The molecular mechanism of temperature-dependent phase separation of heat shock factor 1

Nature Chemical Biology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Language: Английский

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Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

Pharmacology & Therapeutics, Journal Year: 2025, Volume and Issue: unknown, P. 108797 - 108797

Published: Jan. 1, 2025

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged fact that approximately half human proteome made up proteins do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered (IDPs), are involved numerous physiological functions and associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), type 2 diabetes. Targeting these challenging for two reasons: i) we need to preserve their functions, ii) drug design molecular docking possible due lack reliable starting conditions. Faced this challenge, solutions proposed artificial intelligence (AI) such as AlphaFold clearly unsuitable. Instead, suggest an innovative approach consisting mimicking, short synthetic peptides, conformational flexibility IDPs. which call adaptive derived from domains IDPs become structured after interacting ligand. Adaptive peptides designed aim selectively antagonizing harmful effects IDPs, without targeting them directly but through selected ligands, affecting properties. This"target target, arrow" strategy promised open new route discovery currently undruggable proteins.

Language: Английский

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The Conformational Contribution to Molecular Complexity and Its Implications for Information Processing in Living Beings and Chemical Artificial Intelligence

Biomimetics, Journal Year: 2024, Volume and Issue: 9(2), P. 121 - 121

Published: Feb. 19, 2024

This work highlights the relevant contribution of conformational stereoisomers to complexity and functions any molecular compound. Conformers have same structural formulas but different orientations atoms in three-dimensional space. Moving from one conformer another is possible without breaking covalent bonds. The interconversion usually feasible through thermal energy available ordinary conditions. behavior most biopolymers, such as enzymes, antibodies, RNA, DNA, understandable if we consider that each exists an ensemble conformers. Each collection confers multi-functionality adaptability single biopolymers. distribution biopolymer has features a fuzzy set. Hence, every compound conformers allows implementation Since proteins, RNA sets, it fair say life's logic fuzzy. power processing makes living beings capable swift decisions environments dominated by uncertainty vagueness. These performances can be implemented chemical robots, which are confined assemblies mimicking unicellular organisms: they supposed help humans "colonise" world defeat diseases fight pollution environment.

Language: Английский

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Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(14), P. 4711 - 4727

Published: June 20, 2023

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, Aβ42, α-synuclein, their APO states when bound small molecule ligands. Our results shed light on ordered substates within ensembles provide structural mechanistic insights binding modes confer specificity affinity ligand binding. projections preserve local neighborhood information, interpretable visualizations heterogeneity each ensemble, enable quantification cluster populations relative shifts upon approach provides new framework detailed investigations thermodynamics kinetics will aid rational drug design IDPs.

Language: Английский

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Illuminating Intrinsically Disordered Proteins with Integrative Structural Biology

Biomolecules, Journal Year: 2023, Volume and Issue: 13(1), P. 124 - 124

Published: Jan. 7, 2023

Intense study of intrinsically disordered proteins (IDPs) did not begin in earnest until the late 1990s when a few groups, working independently, convinced community that these ‘weird’ could have important functions. Over past two decades, it has become clear IDPs play critical roles multitude biological phenomena with prominent examples including coordination signaling hubs, enabling gene regulation, and regulating ion channels, just to name few. One contributing factor delayed appreciation IDP functional significance is experimental difficulty characterizing their dynamic conformations. The combined application multiple methods, termed integrative structural biology, emerged as an essential approach understanding phenomena. Here, we review some recent applications biology philosophy IDPs.

Language: Английский

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Protein structure–function continuum model: Emerging nexuses between specificity, evolution, and structure

Protein Science, Journal Year: 2024, Volume and Issue: 33(4)

Published: March 27, 2024

Abstract The rationale for replacing the old binary of structure–function with trinity structure, disorder, and function has gained considerable ground in recent years. A continuum model based on expanded form existing paradigm can now subsume importance both conformational flexibility intrinsic disorder protein function. is actually critical understanding protein–protein interactions many regulatory processes, formation membrane‐less organelles, our revised notions specificity as amply illustrated by moonlighting proteins. While its amyloids prions often discussed, roles infectious diseases under extreme conditions are also becoming clear. This review an attempt to discuss how current function, specificity, evolution fit better model. integration structure a single may bring greater clarity continuing quest proteins molecular mechanisms their functionality.

Language: Английский

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Rational drug design targeting intrinsically disordered proteins

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2023, Volume and Issue: 13(6)

Published: Aug. 26, 2023

Abstract Intrinsically disordered proteins (IDPs) are that perform important biological functions without well‐defined structures under physiological conditions. IDPs can form fuzzy complexes with other molecules, participate in the formation of membraneless organelles, and function as hubs protein–protein interaction networks. The malfunction causes major human diseases. However, drug design targeting remains challenging due to their highly dynamic interactions. Turning into druggable targets provides a great opportunity extend target‐space for novel discovery. Integrative structural biology approaches combine information derived from computational simulations, artificial intelligence/data‐driven analysis experimental studies have been used uncover interactions IDPs. An increasing number ligands directly bind found either by target‐based screening or phenotypic screening. Along understanding IDP binding its partners, structure‐based strategies, especially conformational ensemble‐based ligand computer‐aided optimization algorithms, greatly accelerated development ligands. It is inspiring several IDP‐targeting small‐molecule peptide drugs advanced clinical trials. new methods need be further developed efficiently discovering optimizing specific potent vast interactions, expected become valuable treasure targets. This article categorized under: Structure Mechanism > Computational Biochemistry Biophysics

Language: Английский

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PEG–mCherry interactions beyond classical macromolecular crowding

Protein Science, Journal Year: 2025, Volume and Issue: 34(3)

Published: Feb. 19, 2025

Abstract The dense cellular environment influences bio‐macromolecular structure, dynamics, interactions, and function. Despite advancements in understanding protein–crowder 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 interactions 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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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning

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

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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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