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

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

Donyapour N, Hirn MJ and Dickson A. ChemRxiv (preprint). (2020)

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 (https://github.com/ADicksonLab/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...

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...

Selectivity, ligand deconstruction, and cellular activity analysis of a BPTF bromodomain inhibitor

Kirberger SE, Ycas PD, Johnson JA, Chen C, Ciccone M, Lu RWW, Urick AK, Zahid H, Shi K, Aihara H, McAllister S, Kashani-Sabet M, Shi J, Dickson A, dos Santos CO* and Pomerantz W*. Org. Biomol. Chem.. (2019)

Bromodomain and PHD finger containing protein transcription factor (BPTF) is an epigenetic protein involved in chromatin remodelling and is a potential anticancer target. The BPTF bromodomain has one reported small molecule inhibitor (AU1, rac-1). Here, advances made on the study of rac-1 are reported, specifically, identification of the active enantiomer for binding to the bromodomain, and reduction in off-target binding to known kinase targets. Additionally, a ligand deconstruction analysis was conducted to characterize important pharmacophores for engaging the BPTF bromodomain. These studies have been enabled by a protein-based fluorine NMR approach, highlighting the versatility of the...

Mapping the Ligand Binding Landscape

Dickson A*. Biophysical Journal. (2018)

The interaction between a ligand and a protein involves a multitude of conformational states. To achieve a particular deeply-bound pose the ligand must search across a rough free energy landscape, with many metastable minima. Creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The WExplore enhanced sampling method is well-suited to build these maps, as it is designed to broadly explore free-energy landscapes, and is capable of simulating ligand release pathways that occur on timescales as long...

Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge

Dixon T, Lotz SD and Dickson A*. Journal of Computer-Aided Molecular Design. (2018)

Interest in ligand binding kinetics has been growing rapidly, as it is being discovered in more and more systems that ligand residence time is the crucial factor governing drug efficacy. Many enhanced sampling methods have been developed with the goal of predicting ligand binding rates (kon) and/or ligand unbinding rates (koff) through explicit simulation of ligand binding pathways, and these methods work by very different mechanisms. Although there is not yet a blind challenge for ligand binding kinetics, here we take advantage of experimental measurements and rigorously computed benchmarks to compare estimates of KD calculated as...

Structural Insights into Lethal Contractural Syndrome Type 3 (LCCS3) Caused by a Missense Mutation of PIP5Ky

Xuaunkun Zeng, Arzu Uyar, Dexin Sui, Nazanin Donyapour, Dianqing Wu, Alex Dickson, Jian Hu. Biochemical Journal. (2018)

Signaling molecule phosphatidylinositol 4,5-bisphosphate (PIP2) is produced primarily by phosphatidylinositol 4-phosphate 5-kinase (PIP5K). PIP5K is essential for the development of the human neuronal system, which has been exemplified by a recessive genetic disorder, lethal congenital contractural syndrome type 3 (LCCS3), caused by a single aspartate-to-asparagine mutation in the kinase domain of PIP5Ky. So far, the exact role of this aspartate residue has yet to be elucidated. In this work, we conducted structural, functional and computational studies on a zebrafish PIP5Ka variant with a mutation at the same site. Compared with the structure of the wild type...