Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning DOI
Swarnadeep Seth, Aniket Bhattacharya

Biomacromolecules, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). used ∼6500 IDP sequences from MobiDB database length 20–300 to obtain gyration radii BD on coarse-grained single-bead amino acid model (HPS2 model) us others [Dignon, G. L. PLoS Comput. Biol. 2018, 14, e1005941,Tesei, Proc. Natl. Acad. Sci. U.S.A. 2021, 118, e2111696118,Seth, S. J. Chem. Phys. 2024, 160, 014902] generate training sets DL algorithm. Using ⟨Rg⟩ simulated IDPs as set, we develop multilayer perceptron neural net (NN) architecture that predicts 33 previously studied using with 97% accuracy sequence corresponding parameters HPS model. now utilize this NN predict every permutation IDPs. Our approach successfully identifies mutation-prone regions induce significant alterations radius when compared wild-type sequence. further validate prediction running simulations subset identified mutants. The network yields (104–106)-fold faster computation search space potentially harmful mutations. findings have substantial implications understanding diseases related development potential therapeutic interventions. method can be extended accurate predictions other mutation effects proteins.

Language: Английский

Structured protein domains enter the spotlight: modulators of biomolecular condensate form and function DOI Creative Commons

Nathaniel Hess,

Jerelle A. Joseph

Trends in Biochemical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

3
Frontiers in integrative structural modeling of macromolecular assemblies DOI Creative Commons
Kartik Majila, Shreyas Arvindekar,

M. Jindal

et al.

QRB Discovery, Journal Year: 2025, Volume and Issue: 6

Published: Jan. 1, 2025

Abstract Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based prediction cryo electron-microscopy have sparked renewed enthusiasm integrative modeling; structures methods can be integrated situ maps to characterize assemblies. This approach previously allowed us others determine the architectures of diverse assemblies, such as nuclear pore complexes, chromatin remodelers, cell–cell junctions. Experimental spanning several scales was used these studies, ranging high-resolution data, X-ray crystallography AlphaFold structure, low-resolution cryo-electron tomography co-immunoprecipitation experiments. Two recurrent challenges emerged across a range studies. First, contained significant fractions disordered regions, necessitating development new regions context ordered regions. Second, needed developed utilize information tomography, timely challenge structural biology is increasingly moving towards characterization. Here, we recapitulate recent developments proteins analysis highlight other opportunities method modeling.

Language: Английский

Citations

0
Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning DOI
Swarnadeep Seth, Aniket Bhattacharya

Biomacromolecules, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). used ∼6500 IDP sequences from MobiDB database length 20–300 to obtain gyration radii BD on coarse-grained single-bead amino acid model (HPS2 model) us others [Dignon, G. L. PLoS Comput. Biol. 2018, 14, e1005941,Tesei, Proc. Natl. Acad. Sci. U.S.A. 2021, 118, e2111696118,Seth, S. J. Chem. Phys. 2024, 160, 014902] generate training sets DL algorithm. Using ⟨Rg⟩ simulated IDPs as set, we develop multilayer perceptron neural net (NN) architecture that predicts 33 previously studied using with 97% accuracy sequence corresponding parameters HPS model. now utilize this NN predict every permutation IDPs. Our approach successfully identifies mutation-prone regions induce significant alterations radius when compared wild-type sequence. further validate prediction running simulations subset identified mutants. The network yields (104–106)-fold faster computation search space potentially harmful mutations. findings have substantial implications understanding diseases related development potential therapeutic interventions. method can be extended accurate predictions other mutation effects proteins.

Language: Английский

Citations

0