A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(7), P. e1012302 - e1012302

Published: July 24, 2024

Protein kinase function and interactions with drugs are controlled in part by the movement of DFG ɑC-Helix motifs that related to catalytic activity kinase. Small molecule ligands elicit therapeutic effects distinct selectivity profiles residence times often depend on active or inactive conformation(s) they bind. Modern AI-based structural modeling methods have potential expand upon limited availability experimentally determined structures states. Here, we first explored conformational space kinases PDB models generated AlphaFold2 (AF2) ESMFold, two prominent protein structure prediction methods. Our investigation AF2’s ability explore diversity kinome at various multiple sequence alignment (MSA) depths showed a bias within predicted DFG-in conformations, particularly those motif, based their overabundance PDB. We demonstrate predicting using AF2 lower MSA these alternative conformations more extensively, including identifying previously unobserved for 398 kinases. Ligand enrichment analyses 23 that, average, docked distinguished between molecules decoys better than random (average AUC (avgAUC) 64.58), but select perform well (e.g., avgAUCs PTK2 JAK2 were 79.28 80.16, respectively). Further analysis explained ligand discrepancy low- high-performing as binding site occlusions would preclude docking. The overall results our suggested although uncharted regions exhibited scores suitable rational drug discovery, rigorous refinement is likely still necessary discovery campaigns.

Language: Английский

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Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15

Proteins Structure Function and Bioinformatics, Journal Year: 2023, Volume and Issue: 91(12), P. 1903 - 1911

Published: Oct. 23, 2023

Abstract For the first time, 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing ensembles four nine targets, an encouraging result. structures, enhanced sampling with variations AlphaFold2 deep learning method by far most effective approach. One substantial conformational change caused single mutation across complex interface accurately reproduced. In two other assembly modeling cases, methods succeeded near to experimental ones even though environmental factors were not calculations. An experimentally derived flexibility ensemble allowed accurate structure model be identified. Difficulties how handle sparse low‐resolution data current lack RNA/protein complexes. However, these obstacles appear addressable.

Language: Английский

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Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(3), P. 725 - 732

Published: Jan. 12, 2024

Transporter proteins change their conformations to carry substrate across the cell membrane. The conformational dynamics is vital understanding transport function. We have studied oxalate transporter (OxlT), an oxalate:formate antiporter from Oxalobacter formigenes, significant in avoiding kidney stone formation. atomic structure of OxlT has been recently solved outward-open and occluded states. However, inward-open conformation still missing, hindering a complete transporter. Here, we performed Gaussian accelerated molecular simulation sample extensive space successfully predicted where cytoplasmic formate binding was preferred over binding. also identified critical interactions for conformation. results were complemented by AlphaFold2 prediction. Although solely conformation, mutation residues made it partly predict identifying possible state-shifting mutations.

Language: Английский

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Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(4), P. 914 - 936

Published: Jan. 18, 2024

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational improved molecules. For unresolved GPCR-ligand complexes, workflow can apply in combination with alpha-fold (AF)-derived or other homology models experimentally resolved modes relevant GPCR-homologs needs to be tested. Here, as test case, we studied congeneric set bind structurally G protein-coupled (GPCR), inactive human adenosine A3 (hA3R). We tested three available from which two have been generated experimental structures hA1R hA2AR one model was multistate alphafold 2 (AF2)-derived model. applied alchemical calculations integration coupled molecular dynamics (TI/MD) simulations calculate relative free energies residence time (τ)-random accelerated MD (τ-RAMD) times (RTs) for antagonists. While TI/MD produced, models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, 0.67 mean unsigned error (mue) values 0.94, 1.31, 0.81 kcal mol–1, τ-RAMD method showed 0.92 0.52 first but failed produce accurate results AF2-derived With subsequent optimization by reorientation side chain R1735.34 located extracellular loop (EL2) blocked ligand's unbinding, 0.84 performance (r 0.81, mue 0.56 mol–1). Overall, after refining AF2 physics-based tools, were able show strong between predicted ligand affinities, achieving level accuracy comparable an structure. The used receptors, helping rank candidate drugs series enabling prioritization leads stronger affinities longer times.

Language: Английский

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Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling

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

Published: Feb. 20, 2024

The groundbreaking achievements of AlphaFold2 (AF2) approaches in protein structure modeling marked a transformative era structural biology. Despite the success AF2 tools predicting single structures, these methods showed intrinsic limitations multiple functional conformations allosteric proteins and fold-switching systems. recent NMR-based determination unbound ABL kinase active state two inactive low-populated that are unique for presents an ideal challenge approaches. In current study we employ several implementations to predict conformational ensembles states including (a) sequence alignments (MSA) subsampling approach; (b) SPEACH_AF approach which alanine scanning is performed on generated MSAs; (c) introduced this randomized full mutational manipulation variations combined with MSA subsampling. We show proposed adaptation local frustration mapping enable accurate prediction intermediate structures ensembles, also offering robust interpretable characterization predictions detecting hidden states. found large high residue clusters uniquely characteristic low-populated, fully form can define energetically frustrated cracking sites transitions, presenting difficult targets methods. This uncovered previously unappreciated, fundamental connections between distinct patterns successes/limitations conformations, providing better understanding benefits AF2-based adaptations ensembles.

Language: Английский

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Exploring Conformational Landscapes and Binding Mechanisms of Convergent Evolution for the SARS-CoV-2 Spike Omicron Variant Complexes with the ACE2 Receptor Using AlphaFold2-Based Structural Ensembles and Molecular Dynamics Simulations

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(25), P. 17720 - 17744

Published: Jan. 1, 2024

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution binding mechanisms convergent the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 BQ.1.1. We employed validated several different adaptations AlphaFold methodology including introduced randomized full sequence scanning manipulation variations systematically explore dynamics complexes with ACE2 receptor. Microsecond (MD) provide a detailed characterization landscapes thermodynamic stability variant complexes. By integrating predictions from applying statistical confidence metrics can expand identify functional conformations that determine equilibrium ACE2. Conformational RBD-ACE2 obtained using MD are accurate comparative prediction energetics revealing an excellent agreement experimental data. particular, results demonstrated AlphaFold-generated extended produce energies The study suggested complementarities potential synergies between showing information both methods potentially yield more adequate This provides insights in interplay binding, through acquisition mutational sites may leverage adaptability dynamic couplings key energy hotspots optimize affinity enable immune evasion.

Language: Английский

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Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Published: Aug. 14, 2024

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2’s strides in protein native structure prediction, its focus apo structures overlooks ligands and associated holo Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application AlphaFold2 models virtual screening dis-covery remains tentative. Here, we demonstrate an based framework combined with all-atom enhanced sampling molecular dynamics induced fit docking, named AF2RAVE-Glide, to conduct computational model small binding kinase conformations, initiated sequences. We AF2RAVE-Glide workflow three different kinases their type I II inhibitors, special emphasis known inhibitors which target classical DFG-out state. These states are not easy sample AlphaFold2. Here how AF2RAVE these conformations can be sampled for high enough ac- curacy enable subsequent docking more than 50% success rates across calculations. believe protocol should deployable other proteins generally.

Language: Английский

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Targeting in silico GPCR conformations with ultra-large library screening for hit discovery

Trends in Pharmacological Sciences, Journal Year: 2023, Volume and Issue: 44(3), P. 150 - 161

Published: Jan. 19, 2023

Language: Английский

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Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(22), P. 8414 - 8422

Published: Nov. 9, 2023

For an effective drug, strong binding to the target protein is a prerequisite, but it not enough. To produce particular functional response, drugs need either block proteins' functions or modulate their activities by changing conformational equilibrium. The free energy of compound its routinely calculated, timescales for changes are prohibitively long be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that energies ligands with different receptor conformations may infer efficacy. However, this hypothesis has been thoroughly validated. We present actionable protocol and comprehensive study show thermodynamics provides predictor efficacy ligand. apply absolute perturbation method bound active inactive states eight G protein-coupled receptors nuclear then compare resulting energies. find carefully designed restraints often necessary model corresponding ensembles each state. Our achieves unprecedented performance in classifying as agonists antagonists across various investigated receptors, all which important drug targets.

Language: Английский

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Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

eLife, Journal Year: 2024, Volume and Issue: 13

Published: July 1, 2024

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus apo structures overlooks ligands and associated holo Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application AlphaFold2 models virtual screening discovery remains tentative. Here, we demonstrate an based framework combined with all-atom enhanced sampling molecular dynamics induced fit docking, named AF2RAVE-Glide, to conduct computational model small binding kinase conformations, initiated sequences. We AF2RAVE-Glide workflow three different kinases their type I II inhibitors, special emphasis known inhibitors which target classical DFG-out state. These states are not easy sample AlphaFold2. Here how AF2RAVE these conformations can be sampled for high enough accuracy enable subsequent docking more than 50% success rates across calculations. believe protocol should deployable other proteins generally.

Language: Английский

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