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In silico optimization of a fragment-based hit yields biologically active, high-efficiency inhibitors for glutamate racemase.

ABSTRACT: A novel lead compound for inhibition of the antibacterial drug target, glutamate racemase (GR), was optimized for both ligand efficiency and lipophilic efficiency. A previously developed hybrid molecular dynamics-docking and scoring scheme, FERM-SMD, was used to predict relative potencies of potential derivatives prior to chemical synthesis. This scheme was successful in distinguishing between high- and low-affinity binders with minimal experimental structural information, saving time and resources in the process. In vitro potency was increased approximately fourfold against GR from the model organism, B.?subtilis. Lead derivatives show two- to fourfold increased antimicrobial potency over the parent scaffold. In addition, specificity toward B.?subtilis over E.?coli and S.?aureus depends on the substituent added to the parent scaffold. Finally, insight was gained into the capacity for these compounds to reach the target enzyme in vivo using a bacterial cell wall lysis assay. The outcome of this study is a novel small-molecule inhibitor of GR with the following characteristics: Ki=2.5??M, LE=0.45?kcal?mol(-1)?atom(-1), LiPE=6.0, MIC50=260??g?mL(-1) against B.?subtilis, EC50,?lysis=520??g?mL(-1) against B.?subtilis.

SUBMITTER: Whalen KL

PROVIDER: S-EPMC4040332 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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In silico optimization of a fragment-based hit yields biologically active, high-efficiency inhibitors for glutamate racemase.

Whalen Katie L KL Chau Anthony C AC Spies M Ashley MA

ChemMedChem 20130808 10

A novel lead compound for inhibition of the antibacterial drug target, glutamate racemase (GR), was optimized for both ligand efficiency and lipophilic efficiency. A previously developed hybrid molecular dynamics-docking and scoring scheme, FERM-SMD, was used to predict relative potencies of potential derivatives prior to chemical synthesis. This scheme was successful in distinguishing between high- and low-affinity binders with minimal experimental structural information, saving time and resour ...[more]

PMID: 23929705

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Project description:The notion of a contribution of a specific group in an organic molecule's property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)-an analog of ligand efficiency (LE) for a group in the molecule-using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.

Project description:Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

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In silico optimization of a fragment-based hit yields biologically active, high-efficiency inhibitors for glutamate racemase.

Publications

In silico optimization of a fragment-based hit yields biologically active, high-efficiency inhibitors for glutamate racemase.

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