Biology subjects

Mobley, D. L.

Publications and source records attributed to Mobley, D. L..

14 recordsLinked to original sources

bioRxiv2023

POPSHIFT: A THERMODYNAMICALLY SOUND APPROACH TO ESTIMATE BINDING FREE ENERGIES BY ACCOUNTING FOR LIGAND-INDUCED POPULATION SHIFTS FROM A LIGAND-FREE MSM

Obtaining accurate binding free energies from in silico screens has been a longstanding goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a proteins thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking--producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation--and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

biophysics↗

bioRxiv2022

Molecular-Dynamics Simulation Methods for Macromolecular Crystallography

To assess the potential benefits of molecular-dynamics (MD) simulations for macromolecular crystallography (MX), we performed room-temperature X-ray diffraction studies of the catalytic subunit of mouse protein kinase A (PKA-C). We then performed crystalline MD simulations of PKA-C, computed simulated electron densities from the water, protein, and ion components of the MD simulations, and carefully compared them to the initial crystal structure. The results led to the development of an MD-MX analysis procedure and several associated methods: 1) density comparison to evaluate consistency between the MD and the initial crystal structure model; 2) water building to generate alternative solvent models; and 3) protein remodeling to improve the crystal structure where interpretation of density is unclear. This procedure produced a revised structure of PKA with a new ordered water model and a modified protein structure. The revisions yield new insights into PKA mechanisms, including: a sensitivity of the His294 conformation to protonation state, with potential consequences for regulation of substrate binding; a remodeling of the Lys217 side chain along with a bound phosphate; an alternative conformation for Lys213 associated with binding to the regulatory subunit; and an alternative conformation for catalytic base Asp166 and nearby waters, suggesting a mechanism of progression of the phosphotransfer reaction via changes in Mg2+ coordination. Based on the benefits seen applying these methods to PKA, we recommend incorporating our MD-MX procedure into MX studies, to decide among ambiguous interpretations of electron density that occur, inevitably, as part of standard model refinement.

biophysics↗

bioRxiv2022

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

Accurate small molecule force fields are crucial for predicting thermodynamic and kinetic properties of drug-like molecules in biomolecular systems. Torsion parameters, in particular, are essential for determining conformational distribution of molecules. However, they are usually fit to computationally expensive quantum chemical torsion scans and generalize poorly to different chemical environments. Torsion parameters should ideally capture local through-space non-bonded interactions such as 1-4 steric and electrostatics and non-local through-bond effects such as conjugation and hyperconjugation. Non-local through-bond effects are sensitive to remote substituents and are a contributing factor to torsion parameters poor transferability. Here we show that fractional bond orders such as the Wiberg Bond Order (WBO) are sensitive to remote substituents and correctly captures extent of conjugation and hyperconjugation. We show that the relationship between WBO and torsion barrier heights are linear and can therefore serve as a surrogate to QC torsion barriers, and to interpolate torsion force constants. Using this approach we can reduce the number of computationally expensive QC torsion scans needed while maintaining accurate torsion parameters. We demonstrate this approach to a set of substituted benzene rings.

biophysics↗

bioRxiv2021

Enhancing Sampling of Water Rearrangements on Ligand Binding: A Comparison of Techniques

1Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation timescales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

biophysics↗

bioRxiv2020

Overview of the SAMPL6 pKa Challenge: Evaluating small molecule microscopic and macroscopic pKa predictions

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

biophysics↗

bioRxiv2020

Capturing non-local through-bond effects when fragmenting molecules for quantum chemical torsion scans

Accurate molecular mechanics force fields for small molecules are essential for predicting protein-ligand binding affinities in drug discovery and understanding the biophysics of biomolecular systems. Torsion potentials derived from quantum chemical (QC) calculations are critical for determining the conformational distributions of small molecules, but are computationally expensive and scale poorly with molecular size. To reduce computational cost and avoid the complications of distal through-space intramolecular interactions, molecules are generally fragmented into smaller entities to carry out QC torsion scans. However, torsion potentials, particularly for conjugated bonds, can be strongly affected by through-bond chemistry distal to the torsion it-self. Poor fragmentation schemes have the potential to significantly disrupt electronic properties in the region around the torsion by removing important, distal chemistries, leading to poor representation of the parent molecules chemical environment and the resulting torsion energy profile. Here we show that a rapidly computable quantity, the fractional Wiberg bond order (WBO), is a sensitive reporter on whether the chemical environment around a torsion has been disrupted. We show that the WBO can be used as a surrogate to assess the robustness of fragmentation schemes and identify conjugated bond sets. We use this concept to construct a validation set by exhaustively fragmenting a set of druglike organic molecules and examine their corresponding WBO distributions derived from accessible conformations that can be used to evaluate fragmentation schemes. To illustrate the utility of the WBO in assessing fragmentation schemes that preserve the chemical environment, we propose a new fragmentation scheme that uses rapidly-computable AM1 WBOs, which are available essentially for free as part of standard AM1-BCC partial charge assignment. This approach can simultaneously maximize the chemical equivalency of the fragment and the substructure in the larger molecule while minimizing fragment size to accelerate QC torsion potential computation for small molecules and reducing undesired through-space steric interactions.

biophysics↗

bioRxiv2020

Structural and molecular dynamics of Mycobacterium tuberculosis malic enzyme, a potential anti-TB drug target

Tuberculosis (TB) is the most lethal bacterial infectious disease worldwide. It is notoriously difficult to treat, requiring a cocktail of antibiotics administered over many months. The dense, waxy outer membrane of the TB-causing agent, Mycobacterium tuberculosis (Mtb), acts as a formidable barrier against uptake of antibiotics. Subsequently, enzymes involved in maintaining the integrity of the Mtb cell wall are promising drug targets. Recently, we demonstrated that Mtb lacking malic enzyme (MEZ) has altered cell wall lipid composition and attenuated uptake by macrophages. These results suggest that MEZ provides the required reducing power for lipid biosynthesis. Here, we present the X-ray crystal structure of MEZ to 3.6 [A] resolution and compare it with known structures of prokaryotic and eukaryotic malic enzymes. We use biochemical assays to determine its oligomeric state and to evaluate the effects of pH and allosteric regulators on its kinetics and thermal stability. To assess the interactions between MEZ and its substrate malate and cofactors, Mn2+ and NAD(P)+, we ran a series of molecular dynamics (MD) simulations. First, the MD analysis corroborates our empirical observations that MEZ is unusually disordered, which persists even with the addition of substrate and cofactors. Second, the MD simulations reveal that MEZ subunits alternate between open and closed states and that MEZ can stably bind its NAD(P)+ cofactor in multiple conformations, including an inactive, compact NAD+ form. Together the structure of MEZ and insights from its dynamics can be harnessed to inform the design of MEZ inhibitors that target Mtb.

biochemistry↗

bioRxiv2020

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 pKa Challenge, which asked participants to predict pKa values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92{+/-}0.13, 0.48{+/-}0.06, 0.47{+/-}0.05, and 0.50{+/-}0.06, respectively.

biophysics↗

bioRxiv2019

Liquid-like and rigid-body motions in molecular-dynamics simulations of a crystalline protein

To gain insight into crystalline protein dynamics, we performed molecular-dynamics (MD) simulations of a periodic 2x2x2 supercell of staphylococcal nuclease. We used the resulting MD trajectories to simulate X-ray diffraction and to study collective motions. The agreement of simulated X-ray diffraction with the data is comparable to previous MD simulation studies. We studied collective motions by analyzing statistically the covariance of alpha-carbon position displacements. The covariance decreases exponentially with the distance between atoms, which is consistent with a liquid-like motions (LLM) model, in which the protein behaves like a soft material. To gain finer insight into the collective motions, we examined the covariance behavior within a protein molecule (intra-protein) and between different protein molecules (inter-protein). The inter-protein atom pairs, which dominate the overall statistics, exhibit LLM behavior; however, the intra-protein pairs exhibit behavior that is consistent with a superposition of LLM and rigid-body motions (RBM). Our results indicate that LLM behavior of global dynamics is present in MD simulations of a protein crystal. They also show that RBM behavior is detectable in the simulations but that it is subsumed by the LLM behavior. Finally the results provide clues about how correlated motions of atom pairs both within and across proteins might manifest in diffraction data. Overall our findings increase our understanding of the connection between molecular motions and diffraction data, and therefore advance efforts to extract information about functionally important motions from crystallography experiments.

biophysics↗

bioRxiv2019

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

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 provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange--while displaying very small variance--can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

biophysics↗

bioRxiv2019

Octanol-water partition coefficient measurements for the SAMPL6 Blind Prediction Challenge

Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients (Kow), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa Prediction Challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II Partition Coefficient Challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.

biophysics↗

bioRxiv2019

The structure of Mycobacterium tuberculosis heme-degrading protein, MhuD, in complex with product

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, requires iron for survival. In Mtb, MhuD is the cytosolic protein that degrades imported heme. MhuD is distinct, both in sequence and structure, from canonical heme oxygenases (HOs) but homologous with IsdG-type proteins. Canonical HO is found mainly in eukaryotes, while IsdG-type proteins are predominantly found in prokaryotes including pathogens. While there are several published structures of MhuD and other IsdG-type proteins in complex with heme substrate, no structures have been reported of IsdG-type proteins in complex with product, unlike HOs. We recently showed that the Mtb variant MhuD-R26S produces biliverdin IX (BV) rather than the wild-type (WT) mycobilin isomers as product. Given that mycobilin and other IsdG-type protein products like staphylobilin are difficult to isolate in quantities sufficient for structure determination, here we use the MhuD-R26S variant and its product BV as a proxy to study the IsdG-type protein/product complex. First we show that BV has nanomolar affinity for MhuD and the R26S variant. Second we determined the MhuD-R26S-BV complex structure to 2.5 [A], which reveals two notable features (1) two BV molecules bound per active site and (2) a new -helix (3) as compared with the MhuD-heme structure. Finally, by molecular dynamics simulations we show that 3 is stable with the proximal BV alone. MhuDs high affinity for its product and structural and electrostatic changes that accompany substrate turnover suggest that there is an unidentified protein that is responsible for product extraction from MhuD and other IsdG-type proteins.

biochemistry↗

bioRxiv2018

Overview of the SAMPL6 host-guest binding affinity prediction challenge

Accurately predicting the binding affinities of small organic molecules to biological macro-molecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macro-molecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to further enhance predictive accuracy.

biophysics↗

bioRxiv2018

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors--an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid-base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.\n\nAbbreviations

biophysics↗