Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions

Biophysics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 12, 2025

Machine learning (ML) techniques have been making major impacts on all areas of science and engineering, including biophysics. In this review, we discuss several applications ML to biophysical problems based our recent research. The topics include the use identify hotspot residues in allosteric proteins using deep mutational scanning data analyze how mutations these hotspots perturb co-operativity framework a statistical thermodynamic model, improve accuracy free energy simulations by integrating from different levels potential functions, determine phase transition temperature lipid membranes. Through examples, illustrate unique value extracting patterns or parameters complex sets, as well remaining limitations. By implementing approaches context physically motivated models computational frameworks, are able gain deeper mechanistic understanding better convergence numerical simulations. We conclude briefly discussing introduced can be further expanded tackle more problems.

Language: Английский

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The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 169060 - 169060

Published: March 1, 2025

Language: Английский

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Uncovering the Role of Distal Regions in PDK1 Allosteric Activation

ACS Bio & Med Chem Au, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Allosteric regulation is a pivotal mechanism governing wide array of cellular functions. Essential to this process flexible biomolecule allowing distant sites interact through coordinated or sequential conformational shifts. Phosphoinositide-dependent kinase 1 (PDK1) possesses conserved allosteric binding site, the PIF-pocket, which regulates kinase's ATP binding, catalytic activity, and substrate interactions. We elucidated mechanisms PDK1 by comparing ensembles bound with different small-molecule modulators in PIF-pocket that modulator-free kinase. Analysis over 48 μs simulations consistently shows predominantly influence dynamics specific distal regions from driving activation. Furthermore, recently developed advanced difference contact network community analysis employed elucidate communications. This approach integrates multiple into single network, offering valuable tool for future studies aimed at identifying function-related proteins.

Language: Английский

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SHP2–EGFR States in Dephosphorylation Can Inform Selective SHP2 Inhibitors, Dampening RasGAP Action

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(21), P. 5175 - 5187

Published: May 15, 2024

SHP2 is a positive regulator of the EGFR-dependent Ras/MAPK pathway. It dephosphorylates regulatory phosphorylation site in EGFR that serves as binding to RasGAP (RASA1 or p120RasGAP). RASA1 activated by phosphate group. Active deactivates Ras hydrolyzing Ras-bound GTP GDP. Thus, dephosphorylation effectively prevents RASA1-mediated deactivation Ras, thereby stimulating proliferation. Despite knowledge this vital regulation cell life, mechanistic in-depth structural understanding involvement SHP2, EGFR, and pathway has largely remained elusive. Here we elucidate interactions, factors influencing EGFR's recruitment RASA1, SHP2's recognition substrate EGFR. We reveal specifically interacts with DEpY

Language: Английский

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AlphaFold2 in Molecular Discovery

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(19), P. 5947 - 5949

Published: Oct. 9, 2023

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAlphaFold2 in Molecular DiscoveryAriane Nunes-Alves*Ariane Nunes-AlvesInstitute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany*Ariane Nunes Alves - Institute Germany; email: [email protected]More by Ariane Nunes-Alveshttps://orcid.org/0000-0002-5488-4732 and Kenneth MerzKenneth MerzDepartment Michigan State University, East Lansing 48824, Michigan, United StatesMore Merzhttps://orcid.org/0000-0001-9139-5893Cite this: J. Chem. Inf. Model. 2023, 63, 19, 5947–5949Publication Date (Web):October 9, 2023Publication History Received11 September 2023Published online9 October inissue 9 2023https://doi.org/10.1021/acs.jcim.3c01459Copyright © 2023 American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views2112Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence for given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InReddit (929 KB) Get e-AlertscloseSUBJECTS:Computational modeling,Drug discovery,Homology,Molecular structure,Protein structure e-Alerts

Language: Английский

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Augmented Intelligence for Knowledge Management and Green Education in the Post-COVID-19 Era

Advances in educational technologies and instructional design book series, Journal Year: 2024, Volume and Issue: unknown, P. 47 - 71

Published: June 3, 2024

Augmented intelligence is a paradigm that integrates machine capabilities with human cognition, amplifying cognitive abilities and optimizing decision-making processes. Integrating augmented into knowledge management can significantly improve experiential learning training at lower costs while maintaining higher standards. Before the epidemic, AI-based transformation in acquisition, dissemination, responsiveness began, which expanded rapidly during global COVID-19 pandemic. Consequently, virtual management, online learning, hybrid training, similar approaches have become familiar, quality real-time participation declined due to monotonous static features. Hence, this chapter discusses how redesign green education systems intelligence/reality enhance standards through advanced platforms such as holographic labs, 3D systems, bionic lenses, electroencephalography for future generations.

Language: Английский

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Protein structure and dynamics in the era of integrative structural biology

Frontiers in Biophysics, Journal Year: 2023, Volume and Issue: 1

Published: July 17, 2023

Proteins carry out their biological activity as dynamic structures and populate in solution or membranes structural distributions with different degrees of heterogeneity. The central challenge biology is to capture protein dynamics under equilibrium kinetic conditions shifting from single, static pictures movies conformational ensembles. Ideally, this task should be pursued both vitro vivo , the influence native environment. last decade has seen a tremendous development biophysical methods for investigation structure dynamics. However, each method specific limitations no single approach offers such complex level description. Nonetheless, combination experimental computational, complementary opening promising new avenues. Also ambition implementing studies on an “omic” scale becoming more realistic. In spite still major limitations, integrative bringing into proteomics, exciting perspectives basic applied sciences.

Language: Английский

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Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application

Molecular Biotechnology, Journal Year: 2023, Volume and Issue: 66(12), P. 3385 - 3397

Published: Nov. 13, 2023

Language: Английский

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Exploring the conformational ensembles of protein-protein complex with transformer-based generative model

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 26, 2024

Protein-protein interactions are the basis of many protein functions, and understanding contact conformational changes protein-protein is crucial for linking structure to biological function. Although difficult detect experimentally, molecular dynamics (MD) simulations widely used study ensembles complexes, but there significant limitations in sampling efficiency computational costs. In this study, a generative neural network was trained on complex conformations obtained from directly generate novel with physical realism. We demonstrated use deep learning model based transformer architecture explore complexes through MD simulations. The results showed that learned latent space can be unsampled obtaining new complementing pre-existing ones, which as an exploratory tool analysis enhancement complexes.

Language: Английский

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CNS Drug Discovery in Academia: Where Basic Research Meets Innovation

ChemBioChem, Journal Year: 2024, Volume and Issue: unknown

Published: July 3, 2024

Abstract The involvement of academic research in drug discovery is consistently growing. However, projects seldom advance to clinical trials. Here, we assess the landscape within National Centre Competence Research (NCCR) TransCure launched by Swiss Science Foundation foster basic and early‐stage on membrane transporters. This included transporters central nervous system (CNS) disorders, which represent a huge unmet medical need. While idea championship, sustainable funding, collaborations between disciplines at interface academia industry are important for translational research, Popperian falsifiability, strong intellectual property motivated startup team key elements innovation. exemplified NCCR spin‐off company Synendos Therapeutics, stage biotech developing first selective endocannabinoid reuptake inhibitors (SERIs) as novel treatment neuropsychiatric disorders. We provide perspective challenges related entering an uncharted druggable space bridging often mentioned “valley death”. high attrition rate CNS field due lack meaningful animal models that can pharmacological proof‐of‐concept potentially disruptive technologies earliest stages, absence solid property.

Language: Английский

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