Direct prediction of intrinsically disordered protein conformational properties from sequence

Nature Methods, Journal Year: 2024, Volume and Issue: 21(3), P. 465 - 476

Published: Jan. 31, 2024

Abstract Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range functional roles. While folded generally well described by stable three-dimensional structure, IDRs exist in collection interconverting states known as an ensemble. This structural heterogeneity means that largely absent from the Protein Data Bank, contributing to lack computational approaches predict ensemble conformational properties sequence. Here we combine rational sequence design, large-scale molecular simulations deep learning develop ALBATROSS, deep-learning model for predicting dimensions IDRs, including radius gyration, end-to-end distance, polymer-scaling exponent asphericity, directly sequences at proteome-wide scale. ALBATROSS is lightweight, easy use accessible both locally installable software package point-and-click-style interface via Google Colab notebooks. We first demonstrate applicability our predictors examining generalizability sequence–ensemble relationships IDRs. Then, leverage high-throughput nature characterize sequence-specific biophysical behavior within between proteomes.

Language: Английский

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AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: 52(W1), P. W176 - W181

Published: May 15, 2024

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these are challenge to study experimentally, computational methods play roles in their characterization. One of the commonly used tools is IUPred web server which provides prediction binding sites. rooted simple biophysical model uses limited number parameters largely derived globular structures only. This enabled an incredibly fast robust method, however, its limitations have also become apparent light recent breakthrough using deep learning techniques. Here, we present AIUPred, novel version incorporates techniques into energy estimation framework. It achieves improved performance while keeping robustness original method. Based evaluation benchmark datasets, AIUPred scored amongst top three sequence based methods. With new offer reliable visual analysis for users as well options analyze whole genomes mere seconds with downloadable package. available at https://aiupred.elte.hu.

Language: Английский

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A coarse‐grained model for disordered and multi‐domain proteins

Protein Science, Journal Year: 2024, Volume and Issue: 33(11)

Published: Oct. 16, 2024

Many proteins contain more than one folded domain, and such modular multi-domain help expand the functional repertoire of proteins. Because their larger size often substantial dynamics, it may be difficult to characterize conformational ensembles by simulations. Here, we present a coarse-grained model for that is both fast provides an accurate description global properties in solution. We show accuracy one-bead-per-residue depends on how interaction sites domains are represented. Specifically, find excessive domain-domain interactions if located at position C

Language: Английский

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Direct prediction of intermolecular interactions driven by disordered regions

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

Published: June 3, 2024

ABSTRACT Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many which involve interactions with partner proteins. Molecular recognition is typically considered through the lens sequence-specific binding events. However, growing body work has shown that IDRs often interact partners in manner does not depend on precise order amino acid order, instead driven by complementary chemical leading to bound-state complexes. Despite this emerging paradigm, we lack tools describe, quantify, predict, and interpret these types structurally heterogeneous from underlying sequences. Here, repurpose physics developed originally molecular simulations develop an approach predicting intermolecular between Our enables direct prediction phase diagrams, identification chemically-specific interaction hotspots IDRs, route test mechanistic hypotheses regarding IDR function context recognition. We use our examine range systems questions highlight its versatility applicability.

Language: Английский

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Prediction of phase separation propensities of disordered proteins from sequence

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

Published: June 3, 2024

Abstract Phase separation is thought to be one possible mechanism governing the selective cellular enrichment of biomolecular constituents for processes such as transcriptional activation, mRNA regulation, and immune signaling. mediated by multivalent interactions biological macromolecules including intrinsically disordered proteins regions (IDRs). Despite considerable advances in experiments, theory simulations, prediction thermodynamics IDR phase behaviour remains challenging. We combined coarse-grained molecular dynamics simulations active learning develop a fast accurate machine model predict free energy saturation concentration directly from sequence. validate using both experimental computational data. apply our all 27,663 IDRs chain length up 800 residues human proteome find that 1,420 these (5%) are predicted undergo homotypic with transfer energies < −2 k B T . use understand relationship between single-chain compaction separation, changes charge-to hydrophobicity-mediated can break symmetry intra-and inter-molecular interactions. also analyse structural preferences at condensate interfaces substantial heterogeneity determined same sequence properties separation. Our work refines established rules relationships features propensities, models will useful interpreting designing experiments on role design specific propensities.

Language: Английский

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AlphaFold prediction of structural ensembles of disordered proteins

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 14, 2025

Abstract Deep learning methods of predicting protein structures have reached an accuracy comparable to that high-resolution experimental methods. It is thus possible generate accurate models the native states hundreds millions proteins. An open question, however, concerns whether these advances can be translated disordered proteins, which should represented as structural ensembles because their heterogeneous and dynamical nature. To address this problem, we introduce AlphaFold-Metainference method use AlphaFold-derived distances restraints in molecular dynamics simulations construct ordered The results obtained using illustrate possibility making predictions conformational properties proteins deep trained on large databases available for folded

Language: Английский

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Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 91, P. 103002 - 103002

Published: Feb. 12, 2025

Language: Английский

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Prediction of phase-separation propensities of disordered proteins from sequence

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(13)

Published: March 25, 2025

Phase separation is one possible mechanism governing the selective cellular enrichment of biomolecular constituents for processes such as transcriptional activation, mRNA regulation, and immune signaling. mediated by multivalent interactions macromolecules including intrinsically disordered proteins regions (IDRs). Despite considerable advances in experiments, theory, simulations, prediction thermodynamics IDR phase behavior remains challenging. We combined coarse-grained molecular dynamics simulations active learning to develop a fast accurate machine model predict free energy saturation concentration directly from sequence. validate using computational previously measured experimental data, well new data six proteins. apply our all 27,663 IDRs chain length up 800 residues human proteome find that 1,420 these (5%) are predicted undergo homotypic with transfer energies < −2 k B T . use understand relationship between single-chain compaction changes charge- hydrophobicity-mediated can break symmetry intra- intermolecular interactions. also provide proof principle how be used force field refinement. Our work refines quantifies established rules connection sequence features phase-separation propensities, models will useful interpreting designing experiments on role separation, design specific propensities.

Language: Английский

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PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D536 - D544

Published: Oct. 30, 2023

Abstract The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version PED reflects advancements in field, denoting a continual expansion with total 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment number few yet-unprecedented new entered database, also determined or refined by electron paramagnetic resonance circular dichroism data. In addition, was enriched several features, deposition service, improved user interface, database cross-referencing options integration 3D-Beacons network—all representing efforts to improve FAIRness database. Foreseeably, will keep growing size expanding types accurate fast ML-based generative models coarse-grained simulations. Therefore, among future efforts, priority be given further develop compatible modeled at level.

Language: Английский

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SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(16), P. 5609 - 5620

Published: July 18, 2023

Conformational heterogeneity is a defining hallmark of intrinsically disordered proteins and protein regions (IDRs). The functions IDRs the emergent cellular phenotypes they control are associated with sequence-specific conformational ensembles. Simulations ensembles that based on atomistic coarse-grained models routinely used to uncover interactions may contribute IDR functions. These simulations performed either independently or in conjunction data from experiments. Functionally relevant features can span range length scales. Extracting these requires analysis routines quantify properties. Here, we describe new suite simulation unfolded (SOURSOP), an object-oriented open-source toolkit designed for simulated IDRs. SOURSOP implements several motivated by principles polymer physics, offering unique collection simple-to-use characterize As extendable framework, supports development implementation be easily packaged shared.

Language: Английский

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