Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition

How to use not-always-reliable binding site information in protein-protein docking prediction

In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.

ASPDock: protein-protein docking algorithm using atomic solvation parameters model

Background

Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock).

Results

The ASPDock is first tested on the 21 complexes whose binding free energies have been determined experimentally. The results show that the calculated ASP scores have stronger correlation (r ≈ 0.69) with the binding free energies than the pure shape complementarity scores (r ≈ 0.48). The ASPDock is further tested on a large dataset, the benchmark 3.0, which contain 124 complexes and also shows better performance than pure shape complementarity method in docking prediction. Comparisons with other state-of-the-art docking algorithms showed that ASP score indeed gives higher success rate than the pure shape complementarity score of FTDock but lower success rate than Zdock3.0. We also developed a softly restricting method to add the information of predicted binding sites into our docking algorithm. The ASP-based docking method performed well in CAPRI rounds 18 and 19.

Conclusions

ASP may be more accurate and physical than the pure shape complementarity in describing the feature of protein docking.

Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin

Antibodies play an increasingly important role in both basic research and the pharmaceutical industry. Since their efficiency depends, in ultimate analysis, on their atomic interactions with an antigen, studying such interactions is important to understand how they function and, in the long run, to design new molecules with desired properties. Computational docking, the process of predicting the conformation of a complex from its separated components, is emerging as a fast and affordable technique for the structural characterization of antibody-antigen complexes. In this manuscript, we first describe the different computational strategies for the modeling of antibodies and docking of their complexes, and then predict the binding of two antibodies to the stalk region of influenza hemagglutinin, an important pharmaceutical target. The purpose is two-fold: on a general note, we want to illustrate the advantages and pitfalls of computational docking with a practical example, using different approaches and comparing the results to known experimental structures. On a more specific note, we want to assess if docking can be successful in characterizing the binding to the same influenza epitope of other antibodies with unknown structure, which has practical relevance for pharmaceutical and biological research. The paper clearly shows that some of the computational docking predictions can be very accurate, but the algorithm often fails to discriminate them from inaccurate solutions. It is of paramount importance, therefore, to use rapidly obtained experimental data to validate the computational results.

A holistic molecular docking approach for predicting protein-protein complex structure

A holistic protein-protein molecular docking approach, HoDock, was established, composed of such steps as binding site prediction, initial complex structure sampling, refined complex structure sampling, structure clustering, scoring and final structure selection. This article explains the detailed steps and applications for CAPRI Target 39. The CAPRI result showed that three predicted binding site residues, A191HIS, B512ARG and B531ARG, were correct, and there were five submitted structures with a high fraction of correct receptor-ligand interface residues, indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.

Automatic prediction of protein interactions with large scale motion

Proteins often change their conformation upon binding to other molecules. Taking these conformational changes into account in docking is an extremely difficult task: the larger the scale of the motion the harder it is to predict the structure of the association complex. Here, we present a fully automated method for flexible docking with large scale motion in one of the docked molecules. The method automatically identifies hinge regions and rigid parts and then docks the input molecules while explicitly considering the hinges and possible protein motions.

Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12

In CAPRI rounds 6-12, RosettaDock successfully predicted 2 of 5 unbound-unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid-body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound-unbound and homology model binding targets, Rounds 6-12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data.

A filter enhanced sampling and combinatorial scoring study for protein docking in CAPRI

Protein-protein docking is usually exploited with a two-step strategy, i.e., conformational sampling and decoy scoring. In this work, a new filter enhanced sampling scheme was proposed and added into the RosettaDock algorithm to improve the conformational sampling efficiency. The filter term is based on the statistical result that backbone hydrogen bonds in the native protein structures are wrapped by more than nine hydrophobic groups to shield them from attacks of water molecules (Fernandez and Scheraga, Proc Natl Acad Sci USA 2003;100:113-118). A combinatorial scoring function, ComScore, specially designed for the other-type protein-protein complexes was also adopted to select the near native docked modes. ComScore was composed of the atomic contact energy, van der Waals, and electrostatic interaction energies, and the weight of each item was fit through the multiple linear regression approach. To analyze our docking results, the filter enhanced sampling scheme was applied to targets T12, T20, and T21 after the CAPRI blind test, and improvements were obtained. The ligand least root mean square deviations (L_rmsds) were reduced and the hit numbers were increased. ComScore was used in the scoring test for CAPRI rounds 9-12 with good success in rounds 9 and 11.

Complex-type-dependent scoring functions in protein–protein docking

A major challenge in the field of protein-protein docking is to discriminate between the many wrong and few near-native conformations, i.e. scoring. Here, we introduce combinatorial complex-type-dependent scoring functions for different types of protein-protein complexes, protease/inhibitor, antibody/antigen, enzyme/inhibitor and others. The scoring functions incorporate both physical and knowledge-based potentials, i.e. atomic contact energy (ACE), the residue pair potential (RP), electrostatic and van der Waals' interactions. For different type complexes, the weights of the scoring functions were optimized by the multiple linear regression method, in which only top 300 structures with ligand root mean square deviation (L_RMSD) less than 20 A from the bound (co-crystallized) docking of 57 complexes were used to construct a training set. We employed the bound docking studies to examine the quality of the scoring function, and also extend to the unbound (separately crystallized) docking studies and extra 8 protein-protein complexes. In bound docking of the 57 cases, the first hits of protease/inhibitor cases are all ranked in the top 5. For the cases of antibody/antigen, enzyme/inhibitor and others, there are 17/19, 5/6 and 13/15 cases with the first hits ranked in the top 10, respectively. In unbound docking studies, the first hits of 9/17 protease/inhibitor, 6/19 antibody/antigen, 1/6 enzyme/inhibitor and 6/15 others' complexes are ranked in the top 10. Additionally, for the extra 8 cases, the first hits of the two protease/inhibitor cases are ranked in the top for the bound and unbound test. For the two enzyme/inhibitor cases, the first hits are ranked 1st for bound test, and the 119th and 17th for the unbound test. For the others, the ranks of the first hits are the 1st for the bound test and the 12th for the 1WQ1 unbound test. To some extent, the results validated our divide-and-conquer strategy in the docking study, which might hopefully shed light on the prediction of protein-protein interactions.

Implicit flexibility in protein docking: Cross-docking and local refinement

In previous CAPRI rounds (3-5) we showed that using MD-generated ensembles, as inputs for a rigid-body docking algorithm, increased our success rate, especially for targets exhibiting substantial amounts of induced fit. In recent rounds (6-11), our cross-docking was followed by a short MD-based local refinement for the subset of solutions with the lowest interaction energies after minimization. The above approach showed promising results for target 20, where we were able to recover 30% of native contacts for one of our submitted models. Further tests, performed a posteriori, revealed that cross-docking approach produces more near-native (NN) solutions but only for targets with large conformational changes upon binding. However, at the time of the blind docking experiment, these improved solutions were not chosen for the subsequent refinement, as their interaction energies after minimization ranked poorly compared with other solutions. This indicates deficiencies in the present scoring schemes that are based on interaction energies of minimized structures. Refinement MD simulations substantially increase the fraction of native contacts for NN docked solutions, but generally worsen interface and ligand RMSD. Further analysis shows that although MD simulations are able to improve sidechain packing across the interface, which results in an increased fraction of native contacts, they are not capable of improving interface and ligand backbone RMSD for NN structures beyond 1.5 and 3.5 A, respectively, even if explicit solvent is used.

A new protein-protein docking scoring function based on interface residue properties

MOTIVATION

Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them.

RESULTS

To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions.

AVAILABILITY

http://genomics.eu.org/spip/-Bioinformatics-tools-