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Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits.

ABSTRACT: Several studies have suggested that disrupting interactions of the G protein betagamma subunits with downstream binding partners might be a valuable study for pharmaceutical development. Recently, small molecules have been found which bind to Gbetagamma with high apparent affinity in an enzyme-linked immunosorbent assay (ELISA), have demonstrated selective inhibition of interactions of Gbetagamma with downstream signaling partners, and have been shown to increase antinociceptive effects of morphine and inhibit inflammation in vivo. In this paper we examine several docking and scoring protocols for estimating binding affinities for a set of 830 ligands from the NCI diversity set to the Gbeta1gamma2 subunit and compared these with IC50s measured in a competition ELISA with a high-affinity peptidic ligand. The best-performing docking protocol used a consensus score and ensemble docking and resulted in a 6-fold enrichment of high-affinity compounds in the top-ranked 5% of the ligand data set.

SUBMITTER: Park MS

PROVIDER: S-EPMC2846521 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits.

Park Min-Sun MS Dessal Axel L AL Smrcka Alan V AV Stern Harry A HA

Journal of chemical information and modeling 20090201 2

Several studies have suggested that disrupting interactions of the G protein betagamma subunits with downstream binding partners might be a valuable study for pharmaceutical development. Recently, small molecules have been found which bind to Gbetagamma with high apparent affinity in an enzyme-linked immunosorbent assay (ELISA), have demonstrated selective inhibition of interactions of Gbetagamma with downstream signaling partners, and have been shown to increase antinociceptive effects of morph ...[more]

PMID: 19434844

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Project description:G proteins mediate the action of G protein coupled receptors (GPCRs), a major target of current pharmaceuticals and a major target of interest in future drug development. Most pharmaceutical interest has been in the development of selective GPCR agonists and antagonists that activate or inhibit specific GPCRs. Some recent thinking has focused on the idea that some pathologies are the result of the actions of an array of GPCRs suggesting that targeting single receptors may have limited efficacy. Thus, targeting pathways common to multiple GPCRs that control critical pathways involved in disease has potential therapeutic relevance. G protein betagamma subunits released from some GPCRs upon receptor activation regulate a variety of downstream pathways to control various aspects of mammalian physiology. There is evidence from cell- based and animal models that excess Gbetagamma signaling can be detrimental and blocking Gbetagamma signaling has salutary effects in a number of pathological models. Gbetagamma regulates downstream pathways through modulation of enzymes that produce cellular second messengers or through regulation of ion channels by direct protein-protein interactions. Thus, blocking Gbetagamma functions requires development of small molecule agents that disrupt Gbetagamma protein interactions with downstream partners. Here we discuss evidence that small molecule targeting Gbetagamma could be of therapeutic value. The concept of disruption of protein-protein interactions by targeting a "hot spot" on Gbetagamma is delineated and the biochemical and virtual screening strategies for identification of small molecules that selectively target Gbetagamma functions are outlined. Evaluation of the effectiveness of virtual screening indicates that computational screening enhanced identification of true Gbetagamma binding molecules. However, further refinement of the approach could significantly improve the yield of Gbetagamma binding molecules from this screen that could result in multiple candidate leads for future drug development.

Project description:Antimalarial drug resistance has thrown a spanner in the works of malaria elimination. New drugs are required for ancillary support of existing malaria control efforts. Plasmodium falciparum requires host glucose for survival and proliferation. On this basis, P. falciparum hexose transporter 1 (PfHT1) protein involved in hexose permeation is considered a potential drug target. In this study, we tested the antimalarial activity of some compounds against PfHT1 using computational techniques. We performed high throughput virtual screening of 21,352 small-molecule compounds against PfHT1. The stability of the lead compound complexes was evaluated via molecular dynamics (MD) simulation for 100 nanoseconds. We also investigated the pharmacodynamic, pharmacokinetic and physiological characteristics of the compounds in accordance with Lipinksi rules for drug-likeness to bind and inhibit PfHT1. Molecular docking and free binding energy analyses were carried out using Molecular Mechanics with Generalized Born and Surface Area (MMGBSA) solvation to determine the selectivity of the hit compounds for PfHT1 over the human glucose transporter (hGLUT1) orthologue. Five important PfHT1 inhibitors were identified: Hyperoside (CID5281643); avicularin (CID5490064); sylibin (CID5213); harpagoside (CID5481542) and quercetagetin (CID5281680). The compounds formed intermolecular interaction with the binding pocket of the PfHT1 target via conserved amino acid residues (Val314, Gly183, Thr49, Asn52, Gly183, Ser315, Ser317, and Asn48). The MMGBSA analysis of the complexes yielded high free binding energies. Four (CID5281643, CID5490064, CID5213, and CID5481542) of the identified compounds were found to be stable within the PfHT1 binding pocket throughout the 100 nanoseconds simulation run time. The four compounds demonstrated higher affinity for PfHT1 than the human major glucose transporter (hGLUT1). This investigation demonstrates the inhibition potential of sylibin, hyperoside, harpagoside, and avicularin against PfHT1 receptor. Robust preclinical investigations are required to validate the chemotherapeutic properties of the identified compounds.

Project description:A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

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Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits.

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Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits.

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