Dima Kozakov

PIPER: an FFT-based protein docking program with pairwise potentials.

The Fast Fourier Transform (FFT) correlation approach to protein–protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure‐based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein–protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme–inhibitor complexes. With the new FFT‐based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near‐native docked conformations. Although the potential is far from optimal for antibody–antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications. Proteins 2006.

Achieving reliability and high accuracy in automated protein docking: Cluspro, PIPER, SDU, and stability analysis in CAPRI rounds 13–19

Our approach to protein—protein docking includes three main steps. First, we run PIPER, a rigid body docking program based on the Fast Fourier Transform (FFT) correlation approach, extended to use pairwise interactions potentials. Second, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the stability of the clusters is analyzed by short Monte Carlo simulations, and the structures are refined by the medium‐range optimization method SDU. The first two steps of this approach are implemented in the ClusPro 2.0 protein–protein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. When comparing the models obtained in CAPRI rounds 13–19 by ClusPro, by the refinement of the ClusPro predictions and by all predictor groups, we arrived at three conclusions. First, for the first time in the CAPRI history, our automated ClusPro server was able to compete with the best human predictor groups. Second, selecting the top ranked models, our current protocol reliably generates high‐quality structures of protein–protein complexes from the structures of separately crystallized proteins, even in the absence of biological information, provided that there is limited backbone conformational change. Third, despite occasional successes, homology modeling requires further improvement to achieve reliable docking results. Proteins 2010.

How Good is Automated Protein Docking

The protein docking server ClusPro has been participating in critical assessment of prediction of interactions (CAPRI) since its introduction in 2004. This article evaluates the performance of ClusPro 2.0 for targets 46–58 in Rounds 22–27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 h and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near‐native conformations. Proteins 2013; 81:2159–2166.

Convergence and combination of methods in protein-protein docking

The analysis of results from Critical Assessment of Predicted Interactions (CAPRI), the first community-wide experiment devoted to protein docking, shows that all successful methods consist of multiple stages. The methods belong to three classes: global methods based on fast Fourier transforms (FFTs) or geometric matching, medium-range Monte Carlo methods, and the restraint-guided High Ambiguity Driven biomolecular DOCKing (HADDOCK) program. Although these classes of methods require very different amounts of information in addition to the structures of component proteins, they all share the same four computational steps: firstly, simplified and/or rigid body search; secondly, selecting the region(s) of interest; thirdly, refinement of docked structures; and fourthly, selecting the best models. Although each method is optimal for a specific class of docking problems, combining computational steps from different methods can improve the reliability and accuracy of results.

Structural conservation of druggable hot spots in protein–protein interfaces

Despite the growing number of examples of small-molecule inhibitors that disrupt protein–protein interactions (PPIs), the origin of druggability of such targets is poorly understood. To identify druggable sites in protein–protein interfaces we combine computational solvent mapping, which explores the protein surface using a variety of small “probe” molecules, with a conformer generator to account for side-chain flexibility. Applications to unliganded structures of 15 PPI target proteins show that the druggable sites comprise a cluster of binding hot spots, distinguishable from other regions of the protein due to their concave topology combined with a pattern of hydrophobic and polar functionality. This combination of properties confers on the hot spots a tendency to bind organic species possessing some polar groups decorating largely hydrophobic scaffolds. Thus, druggable sites at PPI are not simply sites that are complementary to particular organic functionality, but rather possess a general tendency to bind organic compounds with a variety of structures, including key side chains of the partner protein. Results also highlight the importance of conformational adaptivity at the binding site to allow the hot spots to expand to accommodate a ligand of drug-like dimensions. The critical components of this adaptivity are largely local, involving primarily low energy side-chain motions within 6 Å of a hot spot. The structural and physicochemical signature of druggable sites at PPI interfaces is sufficiently robust to be detectable from the structure of the unliganded protein, even when substantial conformational adaptation is required for optimal ligand binding.

How proteins bind macrocycles

The potential utility of synthetic macrocycles as drugs, particularly against low druggability targets such as protein-protein interactions, has been widely discussed. There is little information, however, to guide the design of macrocycles for good target protein-binding activity or bioavailability. To address this knowledge gap we analyze the binding modes of a representative set of macrocycle-protein complexes. The results, combined with consideration of the physicochemical properties of approved macrocyclic drugs, allow us to propose specific guidelines for the design of synthetic macrocycles libraries possessing structural and physicochemical features likely to favor strong binding to protein targets and also good bioavailability. We additionally provide evidence that large, natural product derived macrocycles can bind to targets that are not druggable by conventional, drug-like compounds, supporting the notion that natural product inspired synthetic macrocycles can expand the number of proteins that are druggable by synthetic small molecules.

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins.

FTMap is a computational mapping server that identifies binding hot spots of macromolecules—i.e., regions of the surface with major contributions to the ligand-binding free energy. To use FTMap, users submit a protein, DNA or RNA structure in PDB (Protein Data Bank) format. FTMap samples billions of positions of small organic molecules used as probes, and it scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots in good agreement with experimental data. FTMap serves as the basis for other servers, namely FTSite, which is used to predict ligand-binding sites, FTFlex, which is used to account for side chain flexibility, FTMap/param, used to parameterize additional probes and FTDyn, for mapping ensembles of protein structures. Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and it is much faster than the more-recent approaches to protein mapping based on mixed molecular dynamics. By using 16 probe molecules, the FTMap server finds the hot spots of an average-size protein in <1 h. As FTFlex performs mapping for all low-energy conformers of side chains in the binding site, its completion time is proportionately longer.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein–protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

Reversing chemoresistance by small molecule inhibition of the translation initiation complex eIF4F

Deregulation of cap-dependent translation is associated with cancer initiation and progression. The rate-limiting step of protein synthesis is the loading of ribosomes onto mRNA templates stimulated by the heterotrimeric complex, eukaryotic initiation factor (eIF)4F. This step represents an attractive target for anticancer drug discovery because it resides at the nexus of the TOR signaling pathway. We have undertaken an ultra-high-throughput screen to identify inhibitors that prevent assembly of the eIF4F complex. One of the identified compounds blocks interaction between two subunits of eIF4F. As a consequence, cap-dependent translation is inhibited. This compound can reverse tumor chemoresistance in a genetically engineered lymphoma mouse model by sensitizing cells to the proapoptotic action of DNA damage. Molecular modeling experiments provide insight into the mechanism of action of this small molecule inhibitor. Our experiments validate targeting the eIF4F complex as a strategy for cancer therapy to modulate chemosensitivity.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community‐wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community‐wide assessment of methods to predict the effects of mutations on protein–protein interactions. Twenty‐two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side‐chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large‐scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980–1987.