Evaluation of AlphaFold2 structures as docking targets

Protein Science, Год журнала: 2022, Номер 32(1)

Опубликована: Дек. 7, 2022

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone global root-mean-square deviation (RMSD) when compared experimental structures. However, it unclear if the predicted by will be valuable targets of docking. To address this question, we redocked ligands in PDBbind datasets against co-crystallized receptor using AutoDock-GPU. We find that quality measure provided during structure prediction not good predictor docking performance, despite accurately reflecting alpha carbon alignment Removing low-confidence regions making side chains flexible improves outcomes. Overall, conformation, fine structural details limit naive application models as targets.

Язык: Английский

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From byte to bench to bedside: molecular dynamics simulations and drug discovery

BMC Biology, Год журнала: 2023, Номер 21(1)

Опубликована: Дек. 29, 2023

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware software improvements. Given these advancements, MD are poised become even more powerful tools for investigating dynamic interactions between potential small-molecule drugs their target proteins, with significant implications pharmacological research.

Язык: Английский

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AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine–associated receptor 1

Science Advances, Год журнала: 2024, Номер 10(32)

Опубликована: Авг. 7, 2024

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using structures generated by AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of trace amine-associated receptor 1 (TAAR1), a G protein-coupled unknown target treating neuropsychiatric disorders. Sets 30 32 highly ranked from model screens, respectively, experimentally evaluated. Of these, 25 TAAR1 agonists with potencies ranging 12 0.03 μM. The screen yielded more twofold higher hit rate (60%) discovered most potent agonists. A agonist promising selectivity profile drug-like properties showed physiological antipsychotic-like effects in wild-type but not knockout mice. These results demonstrate that can accelerate discovery.

Язык: Английский

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Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(2), С. 1358 - 1358

Опубликована: Янв. 22, 2024

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of shares low sequence identity other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold machine learning approach that can predict the 3D proteins high accuracy even in absence similar structures. However, fact models are predicted small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an model by adding zinc ion minimization presence inhibitors. In current study, implement comparative structure-based virtual screening utilizing previously to identify novel selective stepwise was successful identifying hit subsequently tested using vitro enzymatic assay. compound showed IC50 value 3.5 µM could selectively inhibit over subtypes at 10 concentration. addition, carried out molecular dynamics simulations further confirm binding hypothesis obtained docking study. These results reinforce presented optimization applicability search inhibitors discovery.

Язык: Английский

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Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction

Computational and Structural Biotechnology Journal, Год журнала: 2022, Номер 21, С. 158 - 167

Опубликована: Дек. 1, 2022

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how can be transferred to advances structure-based drug discovery. Because lessons from recent GPCR dock challenge were inconclusive primarily due size of dataset, this work we further elaborated on 70 diverse complexes bound either small molecules or peptides investigate best-practice modeling and docking strategies for From our quantitative analysis, it is shown that substantial improvements virtual screening have been possible by advance DL-based predictions with respect expected results combination best pre-DL tools. The success rate model structures approaches cross-docking experimental structures, showing over 30% improvement protocols. This amount performance could achieved only when two points considered properly: 1) correct functional-state receptors 2) receptor-flexible docking. Best-practice confidence estimation metric suggested may serve as guideline future computer-aided discovery scenarios.

Язык: Английский

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Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation

Molecules, Год журнала: 2024, Номер 29(6), С. 1363 - 1363

Опубликована: Март 19, 2024

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is repositioning, enabling the identification new therapeutic applications for existing drugs. In present study, a trained deep-learning model was employed screen library FDA-approved drugs discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active decoy compounds specific JAK2 were obtained from DUD-E database. RDKit, cheminformatic toolkit, utilized extract molecular features compounds. DeepChem framework’s GraphConvMol, based on graph convolutional network models, applied build predictive using datasets. Subsequently, used predict inhibitory potential Based these predictions, ribociclib, topiroxostat, amodiaquine, gefitinib identified as inhibitors. Notably, several known demonstrated high according prediction results, validating reliability our model. further validate findings confirm their activity, docking experiments conducted tofacitinib—an inhibition. Experimental validation successfully confirmed computational analysis results by demonstrating that exhibited comparable activity against compared tofacitinib. conclusion, study highlights how learning models can significantly enhance virtual screening efforts discovery efficiently identifying candidates targets such These newly discovered hold promises deserving exploration investigation.

Язык: Английский

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DiffBindFR: an SE(3) equivariant network for flexible protein–ligand docking

Chemical Science, Год журнала: 2024, Номер 15(21), С. 7926 - 7942

Опубликована: Янв. 1, 2024

DiffBindFR, a diffusion model based flexible full-atom protein–ligand docking tool, demonstrates its superior and side-chain refinement accuracy with reliable physical plausibility.

Язык: Английский

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Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

ACS Omega, Год журнала: 2024, Номер 9(18), С. 20338 - 20349

Опубликована: Апрель 23, 2024

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting harmful increase of reactive oxygen species (ROS) various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed comprehensive approach, integrating both ligand- structure-based virtual screening followed experimental validation. Initially, candidate compounds were extracted from extensive drug chemical libraries using DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement docking dynamics (MD) simulations, which are crucial understanding compound–receptor interactions dynamic behavior simulated physiological environment. Finally, subjected validation their biological activity an aldose inhibitor kit. The approach led identification promising compound, demonstrating significant potential inhibitor. This not only yields therapeutic intervention DM-related but also establishes integrated protocol development, setting new benchmark field.

Язык: Английский

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Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10139 - 10139

Опубликована: Сен. 21, 2024

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in field computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough structural biology been established by developing artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 provides state-of-the-art protein structures from nearly all known sequences with high accuracy. This study examined reliability models compared to experimental drug discovery, focusing on common drug-targeted classes as G protein-coupled receptors (GPCRs) class A. total 32 representative targets were selected, including X-ray crystallographic and Cryo-EM their corresponding models. quality was assessed using different validation tools, pLDDT score, RMSD value, MolProbity percentage Ramachandran favored, QMEAN Z-score, QMEANDisCo Global. molecular docking performed Genetic Optimization Ligand Docking (GOLD) software. models’ virtual screening determined ability predict ligand binding poses closest native pose assessing Root Mean Square Deviation (RMSD) metric scoring function. function evaluated enrichment factor (EF). Furthermore, capability identify hits key protein–ligand interactions analyzed. posing power results showed that successfully predicted (RMSD < 2 Å). However, they exhibited lower power, average EF values 2.24, 2.42, 1.82 X-ray, Cryo-EM, structures, respectively. Moreover, our revealed can competitive inhibitors. In conclusion, this found provided comparable particularly certain GPCR targets, could potentially significantly impact discovery.

Язык: Английский

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

PLoS Computational Biology, Год журнала: 2024, Номер 20(7), С. e1012302 - e1012302

Опубликована: Июль 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.

Язык: Английский

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