Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions

The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current computational landscape for predicting protein complex structures, outlining the role of traditional docking approaches as well as focusing on recent advances in AI-driven methods. We discuss key challenges, including protein flexibility, reliance on co-evolutionary signals, modeling of large assemblies, and interactions involving intrinsically disordered regions (IDRs). Recent innovations aimed at improving sampling diversity, integrating experimental data, and enhancing robustness are also highlighted. Although classical methods remain relevant in specific contexts, the continued evolution of AI-based tools offers transformative potential for structural biology. These advances are poised to deepen our understanding of biomolecular interactions and accelerate the design of therapeutic interventions.

Characterization of two GH10 enzymes with ability to hydrolyze pretreated Sorghum bicolor bagasse

In this study, we characterized two novel enzymes of the glycoside hydrolase family 10 (GH10), Xyl10 C and Xyl10E, identified in the termite gut microbiome. The activities of both enzymes were assayed using beechwood xylan, barley β-glucan, and pretreated Sorghum bicolor bagasse (SBB) as substrates. Both enzymes, assessed individually and in combination, showed activity on beechwood xylan and pretreated SBB, whereas Xyl10E also showed activity on barley β-glucan. The composition of pretreated SBB mainly consisted of xylose and arabinose content. Purified Xyl10 C showed optimum xylanase activity in the pH range 7.0-8.0 and at a temperature of 50-60 °C, while Xyl10E was active at a wider pH range (5.0-10.0) and at 50 °C. The residual activities of Xyl10 C and Xyl10E after 8 h of incubation at 40 °C were 85% and 70%, respectively. The enzymatic activity of Xyl10 C increased to 115% in the presence of 5 M NaCl, was only inhibited in the presence of 0.5% sodium dodecyl sulfate (SDS), and decreased with β-mercaptoethanol. The xylanase and glucanase activities of Xyl10E were inhibited only in the presence of MnSO4, NaCl, and SDS. The main hydrolysis enzymatic product of Xyl10 C and Xyl10E on pretreated SBB was xylobiose. In addition, the xylo-oligosaccharides produced by xylanase Xyl10E on pretreated SBB demonstrated promising antioxidant activity. Thus, the hydrolysis products using Xyl10E on pretreated SBB indicate potential for antioxidant activity and other valuable industrial applications. KEY POINTS: • Two novel GH10 xylanases from the termite gut microbiome were characterized. • Xylo-oligosaccharides obtained from sorghum bagasse exhibited antioxidant potential. • Both enzymes and their hydrolysis product have potential to add value to agro-waste.

A novel FadL family outer membrane transporter is involved in the uptake of polycyclic aromatic hydrocarbons

Gram-negative bacteria play a pivotal role in the bioremediation of persistent organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs). Because the outer membrane (OM) of these bacteria hinders the direct permeation of hydrophobic substances into the cells, trans-OM proteins are required for the uptake of PAHs. However, neither the characteristics of PAH transporters nor the specific transport mechanism has been well interpreted. In this study, we revealed the participation of a novel FadL family transporter, PadL, in the biodegradation of the representative PAH phenanthrene in Novosphingobium pentaromativorans US6-1, an efficient PAH-degrading bacterium. PadL facilitates the cross-OM transport of phenanthrene, thus upregulating the expression of the gene ahdA1e that is critical to the PAH catabolism. We then showed that hydrophobic amino acid residues in the substrate binding pockets of PadL are essential for the binding of PAHs, such as phenanthrene and benzo[a]pyrene. PadL homologs commonly exist in most of the PAH-degrading species from Sphingomonas and Novosphingobium. The characterization of PadL provided in this study holds significant potential for improving the PAH biodegradation efficiency.

IMPORTANCE

Persistent organic pollutants, including polycyclic aromatic hydrocarbons (PAHs), pose serious threats to human health, and biodegradation has been applied as an efficient strategy for PAH removal. However, due to the high hydrophobicity of PAHs, their uptake is hindered by the bacterial outer membrane, restraining degradation efficiency. The present study reveals the critical roles of a novel FadL family protein (PadL) in the biodegradation of PAHs. PadL specifically transports PAHs such as phenanthrene and benzo[a]pyrene and PadL homologs generally exist in PAH-degrading bacteria of Sphingomonas and Novosphingobium. Our findings fill the knowledge gap in the bacterial trans-membrane uptake process of PAHs and provide a future direction for enhancing the bacterial PAH bioremediation capacity.

Advancing protein structure prediction beyond AlphaFold2

Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins with limited evolutionary data or complex molecular interactions. This review explores efforts to enhance AlphaFold2's performance through advanced sequence search techniques and alternative approaches, including protein language models and frameworks that integrate diverse biomolecular interactions. We propose that future progress will depend on developing models grounded in fundamental physicochemical principles, offering more accurate and comprehensive predictions across a wider spectrum of biological systems.

Physical-aware model accuracy estimation for protein complex using deep learning method

With the breakthrough of AlphaFold2 on monomers, the research focus of structure prediction has shifted to protein complexes, driving the continued development of new methods for multimer structure prediction. Therefore, it is crucial to accurately estimate quality scores for the multimer model independent of the used prediction methods. In this work, we propose a physical-aware deep learning method, DeepUMQA-PA, to evaluate the residue-wise quality of protein complex models. Given the input protein complex model, the residue-based contact area and orientation features were first constructed using Voronoi tessellation, representing the potential physical interactions and hydrophobic properties. Then, the relationship between local residues and the overall complex topology as well as the inter-residue evolutionary information are characterized by geometry-based features, protein language model embedding representation, and knowledge-based statistical potential features. Finally, these features are fed into a fused network architecture employing equivalent graph neural network and ResNet network to estimate residue-wise model accuracy. Experimental results on the CASP15 test set demonstrate that our method outperforms the state-of-the-art method DeepUMQA3 by 3.69 % and 3.49 % on Pearson and Spearman, respectively. Notably, our method achieved 16.8 % and 15.5 % improvement in Pearson and Spearman, respectively, for the evaluation of nanobody-antigens. In addition, DeepUMQA-PA achieved better MAE scores than AlphaFold-Multimer and AlphaFold3 self-assessment methods on 43 % and 50 % of the targets, respectively. All these results suggest that physical-aware information based on the area and orientation of atom-atom and atom-solvent contacts has the potential to capture sequence-structure-quality relationships of proteins, especially in the case of flexible proteins. The DeepUMQA-PA server is freely available at http://zhanglab-bioinf.com/DeepUMQA-PA/.

Insights into Nitrogen-Associated Protein 50 (NAP50) as a Tyrosyl-DNA Phosphodiesterase in Dinoflagellates

Nitrogen-associated protein 50 (NAP50) is an abundant plastid protein with an unknown function identified in Alexandrium affine (Dinophyceae). No progress has been made in discovering the function of NAP50 since its first characterization in 2009. The present study is a continuation of work on the predicted function of NAP50. The results show that the NAP50 gene lacks introns but contains abundant base substitutions, consistent with the characteristics of dinoflagellate nuclear genes. The NAP50 protein is found to be widely expressed in dinoflagellate lineages through bioinformatics analysis and Western blotting, suggesting that NAP50 is not exclusive to Alexandrium, which differs from previous understandings. Phylogenetic analysis reveals that NAP50 belongs to the tyrosyl-DNA phosphodiesterase (TDP) family; however, it is structurally distinct from the TDP2 that is present in some dinoflagellate species. The three-dimensional structure and biological functions of NAP50 are predicted using deep learning algorithms. Based on evolutionary relationships and functional predictions, NAP50 may play a role in repairing plastid DNA damage and potentially contribute to the transcription of plastid genes in dinoflagellates.

Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.

Computational modeling of mast cell tryptase family informs selective inhibitor development

Mast cell tryptases, a family of serine proteases involved in inflammatory responses and cancer development, present challenges in structural characterization and inhibitor development. We employed state-of-the-art protein structure prediction algorithms to model the three-dimensional structures of tryptases α, β, δ, γ, and ε with high accuracy. Computational docking identified potential substrates and inhibitors, suggesting overlapping yet distinct activities. Tryptases β, δ, and ε were predicted to act on phenolic compounds, with β and ε additionally hydrolyzing cyanides. Tryptase δ may possess unique formyl-CoA dehydrogenase activity. Virtual screening revealed 63 compounds exhibiting strong binding to tryptase β (TPSB2), 12 exceeding the affinity of the known inhibitor. Notably, the top hit (3-chloro-4-methylbenzimidamide) displayed over 10-fold selectivity for tryptase β over other isoforms. Our integrative approach combining protein modeling, functional annotation, and molecular docking provides a framework for characterizing tryptase isoforms and developing selective inhibitors of therapeutic potential in inflammatory and cancer conditions.

Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae

Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches they occupy. H-NS, the histone-like nucleoid structuring protein, is the best-studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs.

IMPORTANCE

New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. A proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.

CLCC1 promotes hepatic neutral lipid flux and nuclear pore complex assembly

Imbalances in lipid storage and secretion lead to the accumulation of hepatocyte lipid droplets (LDs) (i.e., hepatic steatosis). Our understanding of the mechanisms that govern the channeling of hepatocyte neutral lipids towards cytosolic LDs or secreted lipoproteins remains incomplete. Here, we performed a series of CRISPR-Cas9 screens under different metabolic states to uncover mechanisms of hepatic neutral lipid flux. Clustering of chemical-genetic interactions identified CLIC-like chloride channel 1 (CLCC1) as a critical regulator of neutral lipid storage and secretion. Loss of CLCC1 resulted in the buildup of large LDs in hepatoma cells and knockout in mice caused liver steatosis. Remarkably, the LDs are in the lumen of the ER and exhibit properties of lipoproteins, indicating a profound shift in neutral lipid flux. Finally, remote homology searches identified a domain in CLCC1 that is homologous to yeast Brl1p and Brr6p, factors that promote the fusion of the inner and outer nuclear envelopes during nuclear pore complex assembly. Loss of CLCC1 lead to extensive nuclear membrane herniations, consistent with impaired nuclear pore complex assembly. Thus, we identify CLCC1 as the human Brl1p/Brr6p homolog and propose that CLCC1-mediated membrane remodeling promotes hepatic neutral lipid flux and nuclear pore complex assembly.

Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae

Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches it occupies. H-NS, the histone-like nucleoid structuring protein, is the best studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified a few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs.

Importance

New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. Proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.

"It's Only a Model": When Protein Structure Predictions Need Experimental Validation, the Case of the HTLV-1 Tax Protein

Human T-cell Leukemia Virus type 1 (HTLV-1) is a human retrovirus responsible for leukaemia in 5 to 10% of infected individuals. Among the viral proteins, Tax has been described as directly involved in virus-induced leukemogenesis. Tax is therefore an interesting therapeutic target. However, its 3D structure is still unknown and this hampers the development of drug-design-based therapeutic strategies. Several algorithms are available that can be used to predict the structure of proteins, particularly with the recent appearance of artificial intelligence (AI)-driven pipelines. Here, we review how the structure of Tax is predicted by several algorithms using distinct modelling strategies. We discuss the consequences for the understanding of Tax structure/function relationship, and more generally for the use of structure models for modular and/or flexible proteins, which are frequent in retroviruses.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Electrochemical and structural characterization of recombinant respiratory proteins of the acidophilic iron oxidizer Ferrovum sp. PN-J47-F6 suggests adaptations to the acidic pH at protein level

The tendency of the periplasmic redox proteins in acidophiles to have more positive redox potentials (Em) than their homologous counterparts in neutrophiles suggests an adaptation to acidic pH at protein level, since thermodynamics of electron transfer processes are also affected by acidic pH. Since this conclusion is mainly based on the electrochemical characterization of redox proteins from extreme acidophiles of the genus Acidithiobacillus, we aimed to characterize three recombinant redox proteins of the more moderate acidophile Ferrovum sp. PN-J47-F6. We applied protein film voltammetry and linear sweep voltammetry coupled to UV/Vis spectroscopy to characterize the redox behavior of HiPIP-41, CytC-18, and CytC-78, respectively. The Em-values of HiPIP-41 (571 ± 16 mV), CytC-18 (276 ± 8 mV, 416 ± 2 mV), and CytC-78 (308 ± 7 mV, 399 ± 7 mV) were indeed more positive than those of homologous redox proteins in neutrophiles. Moreover, our findings suggest that the adaptation of redox proteins with respect to their Em occurs more gradually in response to the pH, since there are also differences between moderate and more extreme acidophiles. In order to address structure function correlations in these redox proteins with respect to structural features affecting the Em, we conducted a comparative structural analysis of the Ferrovum-derived redox proteins and homologs of Acidithiobacillus spp. and neutrophilic proteobacteria. Hydrophobic contacts in the redox cofactor binding pockets resulting in a low solvent accessibility appear to be the major factor contributing to the more positive Em-values in acidophile-derived redox proteins. While additional cysteines in HiPIPs of acidophiles might increase the effective shielding of the [4Fe-4S]-cofactor, the tight shielding of the heme centers in acidophile-derived cytochromes is achieved by a drastic increase in hydrophobic contacts (A.f. Cyc41), and by a larger fraction of aromatic residues in the binding pockets (CytC-18, CytC-78).

Expression and Purification of Cp3GT: Structural Analysis and Modeling of a Key Plant Flavonol-3-O Glucosyltransferase from Citrus paradisi

Glycosyltransferases (GTs) are pivotal enzymes in the biosynthesis of various biological molecules. This study focuses on the scale-up, expression, and purification of a plant flavonol-specific 3-O glucosyltransferase (Cp3GT), a key enzyme from Citrus paradisi, for structural analysis and modeling. The challenges associated with recombinant protein production in Pichia pastoris, such as proteolytic degradation, were addressed through the optimization of culture conditions and purification processes. The purification strategy employed affinity, anion exchange, and size exclusion chromatography, leading to greater than 95% homogeneity for Cp3GT. In silico modeling, using D-I-TASSER and COFACTOR integrated with the AlphaFold2 pipeline, provided insights into the structural dynamics of Cp3GT and its ligand binding sites, offering predictions for enzyme-substrate interactions. These models were compared to experimentally derived structures, enhancing understanding of the enzyme's functional mechanisms. The findings present a comprehensive approach to produce a highly purified Cp3GT which is suitable for crystallographic studies and to shed light on the structural basis of flavonol specificity in plant GTs. The significant implications of these results for synthetic biology and enzyme engineering in pharmaceutical applications are also considered.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

WIV, a protein domain found in a wide number of arthropod viruses, which probably facilitates infection

The most powerful approach to detect distant homologues of a protein is based on structure prediction and comparison. Yet this approach is still inapplicable to many viral proteins. Therefore, we applied a powerful sequence-based procedure to identify distant homologues of viral proteins. It relies on three principles: (1) traces of sequence similarity can persist beyond the significance cutoff of homology detection programmes; (2) candidate homologues can be identified among proteins with weak sequence similarity to the query by using 'contextual' information, e.g. taxonomy or type of host infected; (3) these candidate homologues can be validated using highly sensitive profile-profile comparison. As a test case, this approach was applied to a protein without known homologues, encoded by ORF4 of Lake Sinai viruses (which infect bees). We discovered that the ORF4 protein contains a domain that has homologues in proteins from >20 taxa of viruses infecting arthropods. We called this domain 'widespread, intriguing, versatile' (WIV), because it is found in proteins with a wide variety of functions and within varied domain contexts. For example, WIV is found in the NSs protein of tospoviruses, a global threat to food security, which infect plants as well as their arthropod vectors; in the RNA2 ORF1-encoded protein of chronic bee paralysis virus, a widespread virus of bees; and in various proteins of cypoviruses, which infect the silkworm Bombyx mori. Structural modelling with AlphaFold indicated that the WIV domain has a previously unknown fold, and bibliographical evidence suggests that it facilitates infection of arthropods.

Tertiary structure assessment at CASP15

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.

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function

MOTIVATION

Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures.

RESULTS

Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups.

AVAILABILITY AND IMPLEMENTATION

DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.