Improving force field accuracy by training against condensed-phase mixture properties

Boothroyd S, Madin OC, Mobley DL, Wang L-P, Chodera JD, and Shirts MR
Journal of Chemical Theory and Computation 18:3577, 2022 [DOI] [GitHub]

We use a new automated framework for physical property evaluation and fitting to show how molecular mechanics force fields can be systematically improved by fitting to condensed phase properties.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

Grosjean H, Isik M, Aimon A, Mobley D, Chodera JD, von Delft F, and Biggin PC
Journal of Computer-Aided Molecular Design 36:291, 2022 [DOI]

We field a blind community challenge to assess how well state of the art computational chemistry methods can predict the binding modes of small druglike fragments to a protein target for which no chemical matter is known, PHIP2, using fragment screening at the Diamond Light Source.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding

Ackloo S, Al-awar R, Amaro RE, Arrowsmith CH, Azevedo H, Batey RA, Bengio Y, Betz UAK, Bologa CG, Chodera JD, Cornell WD, Dunham I, Ecker GF, Edfeldt K, Edwards AM, Gilsom MK, Gordijo CR, Hessler G, Hillisch A, Hogner A, Irwin JJ, Jansen JM, Kuhn D, Leach AR, Lee AA, Lessel U, Moult J, Muegge I, Oprea TI, Perry BG, Riley, Singh Saikantendu K, Santhakumar V, Schapira M, Scholten C, Todd MH, Vedadi M, Volkamer A, and Wilson TM
Nature Reviews Chemistry 6:287, 2022 [DOI]

We describe CACHE: A new public-private partnership that aims to transform computer-aided drug discovery much the way that CASP transformed protein structure prediction into a reproducible, accurate engineering discipline.

INK4 tumor suppressor proteins mediate resistance to CDK4/6 kinase inhibitors

Li Q, Jiang B, Guo J, Shao H, Del Priore IS, Chang Q, Kudo R, Li Z, Razavi P, Liu B, Boghossian AS, Rees MG, Ronan MM, Roth JA, Donovan KA, Palafox M, Reis-Filho JS, de Stanchina E, Fischer ES, Rosen N, Serra V, Koff A, Chodera JD, Gray NS, and Chandardlapaty S
Cancer Discovery} 12:356, 2022 [DOI]

We demonstrate CDK6 causes drug resistance by binding INK4 proteins, and develop bifunctional degraders conjugating palbociclib with E3 ligands to overcome this mechanism of resistance.

Capturing non-local through-bond effects in molecular mechanics force fields: II. Using fractional bond orders to fit torsion parameters

Stern CD, Maat J, Dotson DL, Bayly CI, Smith DGA, Mobley DL, and Chodera JD
preprint: [bioRxiv]

We show how the Wiberg Bond Order (WBO) can be used to accurately interpolate torsional profiles for molecular mechanics force fields, which holts the potential for drastically reducing the complexity of these force fields while increasing their ability to generalize and accurately treat complex druglike molecules such as kinase inhibitors.

GCN2 kinase activation by ATP-competitive kinase inhibitors

Mellinghoff I, Tang CP, Clark O, Ferrarone J, Campos C, Lalani AS, Chodera JD, Intlekofer AM, and Elemento O
Nature Chemical Biology}, 18:207, 2022 [DOI]

We describe paradoxical activation of GCN2 kinase activity by the kinase inhibitor neratinib, and propose a model for how inhibitor-induced dimerization might cause this unusual activity.

Quantum chemistry common driver and databases (QCDB) and quantum chemistry engine (QCEngine): Automation and interoperability among computational chemistry programs

Smith DGA, Lolinco AT, Glick ZL, Lee J, Alenaizan A, Barnes TA, Borca CH, Di Remigio R, Dotson DL, Ehlert S, Heide AG, Herbst MF, Hermann J, Hicks CB, Horton JT, Hurtado AG, Kraus P, Kruse P, Lee SJR, Misiewicz JP, Naden LN, Ramezanghorbani F, Scheurer M, Shriber JB, Simmonett AC, Steinmetzer J, Wagner JR, Ward L, Welborn M, Altarawy D, Anwar J, Chodera JD, Dreuw A, Kulik HJ, Liu F, Martinez TJ, Matthews DA, Schaefer III HF, Sponer J, Turney JM, Wang L-P, De Silva N, King RA, Stanton JF, Gordon MS, Windus TL, Sherrill CD, Burns LA
Journal of Chemical Physics} 155:204801, 2021 [DOI]

We describe a new community-wide approach to interoperability for quantum chemistry packages that will enable large-scale applications such as next-generation machine learning for chemistry and automated force field construction for drug discovery.

Teaching free energy calculations to learn from experimental data

Marcus Wieder, Josh Fass, and John Chodera
[bioRxiv] [code] [data]

We show, for the first time, how alchemical free energy calculations can be used to not only compute free energy differences between small molecules involving covalent bond rearrangements in systems treated entirely with quantum machine learning potentials, but that these calculations have the capacity to learn to efficiently generalize from conditioning on experimental free energy data.

The Open Force Field Evaluator: An automated, efficient, and scalable framework for the estimation of physical properties from molecular simulation

Simon Boothroyd, Lee-Ping Wang, David L. Mobley, John D. Chodera, and Michael R. Shirts

Preprint ahead of submission: [ChemRxiv]

We describe a new software framework for automated evaluation of physical properties for the benchmarking and optimization of small molecule force fields according to best practices.

Antibodies to the SARS-CoV-2 receptor-binding domain that maximize breadth and resistance to viral escape

Tyler N Starr, Nadine Czudnochowski, Fabrizia Zatta, Young-Jun Park, Zhuoming Liu, Amin Addetia, Dora Pinto, Martina Beltramello, Patrick Hernandez, Allison J Greaney, Roberta Marzi, William G Glass, Ivy Zhang, Adam S Dingens, John E Bowen, Jason A Wojcechowskyj, Anna De Marco, Laura E Rosen, Jiayi Zhou, Martin Montiel-Ruiz, Hannah Kaiser, Heather Tucker, Michael P Housley, Julia Di Iulio, Gloria Lombardo, Maria Agostini, Nicole Sprugasci, Katja Culap, Stefano Jaconi, Marcel Meury, Exequiel Dellota, Elisabetta Cameroni, Tristan I Croll, Jay C Nix, Colin Havenar-Daughton, Amalio Telenti, Florian A Lempp, Matteo Samuele Pizzuto, John D Chodera, Christy M Hebner, Sean PJ Whelan, Herbert W Virgin, David Veesler, Davide Corti, Jesse D Bloom, Gyorgy Snell
Nature, in press. [DOI] [bioRxiv] [GitHub]

We comprehensively characterize escape, breadth, and potency across a panel of SARS-CoV-2 antibodies targeting the receptor binding domain, including the parent antibody of the recently approved Vir antibody drug (Sotrovimab), illuminating escape mutations with structural and dynamic insight into their mechanism of action.

Mutation in Abl kinase with altered drug binding kinetics indicates a novel mechanism of imatinib resistance

Agatha Lyczek, Benedict Tilman Berger, Aziz M Rangwala, YiTing Paung, Jessica Tom, Hannah Philipose, Jiaye Guo, Steven K Albanese, Matthew B Robers, Stefan Knapp, John D Chodera, Markus A Seeliger
Preprint ahead of publication: [bioRxiv]

Here, we characterize the biophysical mechanisms underlying mutants of Abl kinase associated with clinical drug resistance to targeted cancer therapies. We uncover a surprising novel mechanism of mutational resistance to kinase inhibitor therapy in which the off-rate for inhibitor unbinding is increased without affecting inhibitor affinity.

A white-knuckle ride of open COVID drug discovery

Frank von Delft, Mark Calmiano, John Chodera, Ed Griffen, Alpha Lee, Nir London, Tatiana Matviuk, Ben Perry, Matt Robinson, and Annette von Delft.
Nature 594:330, 2021.
[DOI] [PDF]

The COVID Moonshot is an open science effort to discover a direct-acting SARS-CoV-2 oral antiviral. Here, we share lessons from this effort, including the missed opportunity to develop a phase 2 ready drug more than a decade ago that could have halted the COVID-19 pandemic in its tracks.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks

David F Hahn, Christopher I Bayly, Hannah E Bruce Macdonald, John D Chodera, Antonia SJS Mey, David L Mobley, Laura Perez Benito, Christina EM Schindler, Gary Tresadern, Gregory L Warren
Preprint ahead of publication: [arXiv] [GitHub]

This living best practices paper for the Living Journal of Computational Molecular Sciences describes the current community consensus in how to curate experimental benchmark data for assessing predictive affinity models for drug discovery, how to prepare these systems for affinity calculations, and how to assess the results to compare performance.

SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome

Maxwell I. Zimmerman, Justin R. Porter, Michael D. Ward, Sukrit Singh, Neha Vithani, Artur Meller, Upasana L. Mallimadugula, Catherine E. Kuhn, Jonathan H. Borowsky,  Rafal P. Wiewiora, Matthew F. D. Hurley, Aoife M Harbison, Carl A Fogarty, Joseph E. Coffland, Elisa Fadda, Vincent A. Voelz, John D. Chodera, Gregory R. Bowman.
Nature Chemistry 13:651, 2021. [DOI] [bioRxiv] [data] [FAH/MolSSI COVID-19 data sharing site]

To accelerate a multitude of drug development activities to combat the global threat posed by COVID-19, over a million citizen scientists have banded together through the Folding@home distributed computing project to create the world’s first Exascale computer and simulate protein dynamics. An unprecedented 0.1 seconds of simulation of the viral proteome reveal how the spike complex uses conformational masking to evade an immune response, conformational changes implicated in the function of other viral proteins, and cryptic pockets that are absent in experimental structures. These structures and mechanistic insights present new targets for the design of therapeutics..

Bayesian inference-driven model parameterization and model selection for 2CLJQ fluid models

Owen C Madin, Simon Boothroyd, Richard A Messerly, John D Chodera, Josh Fass, and Michael R Shirts
Preprint ahead of publication: [arXiv]

Here, we show how Bayesian inference can be used to automatically perform model selection and fit parameters for a molecular mechanics force field.

Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model

Aaron Morris, William McCorkindale, the COVID Moonshot Consortium, Nir Drayman, John D Chodera, Savaş Tay, Nir London, and Alpha A. Lee.
Chemical Communications 57:5909, 2021
[DOI]

We show how a machine learning models of ligand affinity can be coupled to synthetic enumeration models to rapidly generate potent inhibitors of the SARS-CoV-2 main viral protease.

What Markov State Models can and cannot do: Correlation versus path-based observables in protein-folding models

Ernesto Suárez, Rafal P Wiewiora, Chris Wehmeyer, Frank Noé, John D Chodera, Daniel M Zuckerman
Journal of Chemical Theory and Computation 17:3119, 2021
[DOI] [PDF] [bioRxiv] [GitHub]

Markov state models are now well-established for describing the long-time conformational dynamics of proteins. Here, we take a critical look of what properties can reliably be extracted from these coarse-grained models.

Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity

Emma C. Thompson, Laura E. Rosen, James G. Shepherd, Robert Spreafico, Ana da Silva Filipe, Jason A. Wojcechowskyj, Chris Davis, Luca Piccoli, David J. Pascall, Josh Dillen, Spyros Lytras, Nadine Czudnochowski, Rajiv Shah, Marcel Meury, Natasha Jesudason, Anna De Marco, Kathy Li, Jessia Bassi, Aine O’Toole, Dora Pinto, Rachel M. Colqohoun, Katja Culap, Ben Jackson, Fabrizia Zatta, Andrew Rambaut, Stefano Jaconi, Vattipali B. Sreenu, Jay Nix, Ivy Zhang, Ruth F. Jarrett, William G. Glass, Martina Beltramello, Kyriaki Nomikou, Matteo Pizzuto, Lily Tong, Elisabetta Cameroni, Tristan I. Croll, Natasha Johnson, Julia Di Iulio, Arthur Wickenhagen, Alessandro Ceschi, Aoife M. Harbison, Daniel Mair, Paolo Ferrari, Katherine Smollett, Federica Sallusto, Stephen Carmichael, Christian Garzoni, Jenna Nichols, Massimo Galli, Joseph Hughes, Agostino Riva, Antonia Ho, Marco Schiuma, Malcolm G. Semple, Peter J. M. Openshaw, Elisa Fadda, J. Kenneth Baillie, John D. Chodera, The ISARIC4C Investigators, the COVID-19 Genomics UK (COG-UK) consortium, Suzannah J. Rihn, Samantha J. Lycett, Herbert W. Virgin, Amalio Telenti, Davide Corti, David L. Robertson, and Gyorgy Snell.

Cell 184:1171, 2022. [DOI] [PDF] [bioRxiv] [Supplementary Info] [Folding@home data]

New mutations that enhance the affinity of SARS-CoV-2 spike protein for human ACE2—and potentially pose threats to antibody-based therapeutics and vaccines for COVID-19—are already emerging in the wild. We characterize and describe sentinel mutations of SARS-CoV-2 in the wild that herald challenges for combatting COVID-19, and use simulations of the RBD-ACE2 interface on Folding@home to biophysically characterize why these mutations can lead to enhanced affinity.

Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions

Mehtap Işık, Ariën S Rustenburg, Andrea Rizzi, Marilyn R Gunner, David L Mobley, John D Chodera
Journal of Computer-Aided Molecular Design 35:131, 2021
[DOI] [bioRxiv] [GitHub] [manuscript and figure sources]

The SAMPL6 pKa challenge assessed the ability of the computational chemistry community to predict macroscopic and microscopic pKas for a set of druglike molecules resembling kinase inhibitors. This paper reports on the overall performance and lessons learned, including the surprising finding that many tools predict reasonably accurate macroscopic pKas corresponding to the wrong microscopic protonation sites.

Development and benchmarking of Open Force Field v1.0.0, the Parsley small molecule force field

Yudong Qiu, Daniel Smith, Simon Boothroyd, Hyesu Jang, Jeffrey Wagner, Caitlin C Bannan, Trevor Gokey, Victoria T Lim, Chaya Stern, Andrea Rizzi, Xavier Lucas, Bryon Tjanaka, Michael R Shirts, Michael Gilson, John D. Chodera, Christopher I Bayly, David Mobley, Lee-Ping Wang
Preprint ahead of publication: [chemRxiv] [force fields] [Open Force Field Initiative]

We present a new, modern small molecule force field for molecular design from the Open Force Field Initiative, a large industry-academic collaboration that focuses on open science, open data, and modern open source infrastructure.