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Using tert-Butyl Groups in a Ligand To Identify Its Binding Site on a Protein.

ABSTRACT: Few methods allow determining the binding site of tightly binding ligands. We show that ligands containing a tert-butyl (e.g., Boc) group produce easily observable nuclear Overhauser effects (NOE) with the target protein even when the tert-butyl group is not highly solvent exposed. NOEs with methyl groups of the target protein are readily assigned by selectively isotope labeling, presenting a practical and quick way to pinpoint the location of the ligand without any prior specific nuclear magnetic resonance assignments of the protein. The approach works for nonexchanging ligands as well as for weakly binding ligands.

SUBMITTER: Chen WN

PROVIDER: S-EPMC5807864 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Using tert-Butyl Groups in a Ligand To Identify Its Binding Site on a Protein.

Chen Wan-Na WN Otting Gottfried G

ACS medicinal chemistry letters 20180112 2

Few methods allow determining the binding site of tightly binding ligands. We show that ligands containing a tert-butyl (e.g., Boc) group produce easily observable nuclear Overhauser effects (NOE) with the target protein even when the tert-butyl group is not highly solvent exposed. NOEs with methyl groups of the target protein are readily assigned by selectively isotope labeling, presenting a practical and quick way to pinpoint the location of the ligand without any prior specific ...[more]

PMID: 29456797

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Project description:Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

Project description:BACKGROUND: A recent analysis of protein sequences deposited in the NCBI RefSeq database indicates that ~8.5 million protein sequences are encoded in prokaryotic and eukaryotic genomes, where ~30% are explicitly annotated as "hypothetical" or "uncharacterized" protein. Our Comparison of Protein Active-Site Structures (CPASS v.2) database and software compares the sequence and structural characteristics of experimentally determined ligand binding sites to infer a functional relationship in the absence of global sequence or structure similarity. CPASS is an important component of our Functional Annotation Screening Technology by NMR (FAST-NMR) protocol and has been successfully applied to aid the annotation of a number of proteins of unknown function. FINDINGS: We report a major upgrade to our CPASS software and database that significantly improves its broad utility. CPASS v.2 is designed with a layered architecture to increase flexibility and portability that also enables job distribution over the Open Science Grid (OSG) to increase speed. Similarly, the CPASS interface was enhanced to provide more user flexibility in submitting a CPASS query. CPASS v.2 now allows for both automatic and manual definition of ligand-binding sites and permits pair-wise, one versus all, one versus list, or list versus list comparisons. Solvent accessible surface area, ligand root-mean square difference, and C? distances have been incorporated into the CPASS similarity function to improve the quality of the results. The CPASS database has also been updated. CONCLUSIONS: CPASS v.2 is more than an order of magnitude faster than the original implementation, and allows for multiple simultaneous job submissions. Similarly, the CPASS database of ligand-defined binding sites has increased in size by ~ 38%, dramatically increasing the likelihood of a positive search result. The modification to the CPASS similarity function is effective in reducing CPASS similarity scores for false positives by ~30%, while leaving true positives unaffected. Importantly, receiver operating characteristics (ROC) curves demonstrate the high correlation between CPASS similarity scores and an accurate functional assignment. As indicated by distribution curves, scores ? 30% infer a functional similarity. Software URL: http://cpass.unl.edu.

Dataset Information

Using tert-Butyl Groups in a Ligand To Identify Its Binding Site on a Protein.

Publications

Using <i>tert</i>-Butyl Groups in a Ligand To Identify Its Binding Site on a Protein.

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