Development and Application of Molecular Modeling Methods for Characterization of Drug Targets in Mycobacterium tuberculosis

For decades, tuberculosis (TB) has persisted as a global health burden with over a million people succumbing to the disease each year. As cases of multi-drug resistant and extensively drug-resistant increase, there is an urgent need for the development of novel therapeutics to combat the disease. Fortunately, advances in protein structure determination and development of computational modeling tools provide valuable insight into the specific interactions the mediate binding between proteins and ligands. Nonetheless, the results of these methods are sometimes ambiguous or inconclusive. Taken together, structural determination and computational modeling can complement each other to provide more robust and interpretable data. In Chapter 2 of this work, I focus on the development of a computational method to enhance sampling of side chains, which can rearrange in the presence of different ligands. Specifically, I describe the development and validation of a non-equilibrium candidate Monte Carlo method for the enhanced sampling of side chain rotamers during molecular dynamics (MD) simulations. I validate this method on a simple valine-alanine dipeptide and demonstrate that it significantly improves rotamer sampling of a buried valine sidechain in the binding site of model system T4 lysozyme L99A. In Chapters 3 and 4, we use X-ray crystallography and MD to elucidate and study the structures of two potential TB drug targets from Mycobacterium tuberculosis (Mtb): Mtb heme oxygenase (MhuD) and Mtb malic enzyme (MEZ). In the structure of MhuD in complex with product, we observe the formation of a novel α-helix; however, the unusual 5:2 ratio of product to protein subunit in the asymmetric unit, as well as the proximity of the helix to the crystallographic interface, provoke questions regarding its biological relevance. Using MD, I confirm that formation of the α-helix is favored in the presence of product and likely associated with product-turnover. In Chapter 3, I describe the X-ray structure of apo-MEZ along with results from differential scanning fluorimetry and gel filtration. MD simulations provide insight into interactions of MEZ with NAD(P)+, Mn2+, and malate, while also corroborating our empirical observations that MEZ is unusually disordered

Introducing the new bacterial branch of the RNase A superfamily

Bovine pancreatic ribonuclease (RNase A) is the founding member of the RNase A superfamily. Members of this superfamily of ribonucleases have high sequence diversity, but possess a similar structural fold, together with a conserved His-Lys-His catalytic triad and structural disulfide bonds. Until recently, RNase A proteins had exclusively been identified in eukaryotes within vertebrae. Here, we discuss the discovery by Batot et al. of a bacterial RNase A superfamily member, CdiA-CTYkris: a toxin that belongs to an inter-bacterial competition system from Yersinia kristensenii. CdiA-CTYkris exhibits the same structural fold as conventional RNase A family members and displays in vitro and in vivo ribonuclease activity. However, CdiA-CTYkris shares little to no sequence similarity with RNase A, and lacks the conserved disulfide bonds and catalytic triad of RNase A. Interestingly, the CdiA-CTYkris active site more closely resembles the active site composition of various eukaryotic endonucleases. Despite lacking sequence similarity to eukaryotic RNase A family members, CdiA-CTYkris does share high sequence similarity with numerous Gram-negative and Gram-positive bacterial proteins/domains. Nearly all of these bacterial homologs are toxins associated with virulence and bacterial competition, suggesting that the RNase A superfamily has a distinct bacterial subfamily branch, which likely arose by way of convergent evolution. Finally, RNase A interacts directly with the immunity protein of CdiA-CTYkris, thus the cognate immunity protein for the bacterial RNase A member could be engineered as a new eukaryotic RNase A inhibitor

Introducing the new bacterial branch of the RNase A superfamily

Bovine pancreatic ribonuclease (RNase A) is the founding member of the RNase A superfamily. Members of this superfamily of ribonucleases have high sequence diversity, but possess a similar structural fold, together with a conserved His-Lys-His catalytic triad and structural disulfide bonds. Until recently, RNase A proteins had exclusively been identified in eukaryotes within vertebrae. Here, we discuss the discovery by Batot et al. of a bacterial RNase A superfamily member, CdiA-CTYkris: a toxin that belongs to an inter-bacterial competition system from Yersinia kristensenii. CdiA-CTYkris exhibits the same structural fold as conventional RNase A family members and displays in vitro and in vivo ribonuclease activity. However, CdiA-CTYkris shares little to no sequence similarity with RNase A, and lacks the conserved disulfide bonds and catalytic triad of RNase A. Interestingly, the CdiA-CTYkris active site more closely resembles the active site composition of various eukaryotic endonucleases. Despite lacking sequence similarity to eukaryotic RNase A family members, CdiA-CTYkris does share high sequence similarity with numerous Gram-negative and Gram-positive bacterial proteins/domains. Nearly all of these bacterial homologs are toxins associated with virulence and bacterial competition, suggesting that the RNase A superfamily has a distinct bacterial subfamily branch, which likely arose by way of convergent evolution. Finally, RNase A interacts directly with the immunity protein of CdiA-CTYkris, thus the cognate immunity protein for the bacterial RNase A member could be engineered as a new eukaryotic RNase A inhibitor

Enhancing Sidechain Rotamer Sampling Using Non-Equilibrium Candidate Monte Carlo

Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation timescales, yet critical. Specifically, adequate sampling of sidechain motions in protein binding pockets proves crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The timescale of sidechain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed non-equilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of sidechain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target sidechain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine sidechain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances sidechain sampling, especially in systems where the torsional barrier to rotation is high (>10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment.Overall, this may provide a promising strategy to selectively improve sidechain sampling in molecular simulations.</div

Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo.

Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation time scales, yet critical. Specifically, adequate sampling of side chain motions in protein binding pockets is crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The time scale of side chain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed nonequilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of side chain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target side chain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine side chain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances side chain sampling, especially in systems where the torsional barrier to rotation is high (≥10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment. Overall, this may provide a promising strategy to selectively improve side chain sampling in molecular simulations

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided

Structure of a Mycobacterium tuberculosis Heme-Degrading Protein, MhuD, Variant in Complex with Its 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, in both 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 the heme substrate, no structures of IsdG-type proteins in complex with a product have been reported, unlike the case for HOs. We recently showed that the Mtb variant MhuD-R26S produces biliverdin IXα (αBV) rather than the wild-type mycobilin isomers. 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 αBV as a proxy to study the IsdG-type protein-product complex. First, we show that αBV has a nanomolar affinity for MhuD and the R26S variant. Second, we determined the MhuD-R26S-αBV complex structure to 2.5 Å, which reveals two notable features: (1) two αBV molecules bound per active site and (2) a novel α-helix (α3) that was not observed in previous MhuD-heme structures. Finally, through molecular dynamics simulations, we show that α3 is stable with the proximal αBV alone. MhuD's high affinity for the product and the observed structural and electrostatic changes that accompany substrate turnover suggest that there may be an unidentified class of proteins that are responsible for the extraction of products from MhuD and other IsdG-type proteins