Research Overview

At the atomic level, the molecules in our bodies are in constant motion, and undergoing constant change. The motions are incredibly rich; they range from the isomerization of side-chains, to the formation and destruction of large intermolecular complexes, to the birth and death of the molecules themselves. A deep understanding of these motions can radically improve our understanding of health and disease through rational design, where drugs target specific receptors chosen for a specific molecular impact.

The Dickson laboratory uses computational techniques such as molecular dynamics to simulate the motions of biomolecules (protein, RNA and DNA). These numerical experiments extend our knowledge beyond the "snapshots" provided by X-ray crystallography and NMR, and provide the entire landscape of conformations accessible to a molecular system. Our goal is to use simulations to gain a deep understanding of the ligand binding process, and use this knowledge to aid ongoing drug discovery efforts.

We also use larger-scale network models of biological processes to gain understanding for processes that involve many different molecular species, such as chaperone action in the cell. This allows a much broader reach, and can synthesize findings from simulation and experiment into a coherent biological model. Working in both worlds simultaneously allows for a multiscale disease-targeting strategy that is detailed enough to capture atomic-level perturbations, and broad enough to capture the cell-level consequences of disease.

Recent Publications

Atomic-Resolution Prediction of Degrader-mediated Ternary Complex Structures by Combining Molecular Simulations with Hydrogen Deuterium Exchange

Dixon T, MacPherson D, Mostofian B, Dauzhenka T, Lotz S, McGee D, Shechter S, Shrestha UR, Wiewiora R, McDargh ZA, Pei F, Pal R, Ribeiro JV, Wilkerson T, Sachdeva V, Gao N, Jain S, Sparks S, Li Y, Vinitsky A, Razavi AM, Kolossváry I, Imbriglio J, Evdokimov A, Bergeron L, Dickson A*, Xu H*, Sherman W*, Izaguirre JA*. bioRxiv. (2021)

Targeted protein degradation (TPD) has recently emerged as a powerful approach for removing (rather than inhibiting) proteins implicated in diseases. A key step in TPD is the formation of an induced proximity complex where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process using atomistic simulations: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) degradation efficiency...

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

Uyar A and Dickson A. Journal of Chemical Theory and Computation. (2021)

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein–protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated...

Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method

Donyapour N and Dickson A. Journal of Computer-Aided Molecular Design. (2021)

The prediction of log P values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard–Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a “master” dataset comprised of over 41,000 unique log P values. The specific molecular targets in the...

ClassicalGSG: Prediction of logP Using Classical Molecular Force Fields and Geometric Scattering for Graphs

Donyapour N, Hirn MJ and Dickson A. Journal of Computational Chemistry. (2021)

This work examines methods for predicting the partition coefficient (logP) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic features are transformed into index-invariant molecular features using a recently developed method called Geometric Scattering for Graphs (GSG). We call this approach “ClassicalGSG” and examine its performance under a broad range of conditions and hyperparameters. We train a ClassicalGSG logP predictor with neural networks using 10,722 molecules from the ChEMBL21 dataset and apply it...

Membrane-mediated ligand unbinding of the PK-11195 ligand from TSPO

Dixon T, Uyar A, Ferguson-Miller S and Dickson A. Biophysical Journal. (2020)

The translocator protein (TSPO), previously known as the peripheral benzodiazepine receptor, is of longstanding medical interest as both a biomarker for neuroinjury and a potential drug target for neuroinflammation and other disorders. Recently it was shown that ligand residence time is a key factor determining steroidogenic efficacy of TSPO-binding compounds. This spurs interest in simulations of (un)binding pathways of TSPO ligands, which could reveal the molecular interactions governing ligand residence time. In this study, we use a weighted ensemble algorithm to determine the unbinding pathway for different poses of PK-11195, a TSPO ligand used in neuroimaging....

Wepy: A Flexible Software Framework for Simulating Rare Events with Weighted Ensemble Resampling

Lotz, SD and Dickson A. ACS Omega. (2020)

Here we introduce the open-source software framework wepy ( which is a toolkit for running and analyzing weighted ensemble (WE) simulations. The wepy toolkit is in pure Python and as such is highly portable and extensible making it an excellent platform to develop and use new WE resampling algorithms such as WExplore, REVO, and others while leveraging the entire Python ecosystem. In addition, wepy simplifies WE-specific analyses by defining out-of-core tree-like data structures using the cross-platform HDF5 file format. In this paper we discuss the motivations and challenges for simulating rare-events in biomolecular systems. As has...

On Calculating Free Energy Differences Using Ensembles of Transition Paths

Hall R., Dixon T. and Dickson A.*. Front. Mol. Biosci.. (2020)

The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (kon) and dissociation (koff)—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate...

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi A, Jensen T, Slochower DR, Aldeghi M, Gapsys V, Ntekoumes D, Bosisio S, Papadourakis M, Henriksen NM, de Groot BL, Cournia Z, Dickson A, Michel J, Gilson MK, Shirts MR, Mobley DL, Chodera JD. J. Comp. Aided Drug Design. (2020)

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We...

Enhanced Jarzynski free energy calculations using weighted ensemble

Roussey NM and Dickson A. Journal of Chemical Physics. (2020)

The free energy of transitions between stable states is the key thermodynamic quantity that governs the relative probabilities of the forward and reverse reactions and the ratio of state probabilities at equilibrium. The binding free energy of a drug and its receptor is of particular interest, as it serves as an optimization function for drug design. Over the years, many computational methods have been developed to calculate binding free energies, and while many of these methods have a long history, issues such as convergence of free energy estimates and the projection of a binding process onto...

A Suite of Tutorials for the WESTPA Rare-Events Sampling Software [Article v1.0]

Bogetti AT, Mostofian B, Dickson A, Pratt AJ, Saglam AS, Harrison PO, Adelman JL, Dudek M, Torrillo PA, DeGrave AJ, Adhikari U, Zwier MC, Zuckerman DM, Chong LT. LiveCoMS. (2019)

The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present five tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. Users are expected to already have significant experience with running standard molecular dynamics simulations using the underlying dynamics engine of interest (e.g. Amber, Gromacs, OpenMM). The tutorials range from a molecular association process in explicit solvent to...

REVO: Resampling of ensembles by variation optimization

Donyapour N., Roussey N.M. and Dickson A.*. J. Chem. Phys.. (2019)

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems, this occurs on prohibitively long time scales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here, we propose...

Markov-State Transition Path Analysis of Electrostatic Channeling

Liu Y, Hickey DP, Minteer SD, Dickson A*, Barton SC*. J. Phys. Chem. C. (2019)

Electrostatic channeling is a naturally-occurring approach to control the flux of charged intermediates in catalytic cascades. Computational techniques have enabled quantitative understanding of such mechanisms, augmenting experimental approaches by modeling molecular interactions in atomic detail. In this work, we report the first utilization of a Markov State Model (MSM) to describe the surface diffusion of a reaction intermediate, glucose 6-phosphate, on an artificially modified cascade where hexokinase and glucose-6-phosphate dehydrogenase are covalently conjugated by a cationic oligopeptide bridge. Conformation space networks are used to represent intermediate transport on enzyme surfaces, along with committor probabilities that assess...