Scoring protein-protein docked structures based on the balance and tightness of binding

Modeling Complexes of Transmembrane Proteins: Systematic Analysis of ProteinProtein Docking Tools

Proteinprotein docking methodology is frequently used to model complexes of transmembrane proteins, in particular oligomers of G protein-coupled receptors (GPCRs), even if its applicability for these systems has never been fully validated. The aim of this work is to perform a systematic study on the suitability of some widely-used proteinprotein docking software for modeling complexes of transmembrane proteins. In this study we tested the programs ZDOCK, ClusPro, HEX, GRAMM-X, PatchDock, SymmDock, and HADDOCK, using a set of membrane protein oligomers for which the 3D structure has been obtained experimentally, including opsin dimer, the recently published chemokine CXCR4 and kappa opioid receptor dimers. The results show that the docking success depends on the applied docking algorithm and scoring functions, but also on inherent structural features of the transmembrane proteins. Thus, proteins with large interface surfaces, rich in surface cavities, high-order symmetry, and small conformational change upon complex formation are well predicted more often than proteins without these features. The results of this systematic analysis provide guidelines that can be used for obtaining reliable models of transmembrane proteins, including GPCRs. Therefore they can be useful for the application of structure-based methods in drug discovery projects involving these targets.

Optimized grid-based protein-protein docking as a global search tool followed by incorporating experimentally derivable restraints

Unbound protein docking, or the computational prediction of the structure of a protein complex from the structures of its separated components, is of importance but still challenging. A practical approach toward reliable results for unbound docking is to incorporate experimentally derived information with computation. To this end, truly systematic search of the global docking space is desirable. The fast Fourier transform (FFT) docking is a systematic search method with high computational efficiency. However, by using FFT to perform unbound docking, possible conformational changes upon binding must be treated implicitly. To better accommodate the implicit treatment of conformational flexibility, we develop a rational approach to optimize "softened" parameters for FFT docking. In connection with the increased "softness" of the parameters in this global search step, we use a revised rule to select candidate models from the search results. For complexes designated as of low and medium difficulty for unbound docking, these adaptations of the original FTDOCK program lead to substantial improvements of the global search results. Finally, we show that models resulted from FFT-based global search can be further filtered with restraints derivable from nuclear magnetic resonance (NMR) chemical shift perturbation or mutagenesis experiments, leading to a small set of models that can be feasibly refined and evaluated using computationally more expensive methods and that still include high-ranking near-native conformations.

Direct estimation of entropy loss due to reduced translational and rotational motions upon molecular binding

The entropic cost due to the loss of translational and rotational (T-R) degree of freedom upon binding has been well recognized for several decades. Tightly bound ligands have higher entropic costs than loosely bound ligands. Quantifying the ligand's residual T-R motions after binding, however, is not an easy task. We describe an approach that uses a reduced Hessian matrix to estimate the contributions due to translational and rotational degrees of freedom to entropy change upon molecular binding. The calculations use a harmonic model for the bound state but only include the T-R degrees of freedom. This approximation significantly speeds up entropy calculations because only 6 x 6 matrices need to be treated, which makes it easier to be used in computer-aided drug design for studying many ligands. The methodological connection with other methods is discussed as well. We tested this approximation by applying it to study the binding of ATP, peptide inhibitor (PKI), and several bound water molecules to protein kinase A (PKA). These ligands span a wide range in size. The model gave reasonable estimates of the residual T-R entropy of bound ligands or water molecules. The residual T-R entropy demonstrated a wide range of values, e.g., 4 to 16 cal/K.mol for the bound water molecules of PKA.