Docking Success Rate Research Articles

Predicting protein-protein interactions with DrugScorePPI: fully-flexible docking, scoring, and in silicoalanine-scanning

Protein-protein complexes play key roles in all cellular signal transduction processes. Here, we present a fast and accurate computational approach to predict protein-protein interactions. The approach is based on DrugScorePPI, a knowledge-based scoring function for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. We developed the DrugScorePPI webserver [1], accessible at http://cpclab.uni-duesseldorf.de/dsppi, that is intended for identifying hotspot residues in protein-protein interfaces. For this, it allows performing computational alanine scanning of a protein-protein interface within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS and outperformed the widely used CC/PBSA, FoldX, and Robetta methods [1]. Next, DrugScorePPI was teamed with FRODOCK [2], a fast FFT-based protein-protein docking tool, in order to predict 3D structures of protein-protein complexes. When applied to datasets of 54 bound-bound (I) and 54 unbound-unbound (II) test cases, convincing results were obtained (docking success rate for complexes with rmsd < 10 A: I: ~80%; II: ~50%). Thus, we set out to evaluate whether our approach of deformable potential grids [3], previously developed for protein-ligand docking, also provides an accurate and efficient means for representing intermolecular interactions in fully-flexible protein-protein docking. The underlying idea is to adapt a 3D grid of potential field values, pre-calculated from an initial protein conformation by DrugScorePPI, to another conformation by moving grid intersection points in space, but keeping the potential field values constant. Protein movements are thereby translated into grid intersection displacements by coupling protein atoms to nearby grid intersection points by means of harmonic springs and modelling the irregular, deformable 3D grid as a homogeneous linear elastic body applying elasticity theory. Thus, new protein conformations can be sampled during a docking run without the need to re-calculate potential field values.

Docking by structural similarity at protein‐protein interfaces

Rapid accumulation of experimental data on protein-protein complexes drives the paradigm shift in protein docking from "traditional," template free approaches to template based techniques. Homology docking algorithms based on sequence similarity between target and template complexes can account for up to 20% of known protein-protein interactions. When highly homologous templates for the target complex are not available, but the structure of the target monomers is known, docking by local structural alignment may provide an adequate solution. Such an algorithm was developed based on the structural comparison of monomers to cocrystallized interfaces. A library of the interfaces was generated from cocrystallized protein-protein complexes in PDB. The partial structure alignment algorithm was validated on the DOCKGROUND benchmark sets. The optimal performance of the partial (interface) structure alignment was achieved with the interface residues defined by 12 Å distance across the interface. Overall, the partial structure alignment yielded more accurate models than the full structure alignment. Most templates identified by the partial structure alignment had low sequence identity to the target, which makes them hard to detect by sequence-based methods. The results indicate that the structure alignment techniques provide a much needed addition to the docking arsenal, with the combined structure alignment and template free docking success rate significantly surpassing that of the free docking alone.

An integrated suite of fast docking algorithms.

The CAPRI experiment (Critical Assessment of Predicted Interactions) simulates realistic and diverse docking challenges, each case having specific properties that may be exploited by docking algorithms. Motivated by the different CAPRI challenges, we developed and implemented a comprehensive suite of docking algorithms. These were incorporated into a dynamic docking protocol, consisting of four main stages: (1) Biological and bioinformatics research aiming to predict the binding site residues, to define distance constraints between interface atoms and to analyze the flexibility of molecules; (2) Rigid or flexible docking, performed by the PatchDock or FlexDock method, which utilizes the information gathered in the previous step. Symmetric complexes are predicted by the SymmDock method; (3) Flexible refinement and reranking of the rigid docking solution candidates, performed by FiberDock; and finally, (4) clustering and filtering the results based on energy funnels. We analyzed the performance of our docking protocol on a large benchmark and on recent CAPRI targets. The analysis has demonstrated the importance of biological information gathering prior to docking, which significantly increased the docking success rate, and of the refinement and rescoring stage that significantly improved the ranking of the rigid docking solutions. Our failures were mostly a result of mishandling backbone flexibility, inaccurate homology modeling, or incorrect biological assumptions. Most of the methods are available at http://bioinfo3d.cs.tau.ac.il/.

Docking by Structural Similarity at Protein-Protein Interfaces

Rapid accumulation of the experimental data on protein-protein complexes drives the paradigm shift in protein docking from ‘traditional,’ template free approaches to template based techniques. Homology docking algorithms are based on sequence similarity between target and template complexes and can account for up to 20% of known protein-protein interactions. When the highly homologous templates for the target complex are not available, but the structure of the target monomers is known, docking by local structural alignment may provide an adequate solution, as a complement to the traditional template-free docking, which is notoriously sensitive to structural inaccuracies. Such an algorithm was developed based on structural comparison of monomers to known co-crystallized interfaces. A library of templates was compiled, consisting of 11,932 interfacial fragments, extracted from the asymmetric and biological units in PDB. The structural alignment was performed by TM-align program. The results were optimal with the interfaces defined by 12A distance cutoff. The benchmarking of the procedure was performed on the DOCKGROUND docking benchmark sets: 99 unbound complexes and the extended set of 372 bound complexes. The models were ranked by TMscore. Higher-accuracy models (i-RMSD < 5A) were found in top 10 predictions for 25 % (unbound set) and 33 % (extended bound set) of targets. Importantly, most of the successfully predicted complexes were in addition to those predicted by template-free docking (by GRAMM-X). Compared with the full structure alignment, the partial structure alignment succeeded in a significant number of targets on which the full alignment failed. The method was also tested on previous CAPRI targets. The results indicate that the partial structure alignment provides a much needed addition to the docking arsenal, with the combined structural alignment and template free docking success rate significantly surpassing that of the template free docking alone.

Steering Protein−Ligand Docking with Quantitative NMR Chemical Shift Perturbations

Lead optimization benefits from including structural knowledge of the target. We present a new method that exploits quantitatively NMR amide proton chemical shift perturbations (CSP) on the protein side for protein-ligand docking. The approach is based on a hybrid scoring scheme consisting of a weighted sum of DrugScore, describing protein-ligand interactions, and Kendall's rank correlation coefficient, which scores ligand poses with respect to their agreement with experimental CSP data. For back-calculating CSP for a ligand pose, an efficient empirical model considering only ring-current effects is applied. The hybrid scoring scheme has been implemented in AutoDock. Compared to previous approaches, the rank correlation provides a measure that is more robust against the presence of outliers in back-calculated CSP data. Furthermore, our methods exploit CSP information at docking time and not for postfiltering, resulting in an enhanced generation of native-like solutions. As we exploit CSP information quantitatively, the experimental information effectively contributes to orient the ligand in the binding site. When applied to 70 protein-ligand complexes with computed CSP reference data, the docking success rate increases from 71%, if no CSP information is used, to 99% at the highest CSP weighting factor tested. Global optimization, thus, performs satisfactorily on the hybrid docking energy landscape. We next applied the approach to three test cases with experimental CSP reference data. Without CSP information, neither of the complexes is successfully docked. Including CSP information with the same CSP weighting factor, as determined above, leads to successful docking in all three cases. Only then native-like ligand configurations are generated at two of the three complexes. Binding site movements of up to 2 A are found to not deteriorate the docking success. The approach will be particularly important for protein-ligand complexes that are difficult to predict computationally, such as ligands binding to flat interface regions.

Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking

Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking

Rational Structure-Based Drug Design and Optimization in the Ligand-Binding Domain of the Glucocorticoid Receptor-α

Endogenous glucocorticoids (GCs) are involved in a range of endocrine functions including the metabolism of lipids, carbohydrates and proteins, stress response, fluid and electrolyte balance, as well as the maintenance of immunological, renal and skeletal homeostasis. There is a need to find agents that preserve the immune effects of GCs without side effects such as those affecting metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. In this review, we focus on the use of recent computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of structure-based (e.g., homology modeling and docking) studies that have been implemented and evaluate their success. By the end of 2008, there had been limited achievements utilizing docking studies and no published successes in the area of virtual high-throughput screening. However, the availability of novel crystal structures and the use of induced-fit docking protocols are improving docking success rates and promising to aid the future delivery of nonsteroidal ligands.

General and targeted statistical potentials for protein–ligand interactions

We present a novel atom-atom potential derived from a database of protein-ligand complexes. First, we clarify the similarities and differences between two statistical potentials described in the literature, PMF and Drugscore. We highlight shortcomings caused by an important factor unaccounted for in their reference states, and describe a new potential, which we name the Astex Statistical Potential (ASP). ASP's reference state considers the difference in exposure of protein atom types towards ligand binding sites. We show that this new potential predicts binding affinities with an accuracy similar to that of Goldscore and Chemscore. We investigate the influence of the choice of reference state by constructing two additional statistical potentials that differ from ASP only in this respect. The reference states in these two potentials are defined along the lines of Drugscore and PMF. In docking experiments, the potential using the new reference state proposed for ASP gives better success rates than when these literature reference states were used; a success rate similar to the established scoring functions Goldscore and Chemscore is achieved with ASP. This is the case both for a large, general validation set of protein-ligand structures and for small test sets of actives against four pharmaceutically relevant targets. Virtual screening experiments for these targets show less discrimination between the different reference states in terms of enrichment. In addition, we describe how statistical potentials can be used in the construction of targeted scoring functions. Examples are given for cdk2, using four different targeted scoring functions, biased towards increasingly large target-specific databases. Using these targeted scoring functions, docking success rates as well as enrichments are significantly better than for the general ASP scoring function. Results improve with the number of structures used in the construction of the target scoring functions, thus illustrating that these targeted ASP potentials can be continuously improved as new structural data become available.

A scoring function for docking ligands to low‐resolution protein structures

We present a docking method that uses a scoring function for protein-ligand docking that is designed to maximize the docking success rate for low-resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high- or low-resolution protein structures. We show that this docking method can be successfully applied to predict the ligand-binding site of low-resolution structures. For a set of 25 protein-ligand complexes, in 76% of the cases, more than 50% of ligand-contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 A RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand-contacting residue. Furthermore, using an 81-protein-ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand-contacting residues when native protein structures are used. Using 3 A RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand-binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein-ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater.

Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm.

The influence of various factors on the accuracy of protein-ligand docking is examined. The factors investigated include the role of a grid representation of protein-ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper-surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated-annealing-based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid-based approximations to explicit all-atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all-atom representation of the protein (full force field) is used, while a lower accuracy of 66-76% is observed for grid-based methods. All docking experiments considered a 41-member protein-ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid-based docking is achieved if the explicit all-atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower-accuracy grid-based energy representations can be effectively used when followed with full force field minimization. The results of these grid-based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures.