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Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions.

ABSTRACT: The diverse Regulator of G protein Signaling (RGS) family sets the timing of G protein signaling. To understand how the structure of RGS proteins determines their common ability to inactivate G proteins and their selective G protein recognition, we combined structure-based energy calculations with biochemical measurements of RGS activity. We found a previously unidentified group of variable 'Modulatory' residues that reside at the periphery of the RGS domain-G protein interface and fine-tune G protein recognition. Mutations of Modulatory residues in high-activity RGS proteins impaired RGS function, whereas redesign of low-activity RGS proteins in critical Modulatory positions yielded complete gain of function. Therefore, RGS proteins combine a conserved core interface with peripheral Modulatory residues to selectively optimize G protein recognition and inactivation. Finally, we show that our approach can be extended to analyze interaction specificity across other large protein families.

SUBMITTER: Kosloff M

PROVIDER: S-EPMC3130846 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions.

Kosloff Mickey M Travis Amanda M AM Bosch Dustin E DE Siderovski David P DP Arshavsky Vadim Y VY

Nature structural & molecular biology 20110619 7

The diverse Regulator of G protein Signaling (RGS) family sets the timing of G protein signaling. To understand how the structure of RGS proteins determines their common ability to inactivate G proteins and their selective G protein recognition, we combined structure-based energy calculations with biochemical measurements of RGS activity. We found a previously unidentified group of variable 'Modulatory' residues that reside at the periphery of the RGS domain-G protein interface and fine-tune G p ...[more]

PMID: 21685921

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Project description:Protein-carbohydrate interactions are significant in a wide range of biological processes, disruption of which has been implicated in many different diseases. The capability of glycan-binding proteins (GBPs) to specifically bind to the corresponding glycans allows GBPs to be utilized in glycan biomarker detection or conversely to serve as targets for therapeutic intervention. However, understanding the structural origins of GBP specificity has proven to be challenging due to their typically low binding affinities (mM) and their potential to display broad or complex specificities. Here we perform molecular dynamics (MD) simulations and post-MD energy analyses with the Poisson-Boltzmann and generalized Born solvent models (MM-PB/GBSA) of the Erythrina cristagalli lectin (ECL) with its known ligands, and with new cocrystal structures reported herein. While each MM-PB/GBSA parametrization resulted in different estimates of the desolvation free energy, general trends emerged that permit us to define GBP binding preferences in terms of ligand substructure specificity. Additionally, we have further decomposed the theoretical interaction energies into contributions made between chemically relevant functional groups. Based on these contributions, the functional groups in each ligand can be assembled into a pharmacophore comprised of groups that are either critical for binding, or enhance binding, or are noninteracting. It is revealed that the pharmacophore for ECL consists of the galactopyranose (Gal) ring atoms along with C6 and the O3 and O4 hydroxyl groups. This approach provides a convenient method for identifying and quantifying the glycan pharmacophore and provides a novel method for interpreting glycan specificity that is independent of residue-level glycan nomenclature. A pharmacophore approach to defining specificity is readily transferable to molecular design software and, therefore, may be particularly useful in designing therapeutics (glycomimetics) that target GBPs.

Project description:Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00639-y.

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Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions.

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Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions.

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