Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset

Protein Science, Journal Year: 2023, Volume and Issue: 33(1)

Published: Dec. 12, 2023

Insight into how mutations affect protein stability is crucial for engineering, understanding genetic diseases, and exploring evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on stability. Nevertheless, comparing these poses challenges due variations in their training data. Moreover, it observed that they tend perform better at predicting destabilizing than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, ProThermDB. This dataset, which does not overlap with well-established S2648 consists 4038 single-point mutations, including over 1000 mutations. We assessed using 27 methods, latest ones utilizing mega-scale datasets transfer learning. excluded entries or similarity ensure fairness. Pearson correlation coefficients tested tools ranged 0.20 0.53 unseen data, none could accurately even those performing well anti-symmetric property analysis. While most present consistent trends across various properties such as solvent exposure secondary conformation, do exhibit clear pattern. Our study also suggests solely addressing bias may significantly enhance accuracy These findings emphasize importance developing precise predictive

Language: Английский

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AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(5), P. 1657 - 1681

Published: Feb. 19, 2024

The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution functional hotspots that work synchronously to balance requirements for productive receptor binding efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations mutational profiling energetics stability prediction comprehensive analysis the structure, dynamics, BA.2.86 spike variant ACE2 host distinct classes antibodies. We adapted several AlphaFold2 predict both structure conformational ensembles protein in complex receptor. results showed AlphaFold2-predicted ensemble can accurately capture main states variant. Complementary predictions, microsecond molecular dynamics reveal details landscape produced equilibrium structures are used perform scanning residues characterize energy hotspots. ensemble-based domain BA.2 complexes revealed group conserved hydrophobic critical variant-specific contributions R403K, F486P, R493Q. To examine evasion properties detail, performed structure-based interfaces antibodies significantly reduced neutralization against basis compensatory effects hotspots, showing lineage may have evolved outcompete other subvariants by improving while preserving affinity via effect R493Q F486P This study demonstrated an integrative approach combining predictions complementary robust enable accurate characterization mechanisms newly emerging variants.

Language: Английский

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Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA

Genes, Journal Year: 2025, Volume and Issue: 16(1), P. 101 - 101

Published: Jan. 19, 2025

Background/Objectives: Predicting the effects of protein and DNA mutations on binding free energy protein–DNA complexes is crucial for understanding how variants impact wild-type cellular function. As many interactions involve binding, accurately predicting changes in (ΔΔG) valuable distinguishing pathogenic from benign ones. Methods: This study describes development optimization SAMPDI-3Dv2 machine learning method, which trained an expanded database experimentally measured ΔΔGs. enhanced model incorporates new features, including 3D structure mutant protein, features structure, a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation. Results: The updated SAMPDI-3D (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) 0.68 0.80 mutations. These results represent significant improvements over existing tools. Additionally, method’s rapid execution time enables genome-scale predictions. Conclusions: improved shows predictive performance analyzing complexes. By leveraging structural information training dataset, provides researchers with more accurate efficient tool mutation analysis, contributing to identifying improving our

Language: Английский

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A computational approach to predict the effects of missense mutations on protein amyloidogenicity: A case study in hereditary transthyretin cardiomyopathy

Journal of Structural Biology, Journal Year: 2025, Volume and Issue: 217(1), P. 108176 - 108176

Published: Feb. 9, 2025

Language: Английский

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Computational prediction of deleterious nonsynonymous SNPs in the CTNS gene: implications for cystinosis

BMC Genomic Data, Journal Year: 2025, Volume and Issue: 26(1)

Published: May 15, 2025

Language: Английский

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Quantification and structure-function analysis of calpain-1 and calpain-2 protease subunit interactions

Journal of Biological Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 110243 - 110243

Published: May 1, 2025

Language: Английский

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Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Aug. 28, 2024

Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation important for our understanding structure-function relationship, and also great interest in engineering pharmaceutical design.

Language: Английский

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SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein–protein complexes

Bioinformatics, Journal Year: 2024, Volume and Issue: 40(9)

Published: Sept. 1, 2024

Abstract Motivation Mutations in protein–protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given abundance of membrane proteins, it is crucial assess impact mutations on binding affinity these proteins. Although several methods exist predict free energy change due most require structural information protein complex are primarily trained SKEMPI database, which composed mainly soluble Results A novel sequence-based method (SAAMBE-MEM) for predicting changes (ΔΔG) complexes has been developed. This utilized MPAD contains affinities wild-type mutant complexes. machine learning model was developed ΔΔG by leveraging features such as amino acid indices position-specific scoring matrices (PSSM). Through extensive dataset curation feature extraction, SAAMBE-MEM validated using XGBoost regression algorithm. The optimal set, including PSSM-related features, achieved a Pearson correlation coefficient 0.64, outperforming existing database. Furthermore, demonstrated that performs much better when utilizing evolution-based contrast physicochemical features. Availability implementation accessible via web server standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. cleaned database available website.

Language: Английский

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Free energy landscape and thermodynamics properties of novel mutations in PncA of pyrazinamide resistance isolates of Mycobacterium tuberculosis

Journal of Biomolecular Structure and Dynamics, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 12

Published: Oct. 14, 2023

AbstractPyrazinamide (PZA) is one of the first-line antituberculosis therapy, active against non-replicating Mycobacterium tuberculosis (Mtb). The conversion PZA into pyrazinoic acid (POA), form, required activity pncA gene product pyrazinamidase (PZase) activity. Mutations occurred in are primary cause behind resistance. However, resistance mechanism important to explore using high throughput computational approaches. Here we aimed novel P62T, L120R, and V130M mutations PZase 200 ns molecular dynamics (MD) simulations. MD simulations were performed observe structural changes for these three mutants (MTs) compared wild types (WT). Root means square fluctuation, radius gyration, free energy landscape, root deviation, dynamic cross-correlation motion, pocket volume found variation between WT MTs, revealing effects V130M. conformational landscape MTs differs significantly from system, lowering binding PZA. geometric shape complementarity drug target protein further confirmed that affect structure. These on may vulnerability convert POA.Communicated by Ramaswamy H. SarmaKeywords: Drug resistanceFELmutationsMTBPZAsimulation Author contributionsConceptualization: MTK, DQW. Data curation: ED, EA. Experimental work: ED MTK. Formal analysis: EA, Funding acquisition: DQW.Disclosure statementNo potential conflict interest was reported authors.Additional informationFundingThis work supported grants Key Research Area Grant 2016YFA0501703 Ministry Science Technology China, National Natural Foundation China (Contract no. 61832019, 61503244), State Lab Microbial Metabolism Joint Funds Medical Engineering Scientific at Shanghai Jiao Tong University (YG2017ZD14). A.R. Chaudhry thankful Deanship Bisha, supporting this through Fast-Track Support Program. grateful financial support Institut Universitaire de Faance (IUF). GENCI resources (allocation A0130713808).

Language: Английский

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Phenotypic characterization of point mutations spanning FHA domain and C-terminal region of Dawdle gene in Arabidopsis

Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Journal Year: 2024, Volume and Issue: 15(1), P. 61 - 71

Published: June 4, 2024

The screening analysis of loss-of-function alleles in Arabidopsis thaliana revealed a mutation the At3G20550 gene, called DAWDLE (DDL). DDL gene causes pleiotropic phenotypes and reduced levels several microRNAs. encodes protein with Fork Head-Associated (FHA) domain, found large range proteins significant cellular processes prokaryotes eukaryotes. However, it is not completely known whether FHA domain C-terminal region are necessary for its function. aim this study was to determine function both regions by conducting phenotypic point mutations spanning Targeted Induced Local Lesions IN Genome (Tilling) screen performed Columbia erecta-105 background resulting DDL. mutants were phenotypically characterized. Height plant, hypocotyl root length, fertility measured. Phenotypic analyses ddl varying degrees different organs. Reduction shortening root, stem lengths Tiller mutant lines suggest that may require Arabidopsis. Key words: Dawdle, Domain, Genome, Ethyl Methane Sulfonate,

Language: Английский

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