Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs

Frontiers in Molecular Biosciences, Journal Year: 2025, Volume and Issue: 12

Published: April 8, 2025

Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive struggle sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient scalable conformational sampling. They leverage large-scale datasets learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of in IDPs without constraints physics-based approaches. Such DL approaches have been shown outperform MD generating diverse comparable accuracy. Most models rely primarily on simulated data training experimental serves role validation, aligning generated observable physical biochemical properties. However, challenges remain, including dependence quality, limited interpretability, scalability larger proteins. Hybrid combining AI can bridge gaps integrating statistical thermodynamic feasibility. Future directions include incorporating observables into frameworks refine predictions enhance applicability. AI-driven methods hold significant promise IDP research, offering novel insights protein dynamics therapeutic targeting while overcoming limitations simulations.

Language: Английский

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Intrinsic Disorder in Protein Interaction Networks Linking Cancer with Metabolic Diseases

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 118, P. 108493 - 108493

Published: April 28, 2025

Language: Английский

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PEG-mCherry interactions beyond classical macromolecular crowding

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

Published: May 7, 2024

Abstract The dense cellular environment influences bio-macromolecular structure, dynamics, interactions and function. Despite advancements in understanding protein-crowder interactions, predicting their precise effects on protein structure function remains challenging. Here, we elucidate the of PEG-induced crowding fluorescent mCherry using molecular dynamics simulations fluorescence-based experiments. We identify characterize specific structural dynamical changes mCherry. Importantly, find which PEG molecules wrap around surface-exposed residues a binding mode previously observed crystal structures. Fluorescence correlation spectroscopy experiments capture changes, including aggregation, suggesting potential role for PEG-mCherry identified simulations. Additionally, fluorescence lifetimes are influenced by not bulkier crowder dextran or another linear polymer, polyvinyl alcohol, highlighting importance crowder-protein soft interactions. This work augments our macromolecular dynamics.

Language: Английский

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Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(13), P. 4079 - 4087

Published: June 20, 2023

Coarse-graining is commonly used to decrease the computational cost of simulations. However, coarse-grained models are also considered have lower transferability, with accuracy for systems outside original scope parametrization. Here, we benchmark a bead-necklace model and modified Martini 2 model, both models, set intrinsically disordered proteins, different having degrees coarse-graining. The SOP-IDP has earlier been this proteins; thus, those results included in study compare how levels coarse-graining compare. sometimes naive expectation least performing best does not hold true experimental pool proteins here. Instead, it showed good agreement, indicating that one should necessarily trust otherwise intuitive notion more advanced inherently being better choice.

Language: Английский

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Big versus small: The impact of aggregate size in disease

Protein Science, Journal Year: 2023, Volume and Issue: 32(7)

Published: May 27, 2023

Protein aggregation results in an array of different size soluble oligomers and larger insoluble fibrils. Insoluble fibrils were originally thought to cause neuronal cell deaths neurodegenerative diseases due their prevalence tissue samples disease models. Despite recent studies demonstrating the toxicity associated with oligomers, many therapeutic strategies still focus on or consider all types aggregates as one group. Oligomers require modeling strategies, targeting toxic species is crucial for successful study development. Here, we review role different-size disease, how factors contributing (mutations, metals, post-translational modifications, lipid interactions) may promote opposed We two computational (molecular dynamics kinetic modeling) they are used model both Finally, outline current aggregating proteins strengths weaknesses versus Altogether, aim highlight importance distinguishing difference between determining which when creating therapeutics protein disease.

Language: Английский

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Analysis of proteins in the light of mutations

European Biophysics Journal, Journal Year: 2024, Volume and Issue: 53(5-6), P. 255 - 265

Published: July 2, 2024

Language: Английский

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Addressing Drug Resistance in Cancer: A Team Medicine Approach

Journal of Clinical Medicine, Journal Year: 2022, Volume and Issue: 11(19), P. 5701 - 5701

Published: Sept. 27, 2022

Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, therapy typically ensues. Several confounding factors appear contribute this challenge. Here, we first discuss some challenges associated with drug resistance. We then how a ‘Team Medicine’ approach, involving an interdisciplinary team basic scientists working together clinicians, has uncovered new therapeutic strategies. These strategies, referred as intermittent or ‘adaptive’ therapy, which are based on eco-evolutionary principles, have met remarkable success in potentially precluding delaying emergence several cancers. Incorporating such treatment strategies into clinical protocols could enhance precision delivering personalized medicine patients. Furthermore, reaching out network hospitals affiliated leading academic centers help them benefit from innovative options. Finally, lowering dose and its frequency (because rather than continuous therapy) can also significant impact toxicity undesirable side effects drugs while financial burden carried by patient insurance providers.

Language: Английский

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Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(10), P. 4149 - 4157

Published: May 7, 2024

A substantial portion of various organisms' proteomes comprises intrinsically disordered proteins (IDPs) that lack a defined three-dimensional structure. These IDPs exhibit diverse array conformations, displaying remarkable spatiotemporal heterogeneity and exceptional conformational flexibility. Characterizing the structure or structural ensemble presents significant conceptual methodological challenges owing to absence well-defined native While databases such as Protein Ensemble Database (PED) provide IDP ensembles obtained through combination experimental data molecular modeling, reaction coordinates poses in comprehensively understanding pertinent aspects system. In this study, we leverage energy landscape visualization method (JCTC, 6482, 2019) scrutinize four sourced from PED. ELViM, methodology circumvents need for priori coordinates, aids analyzing ensembles. The specific investigated are follows: two fragments nucleoporin (NUL: 884-993 NUS: 1313-1390), yeast sic 1 N-terminal (1-90), SH3 domain Drk (1-59). Utilizing ELViM enables comprehensive validation ensembles, facilitating detection potential inconsistencies sampling process. Additionally, it allows identifying characterizing most prevalent conformations within an ensemble. Moreover, facilitates comparative analysis under conditions, thereby providing powerful tool investigating functional mechanisms IDPs.

Language: Английский

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Targeting Intrinsically Disordered Proteins (IDPs) in Drug Discovery

Published: Nov. 29, 2024

"Revolutionizing Drug Delivery Through Computational Design: Nanotechnology and Personalized Therapeutics" explores the transformative potential of computational methodologies in advancing drug delivery systems. This chapter delves into intersection nanotechnology personalized medicine, highlighting how design techniques have revolutionized development targeted efficient drug-delivery vehicles. integration advanced algorithms modeling approaches, researchers can optimize formulations, enhance efficiency, tailor treatments to individual patient profiles. Key topics include role artificial intelligence, nanomaterials, real-time monitoring shaping future delivery. Furthermore, emphasizes importance interdisciplinary collaboration driving innovation overcoming challenges this rapidly evolving field. The promise therapeutics improving outcomes is underscored, with a focus on precision medicine approaches. Overall, provides insights current state research outlines directions for harnessing address unmet medical needs.

Language: Английский

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Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Nov. 13, 2022

Abstract Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by heterogeneous ensemble. Grouping an IDP ensemble into “structurally similar” clusters for visualization, interpretation, and analysis purposes is much-desired but formidable task as the conformational space IDPs inherently high-dimensional reduction techniques often result in ambiguous classifications. Here, we employ t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous from full We illustrate utility t-SNE clustering two proteins, A β 42, C-terminal fragment α -synuclein, their APO states when bound small molecule ligands. Our results shed light on ordered sub-states within ensembles provide structural mechanistic insights binding modes confer specificity affinity ligand binding. projections preserve local neighborhood information interpretable visualizations heterogeneity each enable quantification cluster populations relative shifts upon approach provides new framework detailed investigations thermodynamics kinetics will aid rational drug design IDPs. Significance facilitates clearer understanding properties ”structural ensemble: function” relationships. In this work, unique efficiently using non-linear dimensionality method, (t-SNE), create with structurally similar conformations. show how can be used meaningful biophysical analyses such mechanisms -synuclein Amyloid 42 molecules. Graphical

Language: Английский

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