Accelerating Cryptic Pocket Discovery Using AlphaFold

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(14), P. 4355 - 4363

Published: March 22, 2023

Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate discovery either by generating structures with open directly partially that starting points for simulations. We use generate ensembles 10 known examples, including five were deposited after AlphaFold's training data extracted from PDB. find 6 out cases samples state. For plasmepsin II, an aspartic protease causative agent malaria, only captures a partial opening. As result, ran ensemble AlphaFold-generated and show this strategy opening, even though equivalent amount launched ligand-free experimental structure fails do so. Markov state models (MSMs) constructed AlphaFold-seeded quickly yield free energy landscape opening is good agreement same generated well-tempered metadynamics. Taken together, our results demonstrate has useful role play but many may remain difficult sample using alone.

Language: Английский

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Protein dynamics underlying allosteric regulation

Current Opinion in Structural Biology, Journal Year: 2024, Volume and Issue: 84, P. 102768 - 102768

Published: Jan. 11, 2024

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, conformational changes. An increasing level of experimental resolution over allosteric mechanisms promises a deeper understanding molecular basis for life powerful new therapeutics. In this review, we survey literature an up-to-date biological theoretical protein allostery. By delineating five ways energy landscape or kinetics system may change to give rise allostery, aim help reader grasp its physical origins. To illustrate framework, examine three systems that display these forms allostery: inhibitors beta-lactamases, thermosensation TRP channels, role kinetic allostery function kinases. Finally, summarize growing power computational tools available investigate different presented review.

Language: Английский

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Folding@home: Achievements from over 20 years of citizen science herald the exascale era

Biophysical Journal, Journal Year: 2023, Volume and Issue: 122(14), P. 2852 - 2863

Published: March 21, 2023

Simulations of biomolecules have enormous potential to inform our understanding biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach biomolecular simulation, harnessing resources citizen scientists across globe. Here, we summarize scientific and technical advances this perspective enabled. As project's name implies, early years focused on driving in protein folding by developing statistical methods for capturing long-timescale processes facilitating insight into complex dynamical processes. Success laid foundation broadening scope address other functionally relevant conformational changes, such as receptor signaling, enzyme dynamics, ligand binding. Continued algorithmic advances, hardware developments GPU-based computing, growing scale enabled focus new areas where sampling can be impactful. While previous work sought expand toward larger proteins with slower focuses large-scale comparative studies different sequences chemical compounds better understand development small molecule drugs. Progress these fronts community pivot quickly response COVID-19 pandemic, expanding become world's first exascale computer deploying massive resource provide inner workings SARS-CoV-2 virus aid antivirals. This success provides glimpse what's come supercomputers online, continues its work.

Language: Английский

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Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Protein Science, Journal Year: 2024, Volume and Issue: 33(3)

Published: Feb. 15, 2024

The goal of precision medicine is to utilize our knowledge the molecular causes disease better diagnose and treat patients. However, there a substantial mismatch between small number food drug administration (FDA)-approved drugs annotated coding variants compared needs medicine. This review introduces concept physics-based medicine, scalable framework that promises improve understanding sequence-function relationships accelerate discovery. We show accounting for ensemble structures protein adopts in solution with computer simulations overcomes many limitations imposed by assuming single structure. highlight studies dynamics recent methods analysis structural ensembles. These demonstrate differences conformational distributions predict functional within families variants. Thanks new computational tools are providing unprecedented access ensembles, this insight may enable accurate predictions variant pathogenicity entire libraries further explicitly like alchemical free energy calculations or docking Markov state models, can uncover novel lead compounds. To conclude, we cryptic pockets, cavities absent experimental structures, provide an avenue target proteins currently considered undruggable. Taken together, provides roadmap field science

Language: Английский

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PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(3), P. 1036 - 1050

Published: Jan. 31, 2024

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and cost are at odds with one another, limiting utility of methods that perform this type calculation. Many achieve massive scale by explicitly or implicitly assuming target protein adopts single structure, undergoes limited fluctuations around to minimize cost. Others simulate each protein–ligand complex interest, accepting lower throughput exchange better predictions affinities. Here, we present PopShift framework accounting ensemble structures their relative probabilities. Protein degrees freedom enumerated once, then arbitrarily many molecules can be screened against ensemble. Specifically, use Markov state models (MSMs) as compressed representation protein's thermodynamic We start ligand-free MSM calculate how addition ligand shifts populations conformational based on strength interaction between conformation ligand. In work docking estimate affinity given structure ligand, but any estimator affinities could used framework. test classic benchmark pocket T4 Lysozyme L99A. find is more than common strategies, such traditional docking─producing results compare favorably alchemical energy calculations terms RMSE not correlation─and may have favorable profile some applications. predicting poses, also provides insight into probability different shifted upon various concentrations providing platform allosteric effects binding. Therefore, expect will valuable hit finding phenomena like allostery.

Language: Английский

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AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles

Annual Review of Biomedical Data Science, Journal Year: 2024, Volume and Issue: 7(1), P. 51 - 57

Published: April 11, 2024

Like the black knight in classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction masses and opening up innumerable new avenues of research. Despite this enormous success, calling prediction, much less related problems, “solved” dangerous, doing so could stymie further progress. Imagine what world would be like if we had declared flight solved after first commercial airlines opened stopped investing research development. Likewise, there still important limitations that benefit from addressing. Moreover, limited our understanding diversity different structures a single can adopt (called conformational ensemble) dynamics which explores space. What clear ensembles critical function, aspect will advance ability design proteins drugs.

Language: Английский

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Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Frontiers in Molecular Biosciences, Journal Year: 2023, Volume and Issue: 10

Published: April 18, 2023

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data available. In the best case, crystal structures of ligand-bound protein help find more potent ligands. However, virtual screens tend to be less when only ligand-free are available, and even if homology model or other predicted structure must used. Here, we explore possibility that this situation improved by better accounting dynamics, as simulations started from single have reasonable chance sampling nearby compatible with ligand binding. As specific example, consider cancer target PPM1D/Wip1 phosphatase, lacks structures. High-throughput led discovery several allosteric inhibitors PPM1D, their binding mode remains unknown. To enable further efforts, assessed an AlphaFold-predicted PPM1D Markov state (MSM) built molecular dynamics initiated structure. Our reveal cryptic pocket at interface between two important elements, flap hinge regions. Using deep learning predict pose quality each docked compound active site suggests strongly prefer pocket, consistent effect. The affinities dynamically uncovered also recapitulate relative potencies compounds (τb = 0.70) than static 0.42). Taken together, these results suggest targeting good strategy drugging and, generally, conformations selected simulation improve limited

Language: Английский

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From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on “Allosteric Intersection” of Biochemical and Big Data Approaches

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(9), P. 7747 - 7747

Published: April 24, 2023

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems automated workflows that are able to model complex chemical biological phenomena. In years, approaches been developed actively deployed facilitate computational experimental studies protein dynamics allosteric mechanisms. this review, we discuss detail developments along two major directions research through lens data-intensive biochemical AI-based methods. Despite considerable progress applications AI methods for structure studies, intersection between regulation, emerging structural biology technologies remains largely unexplored, calling development AI-augmented integrative biology. focus on latest remarkable deep high-throughput mining comprehensive mapping landscapes regulatory mechanisms as well prediction characterization binding sites proteome level. We also expand our knowledge universe allostery. conclude with an outlook highlight importance developing open science infrastructure regulation validation using community-accessible tools uniquely leverage existing simulation knowledgebase enable interrogation functions can provide a much-needed boost further innovation integration empowered by booming field.

Language: Английский

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Opening and closing of a cryptic pocket in VP35 toggles it between two different RNA-binding modes

Published: Jan. 3, 2025

Cryptic pockets are of growing interest as potential drug targets, particularly to control protein-nucleic acid interactions that often occur via flat surfaces. However, it remains unclear whether cryptic contribute protein function or if they merely happenstantial features can easily be evolved away achieve resistance. Here, we explore a pocket in the Interferon Inhibitory Domain (IID) viral 35 (VP35) Zaire ebolavirus aids its ability bind double-stranded RNA (dsRNA). We use simulations and experiments study relationship between opening dsRNA binding IIDs two other filoviruses, Reston Marburg. These homologs have nearly identical structures but block different interferon pathways due affinities for blunt ends backbone dsRNA. Simulations thiol-labeling demonstrate varying probabilities opening. Subsequent dsRNA-binding assays suggest closed conformations preferentially while open prefer backbone. Point mutations modulate proteins further confirm this preference. results has function, suggesting under selective pressure may difficult evolve

Language: Английский

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Opening and closing of a cryptic pocket in VP35 toggles it between two different RNA-binding modes

Published: Jan. 3, 2025

Cryptic pockets are of growing interest as potential drug targets, particularly to control protein-nucleic acid interactions that often occur via flat surfaces. However, it remains unclear whether cryptic contribute protein function or if they merely happenstantial features can easily be evolved away achieve resistance. Here, we explore a pocket in the Interferon Inhibitory Domain (IID) viral 35 (VP35) Zaire ebolavirus aids its ability bind double-stranded RNA (dsRNA). We use simulations and experiments study relationship between opening dsRNA binding IIDs two other filoviruses, Reston Marburg. These homologs have nearly identical structures but block different interferon pathways due affinities for blunt ends backbone dsRNA. Simulations thiol-labeling demonstrate varying probabilities opening. Subsequent dsRNA-binding assays suggest closed conformations preferentially while open prefer backbone. Point mutations modulate proteins further confirm this preference. results has function, suggesting under selective pressure may difficult evolve

Language: Английский

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