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Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

ABSTRACT: Many protein-protein docking algorithms generate numerous possible complex structures with only a few of them resembling the native structure. The major challenge is choosing the near-native structures from the generated set. Recently it has been observed that the density of conserved residue positions is higher at the interface regions of interacting protein surfaces, except for antibody-antigen complexes, where a very low number of conserved positions is observed at the interface regions. In the present study we have used this observation to identify putative interacting regions on the surface of interacting partners. We studied 59 protein complexes, used previously as a benchmark data set for docking investigations. We computed conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences and used this information to filter out from 56% to 86% of generated docked models, retaining near-native structures for further evaluation. We used a reverse filter of conservation score to filter out the majority of nonnative antigen-antibody complex structures. For each docked model in the filtered subsets, we relaxed the conformation of the side chains by minimizing the energy with CHARMM, and then calculated the binding free energy using a generalized Born method and solvent-accessible surface area calculations. Using the free energy along with conservation information and other descriptors used in the literature for ranking docking solutions, such as shape complementarity and pair potentials, we developed a global ranking procedure that significantly improves the docking results by giving top ranks to near-native complex structures.

SUBMITTER: Duan Y

PROVIDER: S-EPMC2253405 | biostudies-literature | 2005 Feb

REPOSITORIES: biostudies-literature

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Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

Duan Yuhua Y Reddy Boojala V B BV Kaznessis Yiannis N YN

Protein science : a publication of the Protein Society 20050201 2

Many protein-protein docking algorithms generate numerous possible complex structures with only a few of them resembling the native structure. The major challenge is choosing the near-native structures from the generated set. Recently it has been observed that the density of conserved residue positions is higher at the interface regions of interacting protein surfaces, except for antibody-antigen complexes, where a very low number of conserved positions is observed at the interface regions. In t ...[more]

PMID: 15659366

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Project description:The protein docking problem has two major aspects: sampling conformations and orientations, and scoring them for fit. To investigate the extent to which the protein docking problem may be attributed to the sampling of ligand side-chain conformations, multiple conformations of multiple residues were calculated for the uncomplexed (unbound) structures of protein ligands. These ligand conformations were docked into both the complexed (bound) and unbound conformations of the cognate receptors, and their energies were evaluated using an atomistic potential function. The following questions were considered: (1) does the ensemble of precalculated ligand conformations contain a structure similar to the bound form of the ligand? (2) Can the large number of conformations that are calculated be efficiently docked into the receptors? (3) Can near-native complexes be distinguished from non-native complexes? Results from seven test systems suggest that the precalculated ensembles do include side-chain conformations similar to those adopted in the experimental complexes. By assuming additivity among the side chains, the ensemble can be docked in less than 12 h on a desktop computer. These multiconformer dockings produce near-native complexes and also non-native complexes. When docked against the bound conformations of the receptors, the near-native complexes of the unbound ligand were always distinguishable from the non-native complexes. When docked against the unbound conformations of the receptors, the near-native dockings could usually, but not always, be distinguished from the non-native complexes. In every case, docking the unbound ligands with flexible side chains led to better energies and a better distinction between near-native and non-native fits. An extension of this algorithm allowed for docking multiple residue substitutions (mutants) in addition to multiple conformations. The rankings of the docked mutant proteins correlated with experimental binding affinities. These results suggest that sampling multiple residue conformations and residue substitutions of the unbound ligand contributes to, but does not fully provide, a solution to the protein docking problem. Conformational sampling allows a classical atomistic scoring function to be used; such a function may contribute to better selectivity between near-native and non-native complexes. Allowing for receptor flexibility may further extend these results.

Project description:BACKGROUND: Protein-protein docking is an in silico method to predict the formation of protein complexes. Due to limited computational resources, the protein-protein docking approach has been developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations and water contribution is ignored or implicitly presented. Despite obtaining a number of acceptable complex predictions, it seems to-date that most initial rigid docking algorithms still find it difficult or even fail to discriminate successfully the correct predictions from the other incorrect or false positive ones. To improve the rigid docking results, re-ranking is one of the effective methods that help re-locate the correct predictions in top high ranks, discriminating them from the other incorrect ones. RESULTS: Our results showed that the IFACEwat increased both the numbers of the near-native structures and improved their ranks as compared to the initial rigid docking ZDOCK3.0.2. In fact, the IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method F2Dock, the IFACEwat performed equivalently well or even better for several Antigen/Antibody complexes. CONCLUSIONS: With the inclusion of interfacial water, the IFACEwat improves mostly results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly taking into account the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near native structures found. As our implementation so far targeted to improve the results of ZDOCK3.0.2, and particularly for the Antigen/Antibody complexes, it is expected in the near future that more implementations will be conducted to be applicable for other initial rigid docking algorithms.

Project description:BACKGROUND: For over 30 years potentials of mean force have been used to evaluate the relative energy of protein structures. The most commonly used potentials define the energy of residue-residue interactions and are derived from the empirical analysis of the known protein structures. However, single-body residue 'environment' potentials, although widely used in protein structure analysis, have not been rigorously compared to these classical two-body residue-residue interaction potentials. Here we do not try to combine the two different types of residue interaction potential, but rather to assess their independent contribution to scoring protein structures. RESULTS: A data set of nearly three thousand monomers was used to compare pairwise residue-residue 'contact-type' propensities to single-body residue 'contact-count' propensities. Using a large and standard set of protein decoys we performed an in-depth comparison of these two types of residue interaction propensities. The scores derived from the contact-type and contact-count propensities were assessed using two different performance metrics and were compared using 90 different definitions of residue-residue contact. Our findings show that both types of score perform equally well on the task of discriminating between near-native protein decoys. However, in a statistical sense, the contact-count based scores were found to carry more information than the contact-type based scores. CONCLUSION: Our analysis has shown that the performance of either type of score is very similar on a range of different decoys. This similarity suggests a common underlying biophysical principle for both types of residue interaction propensity. However, several features of the contact-count based propensity suggests that it should be used in preference to the contact-type based propensity. Specifically, it has been shown that contact-counts can be predicted from sequence information alone. In addition, the use of a single-body term allows for efficient alignment strategies using dynamic programming, which is useful for fold recognition, for example. These facts, combined with the relative simplicity of the contact-count propensity, suggests that contact-counts should be studied in more detail in the future.

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Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

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Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

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