A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Cruz, Matthew A.; Frederick, Thomas E.; Mallimadugula, Upasana L.; Singh, Sukrit; Vithani, Neha; Zimmerman, Maxwell I.; Porter, Justin R.; Moeder, Katelyn E.; Amarasinghe, Gaya K.; Bowman, Gregory R.

doi:10.1038/s41467-022-29927-9

Cited by 26 publications

(30 citation statements)

References 71 publications

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“…In previous work, MD simulations captured opening at known cryptic sites in TEM β-lactamase 22 , Interleukin-2 23 , and several other protein drug targets 13 , 24 . MD simulations have also identified novel cryptic pockets that were experimentally confirmed in thiol labeling experiments 5 , 25 . However, these studies focused on a narrow set of proteins or did not evaluate whether pocket formation was localized to known ligand-binding sites.…”

Section: Resultsmentioning

confidence: 69%

“…For a full list of simulations that were conducted as part of this study, please reference Supplementary Table 4 . This study also used pre-existing FAST simulation data from multiple sources, not all of which had used the same FAST parameters 5 , 12 .…”

Section: Methodsmentioning

confidence: 99%

“…Protein structural fluctuations often lead to the formation of cryptic pockets 1 – 5 , which present druggable sites beyond pockets apparent in experimentally-determined structures. From a drug development perspective, targeting these cryptic pockets provides a number of compelling opportunities.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we trained a model to take a structure of a protein and forecast whether each residue will participate in the formation of a cryptic pocket over the course of a short simulation initiated from that structure. Our training dataset consists of a previous molecular dynamics simulations study that identified cryptic pockets across most proteins in the SARS-CoV-2 proteome 12 , simulations from a previous study of Ebolavirus VP35 5 , and a set of 16 proteins with known ligand-binding cryptic pockets that we simulated for this study. To test the ability of PocketMiner to predict the locations of ligand-binding cryptic pockets from single structures, we curated a dataset of 39 examples of cryptic pockets from the PDB.…”

Section: Introductionmentioning

confidence: 99%

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Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

et al. 2023

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Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.

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Section: Resultsmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

et al. 2023

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“…The Trp41 adopts a holo-like orientation while the distance between Trp41 and Tyr77 is large, creating a cavity for ligands to bind. Furthermore, we can build Markov State Models 26,40,41 (MSMs) of the cryptic pocket ensemble to measure the probability of cryptic pocket opening. MSM are network models of free energy landscapes composed of many conformational states and the probabilities of transitioning between these states.…”

Section: Seeding With Alphafold Accelerates Exploration Of the Free E...mentioning

confidence: 99%

Accelerating cryptic pocket discovery using AlphaFold

Meller

Bhakat

Solieva

et al. 2022

Preprint

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Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket opening is often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including 5 that were deposited after AlphaFold's training data was extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.

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

Meller,

Kelly,

Smith

et al. 2024

Protein Science

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

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A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Cited by 26 publications

References 71 publications

Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Accelerating cryptic pocket discovery using AlphaFold

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

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