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Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

ABSTRACT: The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

SUBMITTER: Moal IH

PROVIDER: S-EPMC3257286 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

Moal Iain H IH Bates Paul A PA

PLoS computational biology 20120112 1

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can b ...[more]

PMID: 22253587

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Project description:Human histidine triad nucleotide binding protein 1 (hHint1) is a member of a ubiquitous and ancient branch of the histidine triad protein superfamily. hHint1 is a homodimeric protein that catalyzes the hydrolysis of model substrates, phosphoramidate and acyl adenylate, with a high efficiency. Recently, catalytically inactive hHint1 has been identified as the cause of inherited peripheral neuropathy [Zimon, M., et al. (2012) Nat. Genet. 44, 1080-1083]. We have conducted the first detailed kinetic mechanistic studies of hHint1 and have found that the reaction mechanism is consistent with a double-displacement mechanism, in which the active site nucleophile His112 is first adenylylated by the substrate, followed by hydrolysis of the AMP-enzyme intermediate. A transient burst phase followed by a linear phase from the stopped-flow fluorescence assay indicated that enzyme adenylylation was faster than the subsequent intermediate hydrolysis and product release. Solvent viscosity experiments suggested that both chemical transformation and diffusion-sensitive events (product release or protein conformational change) limit the overall turnover. The catalytic trapping experiments and data simulation indicated that the true koff rate of the final product AMP is unlikely to control the overall kcat. Therefore, a protein conformational change associated with product release is likely rate-limiting. In addition, the rate of Hint1 adenylylation was found to be dependent on two residues with pKa values of 6.5 and 8, with the former pKa agreeing well with the nuclear magnetic resonance titration results for the pKa of the active site nucleophile His112. In comparison to the uncatalyzed rates, hHint1 was shown to enhance acyl-AMP and AMP phosphoramidate hydrolysis by 10(6)-10(8)-fold. Taken together, our analysis indicates that hHint1 catalyzes the hydrolysis of phosphoramidate and acyl adenylate with high efficiency, through a mechanism that relies on rapid adenylylation of the active residue, His112, while being partially rate-limited by intermediate hydrolysis and product release associated with a conformational change. Given the high degree of sequence homology of Hint proteins across all kingdoms of life, it is likely that their kinetic and catalytic mechanisms will be similar to those elucidated for hHint1.

Project description:Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small "probe" molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.

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Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

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Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

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