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 format. However, ClusPro also offers a number of advanced options to modify the search that include the removal of unstructured protein regions, applying attraction or repulsion, accounting for pairwise distance restraints, constructing homo-multimers, considering small angle X-ray scattering (SAXS) data, and finding heparin binding sites. Six different energy functions can be used depending on the type of proteins. 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 hours.

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 format. FTMap samples billions of positions of small organic molecules used as probes and 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 basis for other servers, namely FTSite to predict ligand binding sites, FTFlex to account for side chain flexibility, FTMap/param to parameterize additional probes, and FTDyn to map ensembles of protein structures. Applications include determining 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 is much faster than the more recent approaches to protein mapping based on mixed molecular dynamics. Using 16 probe molecules, the FTMap server finds the hot spots of an average size protein in less than an hour. Since FTFlex performs mapping for all low energy conformers of side chains in the binding site, its completion time is proportionately longer.

FTMAP is available as a server at http://ftmap.bu.edu/.

The protein docking server ClusPro has been participating in CAPRI since its introduction in 2004. This paper 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 hours 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.

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.

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.fragment-based drug discovery | ligand binding site | inhibitor design | side-chain adjustment

Key to the pharmaceutical utility of certain macrocyclic drugs is a “chameleonic” ability to change their conformation to expose polar groups in aqueous solution, but bury them when traversing lipid membranes. Based on analysis of the structures of 20 macrocyclic compounds that are approved oral drugs, we propose that good solubility requires a topological polar surface area (TPSA, in Å2) of ≥0.2 × MW. Meanwhile, good passive membrane permeability requires a molecular (i.e. 3D) PSA in nonpolar environments of ≤140 Å2. We show that one or other of these limits is almost invariably violated for compounds with MW > 600 Da., suggesting that some degree of chameleonic behavior is required for most high MW oral drugs.

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. Next, 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 proteinprotein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. 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.