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

How Robust Is the Ligand Binding Transition State?

Bose S, Lotz SD, Deb I, Shuck M, Lee KSS, Dickson A*. J. Am. Chem. Soc.. (2023)

For many drug targets, it has been shown that the kinetics of drug binding (e.g., on rate and off rate) is more predictive of drug efficacy than thermodynamic quantities alone. This motivates the development of predictive computational models that can be used to optimize compounds on the basis of their kinetics. The structural details underpinning these computational models are found not only in the bound state but also in the short-lived ligand binding transition states. Although transition states cannot be directly observed experimentally due to their extremely short lifetimes, recent successes have demonstrated that modeling the...

Flexible Topology: A Dynamic Model of a Continuous Chemical Space

Donyapour N, Fathi Niazi F, Roussey N, Bose S, Dickson A*. J. Chem. Theory Comput.. (2023)

Ligand design problems involve searching chemical space for a molecule with a set of desired properties. As chemical space is discrete, this search must be conducted in a pointwise manner, separately investigating one molecule at a time, which can be inefficient. We propose a method called “Flexible Topology”, where a ligand is composed of a set of shapeshifting “ghost” atoms, whose atomic identities and connectivity can dynamically change over the course of a simulation. Ghost atoms are guided toward their target positions using a translation-, rotation-, and index-invariant restraint potential. This is the first step toward...

Adrenomedullin 2/intermedin is a slow off-rate, long-acting endogenous agonist of the adrenomedullin2 G protein-coupled receptor

Babin KM, Karim JA, Gordon PH, Lennon J, Dickson A*, Pioszak AA*. J. Biol. Chem.. (2023)

The signaling peptides adrenomedullin 2/intermedin (AM2/IMD), adrenomedullin (AM), and CGRP have overlapping and distinct functions in the cardiovascular, lymphatic, and nervous systems by activating three shared receptors comprised of the class B GPCR CLR in complex with a RAMP1, −2, or −3 modulatory subunit. Here, we report that AM2/IMD, which is thought to be a non-selective agonist, is kinetically selective for CLR-RAMP3, known as the AM2R. AM2/IMD-AM2R elicited substantially longer duration cAMP signaling than the eight other peptide-receptor combinations due to AM2/IMD slow off-rate binding kinetics. The regions responsible for the slow off-rate were mapped to...

Quality over quantity: Sampling high probability rare events with the weighted ensemble algorithm

Roussey NM, Dickson A*. J. Comp. Chem.. (2022)

The prediction of (un)binding rates and free energies is of great significance to the drug design process. Although many enhanced sampling algorithms and approaches have been developed, there is not yet a reliable workflow to predict these quantities. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the WE algorithm “Resampling of Ensembles by Variation Optimization”, or “REVO”. Here, we calculate binding free energies retrospectively for three SAMPL6 host-guest systems and prospectively for a SAMPL9 system to test a modification...

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

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, Zhang X, Razavi AM, Kolossváry I, Imbriglio J, Evdokimov A, Bergeron L, Zhou W, Adhikari J, Ruprecht B, Dickson A*, Xu H*, Sherman W*, Izaguirre JA*. Nat. Commun.. (2022)

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity...

A biosensor of protein foldedness identifies increased holdase activity of chaperones in the nucleus following increased cytosolic protein aggregationPredicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

Raeburn CB, Ormsby AR, Cox D, Gerak CA, Makhoul C, Moily NS, Ebbinghaus S, Dickson A, McColl G, Hatters DM*. J. Biol. Chem.. (2022)

Chaperones and other quality control machinery guard proteins from inappropriate aggregation, which is a hallmark of neurodegenerative diseases. However, how the systems that regulate the foldedness of the proteome remain buffered under stress conditions and in different cellular compartments remains incompletely understood. In this study, we applied a FRET-based strategy to explore how well quality control machinery protects against the misfolding and aggregation of bait biosensor proteins, made from the prokaryotic ribonuclease barnase, in the nucleus and cytosol of human embryonic kidney 293T cells. We found that those barnase biosensors were prone to misfolding, were less...

Local Ion Densities can Influence Transition Paths of Molecular Binding

Roussey NM, Dickson A*. Frontiers Mol. Biosci.. (2022)

Improper reaction coordinates can pose significant problems for path-based binding free energy calculations. Particularly, omission of long timescale motions can lead to over-estimation of the energetic barriers between the bound and unbound states. Many methods exist to construct the optimal reaction coordinate using a pre-defined basis set of features. Although simulations are typically conducted in explicit solvent, the solvent atoms are often excluded by these feature sets—resulting in little being known about their role in reaction coordinates, and ultimately, their role in determining (un)binding rates and free energies. In this work, analysis is done on an...

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...