An iterative knowledge‐based scoring function for protein–protein recognition

Abstract

Using an efficient iterative method, we have developed a distance-dependent knowledge-based scoring function to predict protein-protein interactions. The function, referred to as ITScore-PP, was derived using the crystal structures of a training set of 851 protein-protein dimeric complexes containing true biological interfaces. The key idea of the iterative method for deriving ITScore-PP is to improve the interatomic pair potentials by iteration, until the pair potentials can distinguish true binding modes from decoy modes for the protein-protein complexes in the training set. The iterative method circumvents the challenging reference state problem in deriving knowledge-based potentials. The derived scoring function was used to evaluate the ligand orientations generated by ZDOCK 2.1 and the native ligand structures on a diverse set of 91 protein-protein complexes. For the bound test cases, ITScore-PP yielded a success rate of 98.9% if the top 10 ranked orientations were considered. For the more realistic unbound test cases, the corresponding success rate was 40.7%. Furthermore, for faster orientational sampling purpose, several residue-level knowledge-based scoring functions were also derived following the similar iterative procedure. Among them, the scoring function that uses the side-chain center of mass (SCM) to represent a residue, referred to as ITScore-PP(SCM), showed the best performance and yielded success rates of 71.4% and 30.8% for the bound and unbound cases, respectively, when the top 10 orientations were considered. ITScore-PP was further tested using two other published protein-protein docking decoy sets, the ZDOCK decoy set and the RosettaDock decoy set. In addition to binding mode prediction, the binding scores predicted by ITScore-PP also correlated well with the experimentally determined binding affinities, yielding a correlation coefficient of R = 0.71 on a test set of 74 protein-protein complexes with known affinities. ITScore-PP is computationally efficient. The average run time for ITScore-PP was about 0.03 second per orientation (including optimization) on a personal computer with 3.2 GHz Pentium IV CPU and 3.0 GB RAM. The computational speed of ITScore-PP(SCM) is about an order of magnitude faster than that of ITScore-PP. ITScore-PP and/or ITScore-PP(SCM) can be combined with efficient protein docking software to study protein-protein recognition.

Ion sensing in the RCK1 domain of BK channels

“…Then, the three flexible segments around the D367 binding site were remodeled by sampling possible conformations using the LOOPY modeling program (37). To further construct a model for Ca 2+ -bound RCK1-RCK2 complex, the modeled Ca 2+ -bound RCK1 and the experimental Ca 2+ -bound RCK2 of 3MT5 (10) were docked together by using our proteinprotein docking program MDockPP (38). The docked RCK1-RCK2 complex was further optimized/minimized by using Amber force fields (39).…”

Flexible Protein-Protein Docking

“…An ideal scoring function should recognize favourable native contacts as found in the bound complex and discriminate those from non-native contacts with lower scores. Scoring can be based on a physical force field with optimized weights on the energetic contributions (Dominguez et al, 2003;Bonvin, 2006;Audie, 2009) or can involve knowledge-based statistical potentials derived from known protein protein complex structures (Gottschalk et al, 2004;Zhang et al, 2005;Huang & Zou, 2008). Often a single descriptor (e.g.…”

“…Based on these statistics it is possible to design knowledge-based scoring functions which in general compare the frequency of contact pairs in known interfaces with the expected frequency if residues or atoms would randomly distributed at interfaces. Effective knowledge-based potentials have been developed that are based on contact preferences of amino acids at known interfaces compared to interfaces of non-native decoy complexes (Huang & Zou, 2008;Ravikant & Elber, 2009;Kowalsman & Eisenstein, 2009). The resulting contact or distance dependent pair-potentials can improve the scoring of near-native complexes.…”

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

“…The first approach uses a linear combination of energy terms, while the second approach is statistics-based or "knowledge-based", as it uses properties derived from experimental structures of protein-protein complexes, usually embodied in atom-atom or residue-residue potentials. CONSRANK thus deeply differs from other valuable algorithms in the field [22][23][24][25][26][27][28][29][30][31][32][33][34][35], as it uses neither knowledge-based nor physics-based energy functions. Application of CONSRANK to the ranking of over 110 targets from different sources showed a very good performance, as it was able to consistently enrich the top ranked positions in correct solutions, provided that they represented an appreciable fraction of the total models [21,36].…”