Use of pair potentials across protein interfaces in screening predicted docked complexes

Moont, Gidon; Gabb, Henry A.; Sternberg, Michael J.E.

doi:10.1002/(sici)1097-0134(19990515)35:3<364::aid-prot11>3.3.co;2-w

Cited by 92 publications

(166 citation statements)

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“…Thus, the value of the score for each pair is a measure of the likelihood that the particular pair occurs, and the total score for a structure is the sum of all scores for residue pairs that satisfy a distance cutoff constraint of 6 Å. 12 The major difference between ACP and RPScore is that the latter has been constructed from statistics derived from interface data involving 90 nonhomologous protein complexes (http://www.bmm.icnet.uk/ docking/downloads/3d-dock-manual.pdf).…”

Section: Structure-based Potentialsmentioning

confidence: 99%

“…The residue pair potential score (RPScore), developed by Moont et al 12 provides a score for each pair of residues across the protein-protein interface and is defined as the log fraction of the actual frequency and the expected frequency of occurrence. Thus, the value of the score for each pair is a measure of the likelihood that the particular pair occurs, and the total score for a structure is the sum of all scores for residue pairs that satisfy a distance cutoff constraint of 6 Å.…”

Section: Structure-based Potentialsmentioning

confidence: 99%

“…In the first part of this article, we studied the decoys generated by Moont et al [because the 1BDJ complex is hypothesized not to be the biologically active conformation (see http://www.bmm.icnet.uk/docking/systems/1bdj.html), decoys are considered only for the other 13 complexes; see Table I]. 12 Each set of decoys was minimized for 1000 adopted-basis Newton-Raphson (ABNR) steps in CHARMM, with use of the CHARMM 19 parameters. 23 Each set was then ranked according to the following potentials:…”

Section: Docking Filtering and Scoringmentioning

confidence: 99%

“…Our motivation in this article is twofold. First, Moont et al have developed an empirical, residue pair potential score (RPScore) based on structural information from interactions in protein-protein complexes 12 that typically discriminates between near-native and non-native docked conformations better than the sum of electrostatic and desolvation terms we used in our previous work. 3 However, the discriminatory ability of the potential, ACP plus electrostatics (ACPϩElec), was substantially improved by the addition of a van der Waals term; therefore, we tested whether RPScore can also be improved in a similar manner (i.e., by accounting for the van der Waals energy).…”

Section: Introductionmentioning

confidence: 99%

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Combination of scoring functions improves discrimination in protein–protein docking

et al. 2003

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Two structure-based potentials are used for both filtering (i.e., selecting a subset of conformations generated by rigid-body docking), and rescoring and ranking the selected conformations. ACP (atomic contact potential) is an atomlevel extension of the Miyazawa-Jernigan potential parameterized on protein structures, whereas RPScore (residue potential score) is a residue-level potential, based on interactions in protein-protein complexes. These potentials are combined with other energy terms and applied to 13 sets of protein decoys, as well as to the results of docking 10 pairs of unbound proteins. For both potentials, the ability to discriminate between near-native and non-native docked structures is substantially improved by refining the structures and by adding a van der Waals energy term. It is observed that ACP and RPScore complement each other in a number of ways (e.g., although RPScore yields more hits than ACP, mainly as a result of its better performance for charged complexes, ACP usually ranks the near-native complexes higher). As a general solution to the proteindocking problem, we have found that the best discrimination strategies combine either an RPScore filter with an ACP-based scoring function, or an ACP-based filter with an RPScore-based scoring function. Thus, ACP and RPScore capture complementary structural information, and combining them in a multistage postprocessing protocol provides substantially better discrimination than the use of the same potential for both filtering and ranking the docked conformations. Proteins 2003; 52:000 -000.

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Section: Structure-based Potentialsmentioning

confidence: 99%

Section: Structure-based Potentialsmentioning

confidence: 99%

Section: Docking Filtering and Scoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of scoring functions improves discrimination in protein–protein docking

et al. 2003

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“…Soft docking (Jiang and Kim 1991;Palma et al 2000), Fourier correlation (Gabb et al 1997;Ritchie and Kemp 2000), and shape fitting methods (Shoichet and Kuntz 1991;Norel et al 1994Norel et al , 1995Norel et al , 1999, smooth the details of protein-protein interactions, thereby allowing clashes to occur, at least before minimization. Empirically derived, or trained, scoring functions (Weng et al 1996;Moont et al 1999;Palma et al 2000) have also been used to address the protein docking problem. Although these methods allow near-native structures to be identified, they cannot reliably distinguish the near-native complexes from non-native complexes when docking unbound proteins.…”

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confidence: 99%

Protein–protein docking with multiple residue conformations and residue substitutions

2002

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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.

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Scoring docked conformations generated by rigid-body protein-protein docking

et al. 2000

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Rigid‐body methods, particularly Fourier correlation techniques, are very efficient for docking bound (co‐crystallized) protein conformations using measures of surface complementarity as the target function. However, when docking unbound (separately crystallized) conformations, the method generally yields hundreds of false positive structures with good scores but high root mean square deviations (RMSDs). This paper describes a two‐step scoring algorithm that can discriminate near‐native conformations (with less than 5 Å RMSD) from other structures. The first step includes two rigid‐body filters that use the desolvation free energy and the electrostatic energy to select a manageable number of conformations for further processing, but are unable to eliminate all false positives. Complete discrimination is achieved in the second step that minimizes the molecular mechanics energy of the retained structures, and re‐ranks them with a combined free‐energy function which includes electrostatic, solvation, and van der Waals energy terms. After minimization, the improved fit in near‐native complex conformations provides the free‐energy gap required for discrimination. The algorithm has been developed and tested using docking decoys, i.e., docked conformations generated by Fourier correlation techniques. The decoy sets are available on the web for testing other discrimination procedures. Proteins 2000;40:525–537. © 2000 Wiley‐Liss, Inc.

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Use of pair potentials across protein interfaces in screening predicted docked complexes

Cited by 92 publications

References 0 publications

Combination of scoring functions improves discrimination in protein–protein docking

Combination of scoring functions improves discrimination in protein–protein docking

Protein–protein docking with multiple residue conformations and residue substitutions

Scoring docked conformations generated by rigid-body protein-protein docking

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