Proteins-Structure Function and Bioinformatics最新文献

{"title":"3D-ΔΔG: A Dual-Channel Prediction Model for Protein-Protein Binding Affinity Changes Following Mutation Based on Protein 3D Structures.","authors":"Yuxiang Wang, Yibo Zhu, Xiumin Shi, Lu Wang","doi":"10.1002/prot.26837","DOIUrl":"10.1002/prot.26837","url":null,"abstract":"<p><p>Protein-protein interactions are crucial for cellular regulation, antigen-antibody interactions, and other vital processes within living organisms. However, mutations in amino acid residues have the potential to induce changes in protein-protein binding affinity (ΔΔG), which may contribute to the onset and progression of disease. Existing methods for predicting ΔΔG use either protein sequence information or structural data. Furthermore, some methods are only applicable to single-point mutation cases. To address these limitations, we introduce a ΔΔG predictor that can handle complex scenarios involving multipoint mutations. In this investigation, a dual-channel deep learning model three-dimensional (3D)-ΔΔG is introduced, which is designed to predict ΔΔG by combining mutation information from side chain sequences and 3D structures. The proposed model employs a pre-trained protein language model to encode the side-chain amino acid sequence. A graph attention network is deployed to handle the graph representation of proteins simultaneously. Finally, a dual-channel processing module is implemented to facilitate depth fusion and extraction of both sequence and structural features. The model effectively captures the intricate alterations occurring pre- and post-protein mutation by integrating both sequence and 3D structural information. Results on the single-point mutation data set demonstrate a substantial improvement compared to state-of-the-art models. More significantly, 3D-ΔΔG exhibits superior performance when evaluated on the mixed mutation data sets, SKEMPIv1 and SKEMPIv2. The high level of agreement between the computationally predicted ΔΔG values and the experimentally determined values illustrates the potential of the 3D-ΔΔG model as an effective pre-screening tool in protein design and engineering.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"1688-1700"},"PeriodicalIF":2.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Elucidation of Possible Contributors to Formation and Stabilization of ATP-Lid Down-Conformation in the N-Terminal Domain of Hsp90.","authors":"Keigo Gohda","doi":"10.1002/prot.26849","DOIUrl":"10.1002/prot.26849","url":null,"abstract":"<p><p>Heat shock protein 90 (Hsp90) controls activation and maturation of various crucial client proteins through a catalytic cycle. In this catalytic cycle, closure of the lid segment from up- to down-conformation in the N-terminal domain (NTD) of Hsp90 through ATP binding is indispensable for coordinated structural changes, including interchange of dimeric Hsp90 structure between open and closed forms. However, the mechanisms underlying lid closure remain unclear. In this study, we investigate structural characteristics of the lid-down conformation in an isolated monomeric NTD structure by two types of molecular-dynamic simulation: a flopping-down simulation for a lid-up conformation using repulsive distance-restraints, and a down-conformation simulation for in silico H1-mutants of NTD with a lid-down conformation. In the flopping-down simulation, spontaneous formation of a lid-down conformation is observed multiple times. K98 and K102 in the lid segment are observed to interact with ATP phosphate or D40, suggesting that they contribute to the formation of the lid-down conformation. In the down-conformation simulation, the H1 structure of the chimera H1-model, which only retains a proper down-conformation among the models for the entire simulation period, covers the lid segment more than that of the X-ray structure. Because the stability of the lid-down conformation was influenced by H1 structures, the H1 segment is suggested to contribute to stabilization of the lid-down conformation. Although no direct experimental data are currently available to confirm these findings, these simulation results do not show large discrepancies with the experimental data and evidence of structural characteristics of the NTD, deduced from previous X-ray and spectroscopic studies.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"1862-1875"},"PeriodicalIF":2.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of Extracellular Histidine Residues for the Function and pH Sensitivity of Human Organic Anion Transporting Polypeptide 1B3.","authors":"Wanjun Han, Han Liu, Ting Liang, Lanjing Li, Ru Huan, Chunshan Gui","doi":"10.1002/prot.26851","DOIUrl":"10.1002/prot.26851","url":null,"abstract":"<p><p>Organic anion transporting polypeptide 1B3 (OATP1B3) is a liver-specific transporter that mediates uptake of various substances from blood into hepatocytes. The transport function of OATP1B3 was shown to be pH-sensitive. As the protonation state of extracellular histidine residues can be affected by the environmental pH, in the present study, the role of 7 extracellular histidine residues in the function and pH sensitivity of OATP1B3 has been examined. Our results showed that H115 had the most significant effect on the function of OATP1B3. The Cryo-EM structure of OATP1B3 indicated that H115 is involved in the binding and release of bicarbonate during a transport cycle. Functional studies on H115 mutants suggested that a hydrogen-bond forming group was preferred over a positively charged group at site 115, indicating that a hydrogen bond is optimum for bicarbonate's binding/release cycle. This may also explain why OATP1B3 showed lower transport function at pH 4.5 than at pH 7.4, as H115 is positively charged at pH 4.5 but neutral at pH 7.4. In addition, the H115A mutation largely compromised the pH sensitivity of OATP1B3, probably due to the loss of its protonation state switching capability. Taken together, H115 plays an important role in the function and pH sensitivity of OATP1B3.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"1876-1885"},"PeriodicalIF":2.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Dynamics Analysis of Inhibitor Binding Interactions in the Vibrio cholerae Respiratory Complex NQR.","authors":"Joseph A DePaolo-Boisvert, Karina Tuz, David D L Minh, Oscar X Juarez","doi":"10.1002/prot.70036","DOIUrl":"10.1002/prot.70036","url":null,"abstract":"<p><p>The sodium-pumping ubiquinone oxidoreductase sodium pumping quinone reductase (NQR) is an important enzyme in the respiratory chain of multiple pathogenic gram-negative bacteria. NQR has been proposed as a viable antibiotic target due to its importance in supporting energy-consuming reactions and its absence in human cells. In this study, molecular dynamics simulations were conducted to characterize the interactions between the ubiquinone binding pocket of Vibrio cholerae NQR with its substrate analogue ubiquinone-4 and three potent inhibitors: HQNO, aurachin-D42, and korormicin-A. Through interaction fingerprinting, distance calculations, and clustering analysis, important binding motifs for each of these ligands were identified. Subunit B residues K54, F137, E144, V145, V155, E157, G158, F159, and F160 were frequently identified as establishing either hydrogen bonding interactions or hydrophobic interactions with these three ligands. The findings of this in silico study are interpreted in view of mutagenesis analyses previously published in the literature. The elucidation of important binding interactions associated with the inhibitors is critical as it informs structure-activity relationships, which are essential for the development of novel antibiotics targeting NQR.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Basis for M2-2-MAVS Proteins Interaction in Human Metapneumovirus (HMPV): Exploring the Immune Evasion Mechanism Through Biomolecular Modeling, Structural Mutagenesis and Classical Simulations.","authors":"Fahad M Alshabrmi, Eid A Alatawi","doi":"10.1002/prot.70057","DOIUrl":"https://doi.org/10.1002/prot.70057","url":null,"abstract":"<p><p>Human metapneumovirus (HMPV) was first discovered in the Netherlands in 2001 and is now considered one of the most important contributors to viral respiratory diseases. It is often asymptomatic in healthy adults but can cause serious illness among immunocompromised or older patients. In response to the infection, the viral immune evasion mechanism remains a key approach for evading the immune response. In hMPV, the M2-2 protein interacts with the hMAVS protein to evade the immune response. It is essential to understand how the mechanism takes place for designing potential therapeutic agents. Thus, herein, we provide structural mechanisms of the interaction between M2-2 and MAVS through biomolecular interactions, in silico alanine scanning, and classical simulation approaches (repeated). We selected the HADDOCK-generated complex from the docking results, leaving the others from ZDOCK, Cluspro, and PyDOCK. Using alanine scanning, 18 interface residues were identified consensually, among which 8 residues, P29A, E30A, M31A, W33A, E37A, Q39A, E40A, and K48A, significantly affected the binding and were selected for the subsequent analysis. The docking results of these alanine mutants reported a significant reduction in the HADDOCK score, electrostatic energies, and vdW forces. Moreover, the stability of these mutations has been significantly compromised during simulation, while the total binding free energy also corroborates with the docking scores. From the detailed hydrogen-bond analysis, the interactions were significantly reduced in the mutants' complexes compared to the wild type, suggesting that alanine substitutions weaken the M2-1 and MAVS interaction by disrupting its finely tuned interaction network, highlighting potential vulnerabilities in its binding mechanism. The dissociation constant (K_d) results further validated discrepancies in the binding strength caused by the alanine substitutions. This study provides insights into the immune evasion mechanism of the hMPV virus and provides a basis for therapeutic development.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Single Chains: Benchmarking Macromolecular Complex Prediction Methods With the Continuous Automated Model EvaluatiOn (CAMEO).","authors":"Xavier Robin, Peter Škrinjar, Andrew M Waterhouse, Gabriel Studer, Gerardo Tauriello, Janani Durairaj, Torsten Schwede","doi":"10.1002/prot.70060","DOIUrl":"https://doi.org/10.1002/prot.70060","url":null,"abstract":"<p><p>Independent, blind assessment of structure prediction methods is essential for establishing state-of-the-art performance, identifying limitations, and guiding future developments. The Continuous Automated Model EvaluatiOn (CAMEO) platform provides weekly, automated benchmarking of structure prediction servers, complementing the biennial Critical Assessment of Structure Prediction (CASP) experiments.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.","authors":"Qiqige Wuyun, Quancheng Liu, Wentao Ni, Chunxiang Peng, Ziying Zhang, Xiaogen Zhou, Gang Hu, Lydia Freddolino, Wei Zheng","doi":"10.1002/prot.70059","DOIUrl":"https://doi.org/10.1002/prot.70059","url":null,"abstract":"<p><p>We report the results from the \"MIEnsembles-Server\" and \"Zheng\" groups for structure ensemble predictions in CASP16, both of which employed the EnsembleFold pipeline. Initially, multiple sequence alignments (MSAs) were generated using DeepMSA2 for proteins and rMSA for RNA targets. These MSAs were processed by newly developed deep learning methods-D-I-TASSER2 for protein monomer structure prediction, DMFold2 for protein complex structure prediction, ExFold for RNA structure prediction, and DeepProtNA for protein-nucleic acid complex structure prediction-to yield diverse structural decoys. The generated decoys were clustered into representative models corresponding to distinct conformational states using the structural clustering tool MolClust. Protein monomer targets underwent additional refinement via replica-exchange Monte Carlo (REMC) simulations with D-I-TASSER2, and these refined decoys were re-clustered with MolClust to finalize the ensemble predictions. For the 19 ensemble targets in CASP16, the final EnsembleFold models achieved an average TM-score of 0.657, representing improvements of 10.2% compared to the baseline AlphaFold3 program. Notably, EnsembleFold achieved particularly good performance for hybrid protein/nucleic-acid targets, leading to its efficacy in ensemble prediction tasks. Analysis of the resulting structural ensembles highlighted three significant insights: (i) Models derived from distinct DeepMSA2-generated MSAs typically represent different conformational states for ensemble targets; (ii) REMC simulations significantly enhance model diversity, facilitating the identification of alternative conformations; (iii) The structural clustering approach effectively identifies and selects accurate representative models for each conformational state. We further discuss potential improvements in Quality Assessment (QA) scoring methods that could further enhance the reliability and accuracy of ensemble predictions in the future.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iterative Modeling via Structural Diffusion (IMSD): Exploring Fold-Switching Pathways in Metamorphic Proteins Using AlphaFold2-Based Generative Diffusion Model UFConf.","authors":"Dmitrii A Luzik, Nikolai R Skrynnikov","doi":"10.1002/prot.70050","DOIUrl":"https://doi.org/10.1002/prot.70050","url":null,"abstract":"<p><p>Metamorphic proteins (MPs) can fold into two or more distinct spatial structures. Increasing interest in MPs has spurred the search for computational tools to predict proteins fold-switching potential and model their refolding pathways. Here we address this problem by using the recently reported generative diffusion predictor UFConf, based on the AlphaFold2 network. We have developed a new UFConf-driven algorithm dubbed IMSD (iterative modeling via structural diffusion) to model the MP's path from one conformational state to another. In brief, we begin with the experimental structure of state A, perturb it through the \"noising\" process, and infer a number of models (replicas) through the reverse diffusion or \"denoising\" process. From this set of models, we choose the one that is closest to the alternative structure B; then we use it as a starting point to perform another round of noising/denoising and thus generate the next batch of replicas. Repeating this process in an iterative fashion, we have been able to map the entire path from state A to state B for metamorphic proteins GA98, SA1 V90T, and the C-terminal domain of RfaH. The obtained representation of the fold-switching pathways in these MPs is consistent with the dual-funnel energy landscape observed in the previous modeling studies and shows good agreement with the available experimental data. The new UFConf-based IMSD protocol can be viewed as a part of the emerging generation of modeling tools aiming to model protein dynamics by means of deep learning technology.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Docking With Rosetta and Deep Learning Approaches in CAPRI Rounds 47-55.","authors":"Ameya Harmalkar, Lee-Shin Chu, Samuel W Canner, Rituparna Samanta, Rahel Frick, Fatima A Davila-Hernandez, Sudeep Sarma, Fatima Hitawala, Jeffrey J Gray","doi":"10.1002/prot.70016","DOIUrl":"10.1002/prot.70016","url":null,"abstract":"<p><p>Critical Assessment of PRediction of Interactions (CAPRI) rounds 47 through 55 introduced 49 targets comprising multistage assemblies, antibody-antigen complexes, and flexible interfaces. For these rounds, we combined various Rosetta docking approaches (RosettaDock, ReplicaDock, and SymDock) with deep learning approaches (AlphaFold2, IgFold, and AlphaRED). Since prior CAPRI rounds, we have developed methods to better capture conformational changes, updated our scoring function, and integrated structure prediction tools such as AlphaFold2 in our docking routines. Here, we highlight several notable CAPRI targets and address the major challenges in the blind prediction of protein-protein interactions, including binding-induced conformational changes, large multimeric proteins, and antibody-antigen interactions. Although predictors have achieved modest improvements in accuracy for simpler targets post-AlphaFold2, performance for more flexible complexes remains limited. We employed RosettaDock 4.0, ReplicaDock 2.0, and AlphaRED to enhance backbone conformational sampling for flexible complexes. Our docking routines improved the DockQ score (0.77 vs. 0.62 for AF2-multimer) for a GP2 bacteriophage protein (T194), effectively capturing binding-induced conformational changes. Additionally, we introduce a fold-and-dock approach for predicting the assembly of a surface-layer SAP protein derived from Bacillus anthracis (T160), a large hetero-multimer comprising six distinct sub-units. For large symmetric complexes, we used Rosetta-based SymDock 2.0, successfully predicting a human DNA repair protein complex with A10 stoichiometry (T230) with high CAPRI-quality ranking. We also address the challenges in modeling antibody/nanobody-antigen interactions, particularly through the integration of deep learning tools and docking methods. Despite advances with tools like IgFold and AlphaFold2, accurately predicting CDR H3 loops and antibody-antigen binding interfaces remains challenging. Combining ReplicaDock 2.0 with deep learning highlights these difficulties and underscores the need for extensive sampling and CDR-focused strategies to improve prediction accuracy.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metal-Coordination Specificity and Structural Dynamics of C. elegans Metallothionein I: Insights From 3D Modeling and MD Simulations.","authors":"Nilvea Ramalho de Oliveira, Andrei Santos Siqueira, Paulo Sérgio Alves Bueno, Evonnildo Costa Gonçalves, Juliano Zanette","doi":"10.1002/prot.70054","DOIUrl":"https://doi.org/10.1002/prot.70054","url":null,"abstract":"<p><p>Metallothioneins (MTLs) are small, cysteine-rich proteins known for their ability to bind metal ions and exhibit flexible, disordered structures. The structural and functional characteristics of metallothionein I (MTL-1) from Caenorhabditis elegans were investigated, focusing on its behavior in both metal free (MTL-1 Apo) and metal-bond states with Zn²⁺, Cd²⁺, Cu²⁺, Hg²⁺, and Pb²⁺ divalent metal ions. Using molecular dynamics simulations and 3D modeling via AlphaFold, we characterized the flexibility and stability of MTL. The MTL-1 Apo form displayed high flexibility, aligning with its intrinsically disordered protein (IDP) nature, with 89.3% of its structure composed of coils, bends, and turns. Metal binding significantly enhanced the protein's stability, particularly with Zn²⁺, Cd²⁺, Cu²⁺, and Hg²⁺, reducing root mean square deviation (RMSD), root mean square fluctuation (RMSF), accessible surface area (SASA) and radius of gyration (R_g) values, indicating structural compaction. Conversely, Pb²⁺ showed a weaker stabilizing effect, with a more dynamic and less stable structure. Structural analysis revealed that conserved cysteine residues coordinate the metal through strong thiolate interactions, with additional contributions from non-cysteine residues, such as Glu and Lys. The study underscores the importance of incorporating intrinsically disordered protein models in MD simulations to provide deeper insights into how metallothionein's flexibility and stability vary in response to different metal ions, offering a structural perspective on their biological interactions and behavior under diverse environmental conditions. While thermodynamic aspects were not directly assessed, the results reveal consistent conformation trends across different metal coordination states.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}