Rommie E. Amaro

Find an error

Name: Amaro, Rommie E.

Organization: University of California , USA

Department: Department of Chemistry and Biochemistry

Title: Assistant(PhD)

Chemicals
References

POVME 3.0: Software for Mapping Binding Pocket Flexibility

Co-reporter:Jeffrey R. Wagner, Jesper Sørensen, Nathan Hensley, Celia Wong, Clare Zhu, Taylor Perison, and Rommie E. Amaro

Journal of Chemical Theory and Computation September 12, 2017 Volume 13(Issue 9) pp:4584-4584

Publication Date(Web):August 11, 2017

DOI:10.1021/acs.jctc.7b00500

We present a substantial update to the open-source POVME binding pocket analysis software. New capabilities of POVME 3.0 include a flexible chemical coloring scheme for feature identification, postanalysis tools for comparing large ensembles of pockets (e.g., from molecular dynamics simulations), and the introduction of scripts and methods that facilitate binding pocket comparison and analysis. We envision the use of this software for visualization of binding pocket dynamics, selection of representative structures for ensemble docking, and incorporation of molecular dynamics results into ligand design efforts.

Elements of Nucleotide Specificity in the Trypanosoma brucei Mitochondrial RNA Editing Enzyme RET2

Co-reporter:Özlem Demir and Rommie E. Amaro

Journal of Chemical Information and Modeling May 25, 2012 Volume 52(Issue 5) pp:

Publication Date(Web):April 19, 2012

DOI:10.1021/ci3001327

The causative agent of African sleeping sickness, Trypanosoma brucei, undergoes an unusual mitochondrial RNA editing process that is essential for its survival. RNA editing terminal uridylyl transferase 2 of T. brucei (TbRET2) is an indispensable component of the editosome machinery that performs this editing. TbRET2 is required to maintain the vitality of both the insect and bloodstream forms of the parasite, and with its high-resolution crystal structure, it poses as a promising pharmaceutical target. Neither the exclusive requirement of uridine 5'-triphosphate (UTP) for catalysis, nor the RNA primer preference of TbRET2 is well-understood. Using all-atom explicitly solvated molecular dynamics (MD) simulations, we investigated the effect of UTP binding on TbRET2 structure and dynamics, as well as the determinants governing TbRET2’s exclusive UTP preference. Through our investigations of various nucleoside triphosphate substrates (NTPs), we show that UTP preorganizes the binding site through an extensive water-mediated H-bonding network, bringing Glu424 and Arg144 side chains to an optimum position for RNA primer binding. In contrast, cytosine 5'-triphosphate (CTP) and adenosine 5'-triphosphate (ATP) cannot achieve this preorganization and thus preclude productive RNA primer binding. Additionally, we have located ligand-binding “hot spots” of TbRET2 based on the MD conformational ensembles and computational fragment mapping. TbRET2 reveals different binding pockets in the apo and UTP-bound MD simulations, which could be targeted for inhibitor design.

Electrostatic Interactions as Mediators in the Allosteric Activation of Protein Kinase A RIα

Co-reporter:Emília P. Barros, Robert D. Malmstrom, Kimya Nourbakhsh, Jason C. Del Rio, Alexandr P. Kornev, Susan S. Taylor, and Rommie E. Amaro

Biochemistry March 14, 2017 Volume 56(Issue 10) pp:1536-1536

Publication Date(Web):February 21, 2017

DOI:10.1021/acs.biochem.6b01152

Close-range electrostatic interactions that form salt bridges are key components of protein stability. Here we investigate the role of these charged interactions in modulating the allosteric activation of protein kinase A (PKA) via computational and experimental mutational studies of a conserved basic patch located in the regulatory subunit’s B/C helix. Molecular dynamics simulations evidenced the presence of an extended network of fluctuating salt bridges spanning the helix and connecting the two cAMP binding domains in its extremities. Distinct changes in the flexibility and conformational free energy landscape induced by the separate mutations of Arg239 and Arg241 suggested alteration of cAMP-induced allosteric activation and were verified through in vitro fluorescence polarization assays. These observations suggest a mechanical aspect to the allosteric transition of PKA, with Arg239 and Arg241 acting in competition to promote the transition between the two protein functional states. The simulations also provide a molecular explanation for the essential role of Arg241 in allowing cooperative activation, by evidencing the existence of a stable interdomain salt bridge with Asp267. Our integrated approach points to the role of salt bridges not only in protein stability but also in promoting conformational transition and function.

Molecular Simulations Reveal an Unresolved Conformation of the Type IA Protein Kinase A Regulatory Subunit and Suggest Its Role in the cAMP Regulatory Mechanism

Co-reporter:Sophia P. Hirakis, Robert D. Malmstrom, and Rommie E. Amaro

Biochemistry August 1, 2017 Volume 56(Issue 30) pp:3885-3885

Publication Date(Web):June 29, 2017

DOI:10.1021/acs.biochem.7b00461

We identify a previously unresolved, unrecognized, and highly stable conformation of the protein kinase A (PKA) regulatory subunit RIα. This conformation, which we term the “Flipback” structure, bridges conflicting characteristics in crystallographic structures and solution experiments of the PKA RIα heterotetramer. Our simulations reveal a hinge residue, G235, in the B/C helix that is conserved through all isoforms of RI. Brownian dynamics simulations suggest that the Flipback conformation plays a role in cAMP association to the A domain of the R subunit.

SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin–Benzamidine Binding

Co-reporter:Lane W. Votapka, Benjamin R. Jagger, Alexandra L. Heyneman, and Rommie E. Amaro

The Journal of Physical Chemistry B April 20, 2017 Volume 121(Issue 15) pp:3597-3597

Publication Date(Web):February 13, 2017

DOI:10.1021/acs.jpcb.6b09388

We present the Simulation Enabled Estimation of Kinetic Rates (SEEKR) package, a suite of open-source scripts and tools designed to enable researchers to perform multiscale computation of the kinetics of molecular binding, unbinding, and transport using a combination of molecular dynamics, Brownian dynamics, and milestoning theory. To demonstrate its utility, we compute the kon, koff, and ΔGbind for the protein trypsin with its noncovalent binder, benzamidine, and examine the kinetics and other results generated in the context of the new software, and compare our findings to previous studies performed on the same system. We compute a kon estimate of (2.1 ± 0.3) × 107 M–1 s–1, a koff estimate of 83 ± 14 s–1, and a ΔGbind of −7.4 ± 0.1 kcal·mol–1, all of which compare closely to the experimentally measured values of 2.9 × 107 M–1 s–1, 600 ± 300 s–1, and −6.71 ± 0.05 kcal·mol–1, respectively.

A Kepler Workflow Tool for Reproducible AMBER GPU Molecular Dynamics

Co-reporter:Shweta Purawat, Pek U Ieong, Robert D. Malmstrom, Garrett J. Chan, ... Rommie E. Amaro

Biophysical Journal 2017 Volume 112, Issue 12(Volume 112, Issue 12) pp:

Publication Date(Web):20 June 2017

DOI:10.1016/j.bpj.2017.04.055

With the drive toward high throughput molecular dynamics (MD) simulations involving ever-greater numbers of simulation replicates run for longer, biologically relevant timescales (microseconds), the need for improved computational methods that facilitate fully automated MD workflows gains more importance. Here we report the development of an automated workflow tool to perform AMBER GPU MD simulations. Our workflow tool capitalizes on the capabilities of the Kepler platform to deliver a flexible, intuitive, and user-friendly environment and the AMBER GPU code for a robust and high-performance simulation engine. Additionally, the workflow tool reduces user input time by automating repetitive processes and facilitates access to GPU clusters, whose high-performance processing power makes simulations of large numerical scale possible. The presented workflow tool facilitates the management and deployment of large sets of MD simulations on heterogeneous computing resources. The workflow tool also performs systematic analysis on the simulation outputs and enhances simulation reproducibility, execution scalability, and MD method development including benchmarking and validation.

A Reflection on Klaus Schulten

Co-reporter:Rommie E. Amaro

Journal of Chemical Theory and Computation 2017 Volume 13(Issue 1) pp:

Publication Date(Web):December 6, 2016

DOI:10.1021/acs.jctc.6b01111

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research

Co-reporter:Shweta Purawat, Charles Cowart, Rommie E. Amaro, Ilkay Altintas

Journal of Computational Science 2017 Volume 20(Volume 20) pp:

Publication Date(Web):1 May 2017

DOI:10.1016/j.jocs.2017.03.010

•A framework for open knowledge dissemination in the biomedical community is presented.•The BBDTC enables course content personalization through the playlist feature.•It supports cross-platform operability to reduce effort duplication and encourage innovation.•The BBDTC exploits virtualization technologies to enable smooth user experience.•The framework tracks content consumption and usability through usage statistics and user scenarios.The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC is an e-learning platform that empowers the biomedical community to develop, launch and share open training materials. It deploys hands-on software training toolboxes through virtualization technologies such as Amazon EC2 and Virtualbox. The BBDTC facilitates migration of courses across other course management platforms. The framework encourages knowledge sharing and content personalization through the playlist functionality that enables unique learning experiences and accelerates information dissemination to a wider community.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Co-reporter:Jeffrey R. Wagner, Christopher T. Lee, Jacob D. Durrant, Robert D. Malmstrom, Victoria A. Feher, and Rommie E. Amaro

Chemical Reviews 2016 Volume 116(Issue 11) pp:6370

Publication Date(Web):April 13, 2016

DOI:10.1021/acs.chemrev.5b00631

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations

Co-reporter:Tavina L. Offutt, Robert V. Swift, and Rommie E. Amaro

Journal of Chemical Information and Modeling 2016 Volume 56(Issue 10) pp:1923-1935

Publication Date(Web):September 23, 2016

DOI:10.1021/acs.jcim.6b00261

In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas. However, it remains difficult to predict a priori when an MD conformation will outperform a VS against the crystal structure alone. Here, we assess whether MD conformations result in improved VS performance for six protein kinases. MD conformations are selected using three different methods, and their VS performances are compared to the corresponding crystal structures. Additionally, these conformations are used to train ensembles, and their VS performance is compared to the individual MD conformations and the corresponding crystal structures using receiver operating characteristic curve (ROC) metrics. We show that performing MD results in at least one conformation that offers better VS performance than the crystal structure, and that, while it is possible to train ensembles to outperform the crystal structure alone, the extent of this enhancement is target dependent. Lastly, we show that the optimal structural selection method is also target dependent and recommend optimizing virtual screens on a kinase-by-kinase basis to improve the likelihood of success.

Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds

Co-reporter:Christopher T. Lee, Jeffrey Comer, Conner Herndon, Nelson Leung, Anna Pavlova, Robert V. Swift, Chris Tung, Christopher N. Rowley, Rommie E. Amaro, Christophe Chipot, Yi Wang, and James C. Gumbart

Journal of Chemical Information and Modeling 2016 Volume 56(Issue 4) pp:721-733

Publication Date(Web):April 4, 2016

DOI:10.1021/acs.jcim.6b00022

Predicting the rate of nonfacilitated permeation of solutes across lipid bilayers is important to drug design, toxicology, and signaling. These rates can be estimated using molecular dynamics simulations combined with the inhomogeneous solubility-diffusion model, which requires calculation of the potential of mean force and position-dependent diffusivity of the solute along the transmembrane axis. In this paper, we assess the efficiency and accuracy of several methods for the calculation of the permeability of a model DMPC bilayer to urea, benzoic acid, and codeine. We compare umbrella sampling, replica exchange umbrella sampling, adaptive biasing force, and multiple-walker adaptive biasing force for the calculation of the transmembrane PMF. No definitive advantage for any of these methods in their ability to predict the membrane permeability coefficient Pm was found, provided that a sufficiently long equilibration is performed. For diffusivities, a Bayesian inference method was compared to a generalized Langevin method, both being sensitive to chosen parameters and the slow relaxation of membrane defects. Agreement within 1.5 log units of the computed Pm with experiment is found for all permeants and methods. Remaining discrepancies can likely be attributed to limitations of the force field as well as slowly relaxing collective movements within the lipid environment. Numerical calculations based on model profiles show that Pm can be reliably estimated from only a few data points, leading to recommendations for calculating Pm from simulations.

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

Co-reporter:Robert V. Swift; Siti A. Jusoh; Tavina L. Offutt; Eric S. Li

Journal of Chemical Information and Modeling 2016 Volume 56(Issue 5) pp:830-842

Publication Date(Web):April 20, 2016

DOI:10.1021/acs.jcim.5b00684

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2N). A recursive approximation to the optimal solution scales as O(N2), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets.

Two Relations to Estimate Membrane Permeability Using Milestoning

Co-reporter:Lane W. Votapka, Christopher T. Lee, and Rommie E. Amaro

The Journal of Physical Chemistry B 2016 Volume 120(Issue 33) pp:8606-8616

Publication Date(Web):May 6, 2016

DOI:10.1021/acs.jpcb.6b02814

Prediction of passive permeation rates of solutes across lipid bilayers is important to drug design, toxicology, and other biological processes such as signaling. The inhomogeneous solubility-diffusion (ISD) equation is traditionally used to relate the position-dependent potential of mean force and diffusivity to the permeability coefficient. The ISD equation is derived via the Smoluchowski equation and assumes overdamped system dynamics. It has been suggested that the complex membrane environment may exhibit more complicated damping conditions. Here we derive a variant of the inhomogeneous solubility diffusion equation as a function of the mean first passage time (MFPT) and show how milestoning, a method that can estimate kinetic quantities of interest, can be used to estimate the MFPT of membrane crossing and, by extension, the permeability coefficient. We further describe a second scheme, agnostic to the damping condition, to estimate the permeability coefficient from milestoning results or other methods that compute a probability of membrane crossing. The derived relationships are tested using a one-dimensional Langevin dynamics toy system confirming that the presented theoretical methods can be used to estimate permeabilities given simulation and milestoning results.

Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands

Co-reporter:Jacob D. Durrant; Kathryn E. Carlson; Teresa A. Martin; Tavina L. Offutt; Christopher G. Mayne; John A. Katzenellenbogen

Journal of Chemical Information and Modeling 2015 Volume 55(Issue 9) pp:1953-1961

Publication Date(Web):August 18, 2015

DOI:10.1021/acs.jcim.5b00241

The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery process has the potential to accelerate the delivery of new treatments to countless patients in need. “Virtual screening,” wherein molecules are first tested in silico in order to prioritize compounds for subsequent experimental testing, is one such innovation. Although the traditional scoring functions used in virtual screens have proven useful, improved accuracy requires novel approaches. In the current work, we use the estrogen receptor to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates. We describe 39 novel estrogen-receptor ligands identified in silico with experimentally determined Ki values ranging from 460 nM to 20 μM, presented here for the first time.

Application of Molecular-Dynamics Based Markov State Models to Functional Proteins

Co-reporter:Robert D. Malmstrom, Christopher T. Lee, Adam T. Van Wart, and Rommie E. Amaro

Journal of Chemical Theory and Computation 2014 Volume 10(Issue 7) pp:2648-2657

Publication Date(Web):June 4, 2014

DOI:10.1021/ct5002363

Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.

POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics

Co-reporter:Jacob D. Durrant, Lane Votapka, Jesper Sørensen, and Rommie E. Amaro

Journal of Chemical Theory and Computation 2014 Volume 10(Issue 11) pp:5047-5056

Publication Date(Web):September 29, 2014

DOI:10.1021/ct500381c

Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics. Since its release in 2011, the POVME (POcket Volume MEasurer) algorithm has been widely adopted as a simple-to-use tool for measuring and characterizing pocket volumes and shapes. We here present POVME 2.0, which is an order of magnitude faster, has improved accuracy, includes a graphical user interface, and can produce volumetric density maps for improved pocket analysis. To demonstrate the utility of the algorithm, we use it to analyze the binding pocket of RNA editing ligase 1 from the unicellular parasite Trypanosoma brucei, the etiological agent of African sleeping sickness. The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts. We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis

Co-reporter:Adam T. Van Wart, Jacob Durrant, Lane Votapka, and Rommie E. Amaro

Journal of Chemical Theory and Computation 2014 Volume 10(Issue 2) pp:511-517

Publication Date(Web):January 14, 2014

DOI:10.1021/ct4008603

Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.

Computer-Aided Discovery of Trypanosoma brucei RNA-Editing Terminal Uridylyl Transferase 2 Inhibitors

Co-reporter:Özlem Demir;Mehdi Labaied;Chris Merritt;Ken Stuart

Chemical Biology & Drug Design 2014 Volume 84( Issue 2) pp:131-139

Publication Date(Web):

DOI:10.1111/cbdd.12302

Human African trypanosomiasis (HAT) is a major health problem in sub-Saharan Africa caused by Trypanosoma brucei infection. Current HAT drugs are difficult to administer and not effective against all parasite species at different stages of the disease which indicates an unmet pharmaceutical need. TbRET2 is an indispensable enzyme for the parasite and is targeted here using a computational approach that combines molecular dynamics simulations and virtual screening. The compounds prioritized are then tested in T. brucei via Alamar blue cell viability assays. This work identified 20 drug-like compounds which are candidates for further testing in the drug discovery process.

Computation-Guided Discovery of Influenza Endonuclease Inhibitors

Co-reporter:Eric Chen, Robert V. Swift, Nazilla Alderson, Victoria A. Feher, Gen-Sheng Feng, and Rommie E. Amaro

ACS Medicinal Chemistry Letters 2014 Volume 5(Issue 1) pp:61-64

Publication Date(Web):November 3, 2013

DOI:10.1021/ml4003474

Influenza is a global human health threat, and there is an immediate need for new antiviral therapies to circumvent the limitations of vaccination and current small molecule therapies. During viral transcription, influenza incorporates the 5′-end of the host cell’s mRNA in a process that requires the influenza endonuclease. On the basis of recently published endonuclease crystallized structures, a three-dimensional pharmacophore was developed and used to virtually screen 450,000 compounds for influenza endonuclease inhibitors. Of 264 compounds tested in a FRET-based endonuclease-inhibition assay, 16 inhibitors (IC50 < 50 μM) that span 5 molecular classes novel to this endonuclease were found (6.1% hit rate). To determine cytotoxicity and antiviral activity, subsequent cellular assays were performed. Two compounds suppress viral replication with negligible cell toxicity.Keywords: Antiviral; endonuclease; influenza A; pharmacophore; virtual screening;

Molecular Simulations of Aromatase Reveal New Insights Into the Mechanism of Ligand Binding

Co-reporter:Jiho Park, Luke Czapla, and Rommie E. Amaro

Journal of Chemical Information and Modeling 2013 Volume 53(Issue 8) pp:2047-2056

Publication Date(Web):July 17, 2013

DOI:10.1021/ci400225w

CYP19A1, also known as aromatase or estrogen synthetase, is the rate-limiting enzyme in the biosynthesis of estrogens from their corresponding androgens. Several clinically used breast cancer therapies target aromatase. In this work, explicitly solvated all-atom molecular dynamics simulations of aromatase with a model of the lipid bilayer and the transmembrane helix are performed. The dynamics of aromatase and the role of titration of an important amino acid residue involved in aromatization of androgens are investigated via two 250-ns long simulations. One simulation treats the protonated form of the catalytic aspartate 309, which appears more consistent with crystallographic data for the active site, while the simulation of the deprotonated form shows some notable conformational shifts. Ensemble-based computational solvent mapping experiments indicate possible novel druggable binding sites that could be utilized by next-generation inhibitors. In addition, the effects of protonation on the ligand positioning and channel dynamics are investigated using geometrical models that estimate the opening width of critical channels. Significant differences in channel dynamics between the protonated and deprotonated trajectories are exhibited, suggesting that the mechanism for substrate and product entry and the aromatization process may be coupled to a “locking” mechanism and channel opening. Our results may be particularly relevant in the design of novel drugs, which may be useful therapeutic treatments of cancers such as those of the breast and prostate.

Back to the Future: Can Physical Models of Passive Membrane Permeability Help Reduce Drug Candidate Attrition and Move Us Beyond QSPR?

Co-reporter:Robert V. Swift

Chemical Biology & Drug Design 2013 Volume 81( Issue 1) pp:61-71

Publication Date(Web):

DOI:10.1111/cbdd.12074

It is widely recognized that adsorption, distribution, metabolism, excretion, and toxicology liabilities kill the majority of drug candidates that progress to clinical trials. The development of computational models to predict small molecule membrane permeability is therefore of considerable scientific and public health interest. Empirical qualitative structure permeability relationship models of permeability have been a mainstay in industrial applications, but lack a deep understanding of the underlying biologic physics. Others and we have shown that implicit solvent models to predict passive permeability for small molecules exhibit mediocre predictive performance when validated across experimental test sets. Given the vast increase in computer power, more efficient parallelization schemes, and extension of current atomistic simulation codes to general use graphical processing units, the development and application of physical models based on all-atom simulations may now be feasible. Preliminary results from rigorous free energy calculations using all-atom simulations indicate that performance relative to implicit solvent models may be improved, but many outstanding questions remain. Here, we review the current state-of-the-art physical models for passive membrane permeability prediction and present a prospective look at promising new directions for all-atom approaches.

Exploring Residue Component Contributions to Dynamical Network Models of Allostery

Co-reporter:Adam T. VanWart, John Eargle, Zaida Luthey-Schulten, and Rommie E. Amaro

Journal of Chemical Theory and Computation 2012 Volume 8(Issue 8) pp:2949-2961

Publication Date(Web):July 5, 2012

DOI:10.1021/ct300377a

Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.

Magnesium-Induced Nucleophile Activation in the Guanylyltransferase mRNA Capping Enzyme

Co-reporter:Robert V. Swift, Chau D. Ong, and Rommie E. Amaro

Biochemistry 2012 Volume 51(Issue 51) pp:

Publication Date(Web):December 4, 2012

DOI:10.1021/bi301224b

The mRNA guanylyltransferase, or mRNA capping enzyme, cotranscriptionally caps the 5′-end of nascent mRNA with GMP during the second reaction in a set of three enzymatic reactions that result in the formation of an N7-methylguanosine cap during mRNA maturation. The mRNA capping enzyme is characterized, in part, by a conserved lysine nucleophile that attacks the α-phosphorus atom of GTP, forming a lysine–GMP intermediate. Experiments have firmly established that magnesium is required for efficient intermediate formation but have provided little insight into the requirement’s molecular origins. Using empirical and thermodynamic integration pKa estimates, along with conventional molecular dynamics simulations, we show that magnesium binding likely activates the lysine nucleophile by increasing its acidity and by biasing the deprotonated nucleophile into conformations conducive to intermediate formation. These results provide additional functional understanding of an important enzyme in the mRNA transcript life cycle and allow functional analogies to be drawn that affect our understanding of the metal dependence of related superfamily members.

Teach–Discover–Treat (TDT): Collaborative computational drug discovery for neglected diseases

Co-reporter:Johanna M. Jansen, Wendy Cornell, Y. Jane Tseng, Rommie E. Amaro

Journal of Molecular Graphics and Modelling 2012 Volume 38() pp:360-362

Publication Date(Web):September 2012

DOI:10.1016/j.jmgm.2012.07.007

Teach–Discover–Treat (TDT) is an initiative to promote the development and sharing of computational tools solicited through a competition with the aim to impact education and collaborative drug discovery for neglected diseases. Collaboration, multidisciplinary integration, and innovation are essential for successful drug discovery. This requires a workforce that is trained in state-of-the-art workflows and equipped with the ability to collaborate on platforms that are accessible and free. The TDT competition solicits high quality computational workflows for neglected disease targets, using freely available, open access tools.Graphical abstractHighlights► We present a call-to-action for the community in the form of the teach–discover–treat (TDT) initiative. ► TDT will sponsor a 4-category competition to develop state-of-the-art CADD workflows. ► Workflows must use open-access software in order to promote collaboration and innovation. ► We seek high-quality drug discovery tutorials to educate a new cadre of scientists. ► Tutorials will describe CADD workflows developed around targets in neglected diseases.

Molecular Dynamics Analysis of Antibody Recognition and Escape by Human H1N1 Influenza Hemagglutinin

Co-reporter:Pek Ieong, Rommie E. Amaro, Wilfred W. Li

Biophysical Journal (2 June 2015) Volume 108(Issue 11) pp:

Publication Date(Web):2 June 2015

DOI:10.1016/j.bpj.2015.04.025

The antibody immunoglobulin (Ig) 2D1 is effective against the 1918 hemagglutinin (HA) and also known to cross-neutralize the 2009 pandemic H1N1 influenza HA through a similar epitope. However, the detailed mechanism of neutralization remains unclear. We conducted molecular dynamics (MD) simulations to study the interactions between Ig-2D1 and the HAs from the 1918 pandemic flu (A/South Carolina/1/1918, 18HA), the 2009 pandemic flu (A/California/04/2009, 09HA), a 2009 pandemic flu mutant (A/California/04/2009, 09HA_mut), and the 2006 seasonal flu (A/Solomon Islands/3/2006, 06HA). MM-PBSA analyses suggest the approximate free energy of binding (ΔG) between Ig-2D1 and 18HA is −74.4 kcal/mol. In comparison with 18HA, 09HA and 06HA bind Ig-2D1 ∼6 kcal/mol (ΔΔG) weaker, and the 09HA_mut bind Ig-2D1 only half as strong. We also analyzed the contributions of individual epitope residues using the free-energy decomposition method. Two important salt bridges are found between the HAs and Ig-2D1. In 09HA, a serine-to-asparagine mutation coincided with a salt bridge destabilization, hydrogen bond losses, and a water pocket formation between 09HA and Ig-2D1. In 09HA_mut, a lysine-to-glutamic-acid mutation leads to the loss of both salt bridges and destabilizes interactions with Ig-2D1. Even though 06HA has a similar ΔG to 09HA, it is not recognized by Ig-2D1 in vivo. Because 06HA contains two potential glycosylation sites that could mask the epitope, our results suggest that Ig-2D1 may be active against 06HA only in the absence of glycosylation. Overall, our simulation results are in good agreement with observations from biological experiments and offer novel mechanistic insights, to our knowledge, into the immune escape of the influenza virus.

Drug Discovery Gets a Boost from Data Science

Co-reporter:Rommie E. Amaro

Structure (2 August 2016) Volume 24(Issue 8) pp:1225-1226

Publication Date(Web):2 August 2016

DOI:10.1016/j.str.2016.07.003

In this issue of Structure, Schiebel et al. (2016) describe a workflow-driven approach to high-throughput X-ray crystallographic fragment screening and refinement. In doing so, they extend the applicability of X-ray crystallography as a primary fragment-screening tool and show how data science techniques can favorably impact drug discovery efforts.

Model of the Ankyrin and SOCS Box Protein, ASB9, E3 Ligase Reveals a Mechanism for Dynamic Ubiquitin Transfer

Co-reporter:Jamie M. Schiffer, Robert D. Malmstrom, Jonathan Parnell, Cesar Ramirez-Sarmiento, ... Elizabeth A. Komives

Structure (2 August 2016) Volume 24(Issue 8) pp:1248-1256

Publication Date(Web):2 August 2016

DOI:10.1016/j.str.2016.05.016

•Protein-protein docked models of ASB9-CK align with new ITC and SAXS data•Simulations from these models converge to a similar complex interface•Key residues in this interface are validated with mutagenesis and pull-down assays•A dominant mode of motion of the CK-targeting E3 ligase is revealedCullin-RING E3 ligases (CRLs) are elongated and bowed protein complexes that transfer ubiquitin over 60 Å to proteins targeted for proteasome degradation. One such CRL contains the ankyrin repeat and SOCS box protein 9 (ASB9), which binds to and partially inhibits creatine kinase (CK). While current models for the ASB9-CK complex contain some known interface residues, the overall structure and precise interface of the ASB9-CK complex remains unknown. Through an integrative modeling approach, we report a third-generation model that reveals precisely the interface interactions and also fits the shape of the ASB9-CK complex as determined by small-angle X-ray scattering. We constructed an atomic model for the entire CK-targeting CRL to uncover dominant modes of motion that could permit ubiquitin transfer. Remarkably, only the correctly docked CK-containing E3 ligase and not incorrectly docked structures permitted close approach of ubiquitin to the CK substrate.Download high-res image (210KB)Download full-size image