Artur Meller
Program: Computational and Systems Biology
Current advisor: Gregory R. Bowman, PhD
Undergraduate university: Harvard University
Research summary
Of the protein structures deposited in the Protein Data Bank, less than half have pockets
suitable for the binding of drugs. Even when proteins contain pockets in their ground state structures (e.g., the nucleotide-binding active site in myosin motors), achieving specificity remains a central challenge in drug design as many protein families share common structural motifs. Cryptic pockets are cavities absent in ligand-free experimental structures that form due to protein fluctuations in solution. They provide a means to specifically target proteins currently considered undruggable. While cryptic pockets are alluring drug targets, it remains difficult to predict which proteins will form cryptic pockets. It is also unclear how certain compounds that bind at cryptic pockets discriminate between similar targets, even though those targets all have closed pockets in experimental structures. To address these problems, I developed a graph neural network called PocketMiner that predicts whether a protein is likely to form a cryptic pocket based on its ground state structure. I demonstrated that PocketMiner achieves improved performance (ROC-AUC: 0.87) compared to existing methods at >1,000-fold faster run times.
To further accelerate cryptic pocket discovery, I leveraged the protein structure prediction algorithm AlphaFold to generate ensembles of structures. I showed that AlphaFold-generated ensembles often sample cryptic pocket opening, and that using these ensembles as starting structures for molecular dynamics simulations can enhance sampling of a rare cryptic pocket opening in an antimalarial drug target. To connect cryptic pocket opening to drug specificity, I showed that differences in the probability of cryptic pocket opening underpin the specificity of a myosin inhibitor known to bind at a cryptic site. By combining Markov state models with molecular docking, we accurately predicted the affinity of blebbistatin for different myosin proteins. Finally, to demonstrate the utility of these methods for drug discovery applications, I used simulations of a cancer drug target, PPM1D phosphatase, to discover a novel cryptic pocket. Docked poses of compounds bound to this cryptic pocket can be fed to a neural network that predicts affinities to accurately rank compounds by their experimental affinities. Taken together, these results represent an important advancement towards rational drug design against previously undruggable targets.
Graduate publications
Liu C, Karabina A, Meller A, Bhattacharjee A, Agostino CJ, Bowman GR, Ruppel KM, Spudich JA, Leinwand LA. 2024 Homologous mutations in human β, embryonic, and perinatal muscle myosins have divergent effects on molecular power generation. Proc Natl Acad Sci USA, 121(9)::e2315472121.
Meller A, Kelly D, Smith LG, Bowman GR. 2024 Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci, 33(3)::e4902.
Meller A, De Oliveira S, Davtyan A, Abramyan T, Bowman GR, van den Bedem H. 2023 Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors. Front Mol Biosci, 10():1171143.
Meller A, Lotthammer JM, Smith LG, Novak B, Lee LA, Kuhn CC, Greenberg L, Leinwand LA, Greenberg MJ, Bowman GR. 2023 Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains. Elife, 12():e83602.
Meller A, Ward M, Borowsky J, Kshirsagar M, Lotthammer JM, Oviedo F, Ferres JL, Bowman GR. 2023 Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Nat Commun, 14(1):1177.
Meller A, Bhakat S, Solieva S, Bowman GR. 2023 Accelerating Cryptic Pocket Discovery Using AlphaFold. J Chem Theory Comput, (Epub ahead of print):.
Lee LA, Barrick SK, Meller A, Walklate J, Lotthammer JM, Tay JW, Stump WT, Bowman G, Geeves MA, Greenberg MJ, Leinwand LA. 2023 Functional divergence of the sarcomeric myosin, MYH7b, supports species-specific biological roles. J Biol Chem, 299(1):102657.
Ward MD, Zimmerman MI, Meller A, Chung M, Swamidass SJ, Bowman GR. 2021 Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets. Nat Commun, 12(1):3023.
Zimmerman MI, Porter JR, Ward MD, Singh S, Vithani N, Meller A, Mallimadugula UL, Kuhn CE, Borowsky JH, Wiewiora RP, Hurley MFD, Harbison AM, Fogarty CA, Coffland JE, Fadda E, Voelz VA, Chodera JD, Bowman GR. 2021 SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat Chem, 13(7):651-59.
Porter JR, Meller A, Zimmerman MI, Greenberg MJ, Bowman GR. 2020 Conformational distributions of isolated myosin motor domains encode their mechanochemical properties. Elife, 9():e55132.