitin family protein, while candidate genes 2 and 3 encode zinc finger proteins. The results provide valuable germplasm resources for cloning novel grain shape genes and molecular breeding by design. [ABSTRACT FROM AUTHOR]

Motivation Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry. Results In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information. Availability and implementation MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use. [ABSTRACT FROM AUTHOR]

Cysteines (Cys) are chemically reactive amino acids containing sulfur that play diverse roles in plant biology. Recent proteomics investigations in Arabidopsis thaliana have revealed the presence of thiol post-translational modifications (PTMs) in several Cys residues. These PTMs are presumed to impact protein structure and function, yet mechanistic data regarding the specific Cys susceptible to modification and their biochemical relevance remain limited. To help address these limitations, we have conducted a wide-ranging analysis by integrating published datasets encompassing PTM proteomics (comparing S-sulfenylation, persulfidation, S-nitrosylation and S-acylation), genomics and protein structures, with a specific focus on proteins involved in plant lipid metabolism. The prevalence and distribution of modified Cys residues across all analyzed proteins is diverse and multifaceted. Nevertheless, by combining an evaluation of sequence conservation across 100+ plant genomes with AlphaFold-generated protein structures and physicochemical predictions, we have unveiled structural propensities associated with Cys modifications. Furthermore, we have identified discernible patterns in lipid biochemical pathways enriched with Cys PTMs, notably involving beta-oxidation, jasmonic acid biosynthesis, fatty acid biosynthesis and wax biosynthesis. These collective findings provide valuable insights for future investigations targeting the mechanistic foundations of Cys modifications and the regulation of modified proteins in lipid metabolism and other metabolic pathways. [ABSTRACT FROM AUTHOR]

Recent cryo-electron microscopy (cryo-EM) structures have offered critical new insights into the molecular mechanisms governing tRNA biogenesis. Structural and functional studies along with new advances in tRNA sequencing have revealed new details on the processing of tRNAs into tRNA-derived fragments (tRFs) by RNase A family members, RNase L, and Dicer. Recent structural advances and reconstitution experiments have provided new insights into the mechanism of surveillance and decay of tRNAs by exoribonucleases, including the Xrn nucleases and the exosome. Ribonucleases (RNases) such as the SLFN family, ANKZF1, and SAMD9 have new links with tRNA quality control and cellular defense mechanisms. Defining the cellular cues and molecular mechanisms that regulate tRNA processing and decay will enhance our understanding of human diseases associated with tRNA dysregulation. Ribonucleases (RNases) play important roles in supporting canonical and non-canonical roles of tRNAs by catalyzing the cleavage of the tRNA phosphodiester backbone. Here, we highlight how recent advances in cryo-electron microscopy (cryo-EM), protein structure prediction, reconstitution experiments, tRNA sequencing, and other studies have revealed new insight into the nucleases that process tRNA. This represents a very diverse group of nucleases that utilize distinct mechanisms to recognize and cleave tRNA during different stages of a tRNA's life cycle including biogenesis, fragmentation, surveillance, and decay. In this review, we provide a synthesis of the structure, mechanism, regulation, and modes of tRNA recognition by tRNA nucleases, along with open questions for future investigation. [ABSTRACT FROM AUTHOR]

The article discusses the development of an AI-based algorithm called DMFold for predicting the structure of protein-protein complexes. Proteins carry out important biological functions through their interactions with other proteins, known as protein-protein interactions (PPIs). Determining the three-dimensional structures of PPI complexes is crucial for understanding their functions and developing drugs that target PPIs. The DMFold algorithm constructs precise multiple sequence alignments (MSAs) and utilizes co-evolutionary information to predict the structure of PPI complexes. In blind tests, DMFold outperformed other methods, including the widely acclaimed AlphaFold2, in predicting the structures of protein complexes. The algorithm has the potential to enhance protein function annotations and aid in the discovery of drugs targeting PPI-related diseases. [Extracted from the article]

Throughout history, vector-borne diseases have consistently posed significant challenges to human health. Among the strategies for vector control, chemical insecticides have seen widespread use since their inception. Nevertheless, their effectiveness is continually undermined by the steady growth of insecticide resistance within these vector populations. As such, the demand for more robust, efficient, and cost-effective natural insecticides has become increasingly pressing. One promising avenue of research focuses on chitin, a crucial structural component of mosquitoes' exoskeletons and other insects. Chitin not only provides protection and rigidity but also lends flexibility to the insect body. It undergoes substantial transformations during insect molting, a process known as ecdysis. Crucially, the production of chitin is facilitated by an enzyme known as chitin synthase, making it an attractive target for potential novel insecticides. Our recent study delved into the impacts of curcumin, a natural derivative of turmeric, on chitin synthesis and larval development in Aedes aegypti, a mosquito species known to transmit dengue and yellow fever. Our findings demonstrate that even sub-lethal amounts of curcumin can significantly reduce overall chitin content and disrupt the cuticle development in the 4th instar larvae of Aedes aegypti. Further to this, we utilized computational analyses to investigate how curcumin interacts with chitin synthase. Techniques such as molecular docking, pharmacophore feature mapping, and molecular dynamics (MD) simulations helped to illustrate that curcumin binds to the same site as polyoxin D, a recognized inhibitor of chitin synthase. These findings point to curcumin's potential as a natural, bioactive larvicide that targets chitin synthase in mosquitoes and potentially other insects. [ABSTRACT FROM AUTHOR]

Drug-resistant Morganella morganii, a rod-shaped, Gram-negative, facultatively anaerobic bacillus belonging to the Enterobacteriaceae family, is a growing worldwide health concern due to its association with high morbidity and mortality rates. Recent advancements in machine learning, particularly Alphafold 2's protein structure prediction using local physics and pattern recognition, have aided research efforts. This study focuses on the enzymatic activity of aminoglycoside N6′-acetyltransferase (aacA7), a critical transferase enzyme in bacteria that confers resistance to aminoglycosides. AacA7 modifies aminoglycoside molecules by catalyzing the acetylation of their 6′-amino group using acetyl-CoA, rendering antibiotics like kanamycin, neomycin, tobramycin, and amikacin inactive. We propose that Doripenem and OncoglabrinolC can interact with aacA7, potentially modifying its enzymatic activity. Molecular docking analysis of aacA7 with 22 drug targets revealed OncoglabrinolC as the most promising candidate, exhibiting a binding energy of −12.82 kcal/mol. These two top candidates, OncoglabrinolC and Doripenem, were then subjected to 100 ns of molecular dynamic simulations to assess their dynamic conformational features. Furthermore, the PredictSNP consensus classifier was used to predict the impact of mutations on aacA7 protein functionality. The study also investigated the interaction of wild-type and mutant aacA7 proteins with both Doripenem and OncoglabrinolC. These findings provide valuable insights into the binding behavior of OncoglabrinolC and Doripenem as potential lead molecules for repurposing against aacA7, potentially reducing the pathogenicity of Morganella morganii. [ABSTRACT FROM AUTHOR]

p97/valosin-containing protein is an essential eukaryotic AAA+ ATPase with diverse functions including protein homeostasis, membrane remodeling, and chromatin regulation. Dysregulation of p97 function causes severe neurodegenerative disease and is associated with cancer, making this protein a significant therapeutic target. p97 extracts polypeptide substrates from macromolecular assemblies by hydrolysis-driven translocation through its central pore. Growing evidence indicates that this activity is highly coordinated by adapter partner proteins, of which more than 30 have been identified and are commonly described to facilitate translocation through substrate recruitment or modification. In so doing, these adapters enable critical p97-dependent functions such as extraction of misfolded proteins from the endoplasmic reticulum or mitochondria, and are likely the reason for the extreme functional diversity of p97 relative to other AAA+ translocases. Here, we review the known functions of adapter proteins and highlight recent structural and biochemical advances that have begun to reveal the diverse molecular bases for adapter-mediated regulation of p97 function. These studies suggest that the range of mechanisms by which p97 activity is controlled is vastly underexplored with significant advances possible for understanding p97 regulation by the most known adapters.

Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax. This article is protected by copyright. All rights reserved.

Abstract 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.

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.