Nathan M. Lim

Lead optimization mapper: automating free energy calculations for lead optimization

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger’s and OpenEye’s APIs, has been made available freely under the BSD license.

Structure-based design of bacterial nitric oxide synthase inhibitors

Inhibition of bacterial nitric oxide synthase (bNOS) has the potential to improve the efficacy of antimicrobials used to treat infections by Gram-positive pathogens Staphylococcus aureus and Bacillus anthracis. However, inhibitor specificity toward bNOS over the mammalian NOS (mNOS) isoforms remains a challenge because of the near identical NOS active sites. One key structural difference between the NOS isoforms is the amino acid composition of the pterin cofactor binding site that is adjacent to the NOS active site. Previously, we demonstrated that a NOS inhibitor targeting both the active and pterin sites was potent and functioned as an antimicrobial (Holden, , Proc. Natl. Acad. Sci. U.S.A.2013, 110, 1812724145412). Here we present additional crystal structures, binding analyses, and bacterial killing studies of inhibitors that target both the active and pterin sites of a bNOS and function as antimicrobials. Together, these data provide a framework for continued development of bNOS inhibitors, as each molecule represents an excellent chemical scaffold for the design of isoform selective bNOS inhibitors.

Identification of Redox Partners and Development of a Novel Chimeric Bacterial Nitric Oxide Synthase for Structure Activity Analyses.

Background: Inhibition of bacterial nitric oxide synthase (NOS) improves the efficacy of antibiotics. Results: Development and characterization of a novel bacterial NOS chimera supports NO production in the presence of NADPH and a flavodoxin reductase. Conclusion: Structure activity relationship between NOS inhibitors and bacterial NOS can now be evaluated. Significance: Assays can be adapted for high-throughput screening to identify new bacterial NOS inhibitors. Production of nitric oxide (NO) by nitric oxide synthase (NOS) requires electrons to reduce the heme iron for substrate oxidation. Both FAD and FMN flavin groups mediate the transfer of NADPH derived electrons to NOS. Unlike mammalian NOS that contain both FAD and FMN binding domains within a single polypeptide chain, bacterial NOS is only composed of an oxygenase domain and must rely on separate redox partners for electron transfer and subsequent activity. Here, we report on the native redox partners for Bacillus subtilis NOS (bsNOS) and a novel chimera that promotes bsNOS activity. By identifying and characterizing native redox partners, we were also able to establish a robust enzyme assay for measuring bsNOS activity and inhibition. This assay was used to evaluate a series of established NOS inhibitors. Using the new assay for screening small molecules led to the identification of several potent inhibitors for which bsNOS-inhibitor crystal structures were determined. In addition to characterizing potent bsNOS inhibitors, substrate binding was also analyzed using isothermal titration calorimetry giving the first detailed thermodynamic analysis of substrate binding to NOS.

Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

Here, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types — a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck’s parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈ 300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

Escaping atom types in force fields using direct chemical perception

Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.