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Estimating affinities of calcium ions to proteins.

ABSTRACT: Ca(2+)-ions have a range of affinities to different proteins, depending on the various functions of these proteins. This makes the determination of Ca(2+)-protein affinities an interesting subject for functional studies. We have investigated the performance of two methods - Fold-X and AutoDock vina - in the prediction of Ca(2+)-protein affinities. Both methods, although based on different energy functions, showed virtually the same correlation with experimental affinities. Guided by insight from experiment, we further derived a simple linear model based on the solvent accessible surface of Ca(2+) that had practically the same performance in terms of absolute errors as the more complex docking methods.

SUBMITTER: Franke S

PROVIDER: S-EPMC3170010 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Estimating affinities of calcium ions to proteins.

Franke Stefan S Herfurth Julia J Hoffmann Daniel D

Advances and applications in bioinformatics and chemistry : AABC 20100315

Ca(2+)-ions have a range of affinities to different proteins, depending on the various functions of these proteins. This makes the determination of Ca(2+)-protein affinities an interesting subject for functional studies. We have investigated the performance of two methods - Fold-X and AutoDock vina - in the prediction of Ca(2+)-protein affinities. Both methods, although based on different energy functions, showed virtually the same correlation with experimental affinities. Guided by insight from ...[more]

PMID: 21918621

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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.

Dataset Information

Estimating affinities of calcium ions to proteins.

Publications

Estimating affinities of calcium ions to proteins.

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