How good is automated protein docking? Kozakov, Dima; Beglov, Dmitri; Bohnuud, Tanggis ...
Proteins,
December 2013, Volume:
81, Issue:
12
Journal Article
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.
Abstract
Novel modalities such as PROTAC and RNAi have the ability to inadvertently alter the abundance of endogenous proteins. Currently available in vitro secondary pharmacology assays, which ...evaluate off-target binding or activity of small molecules, do not fully assess the off-target effects of PROTAC and are not applicable to RNAi. To address this gap, we developed a proteomics-based platform to comprehensively evaluate the abundance of off-target proteins. First, we selected off-target proteins using genetics and pharmacology evidence. This process yielded 2813 proteins, which we refer to as the “selected off-target proteome” (SOTP). An iterative algorithm was then used to identify four human cell lines out of 932. The 4 cell lines collectively expressed ~ 80% of the SOTP based on transcriptome data. Second, we used mass spectrometry to quantify the intracellular and extracellular proteins from the selected cell lines. Among over 10,000 quantifiable proteins identified, 1828 were part of the predefined SOTP. The SOTP was designed to be easily modified or expanded, owing to the rational selection process developed and the label free LC–MS/MS approach chosen. This versatility inherent to our platform is essential to design fit-for-purpose studies that can address the dynamic questions faced in investigative toxicology.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
An effective docking algorithm for antibody-protein antigen complex prediction is an important first step toward design of biologics and vaccines. We have recently developed a new class of ...knowledge-based interaction potentials called Decoys as the Reference State (DARS) and incorporated DARS into the docking program PIPER based on the fast Fourier transform correlation approach. Although PIPER was the best performer in the latest rounds of the CAPRI protein docking experiment, it is much less accurate for docking antibody-protein antigen pairs than other types of complexes, in spite of incorporating sequence-based information on the location of the paratope. Analysis of antibody-protein antigen complexes has revealed an inherent asymmetry within these interfaces. Specifically, phenylalanine, tryptophan and tyrosine residues highly populate the paratope of the antibody but not the epitope of the antigen.
Since this asymmetry cannot be adequately modeled using a symmetric pairwise potential, we have removed the usual assumption of symmetry. Interaction statistics were extracted from antibody-protein complexes under the assumption that a particular atom on the antibody is different from the same atom on the antigen protein. The use of the new potential significantly improves the performance of docking for antibody-protein antigen complexes, even without any sequence information on the location of the paratope. We note that the asymmetric potential captures the effects of the multi-body interactions inherent to the complex environment in the antibody-protein antigen interface.
The method is implemented in the ClusPro protein docking server, available at http://cluspro.bu.edu.
Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the ...mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small "probe" molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Peptide-mediated interactions are of primordial importance to the cell, and the structure of such interaction provides an important starting point for their further characterization. In many cases, ...the structure of the peptide-protein complex has not been solved by experiment, and modeling tools need to be applied to generate structural models of the interaction. PeptiMap is a protocol that identifies the peptide-binding site when only the structure of the receptor is known, but no information about where the peptide binds is available. This is achieved by mapping the surface for solvents to identify ligand-binding sites, similar in approach to ANCHORMAP in which amino acids are mapped. Peptimap is a free open access web-based server. It can be accessed at http://peptimap.cluspro.org .
We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) ...that are very similar to the peptide's final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.
The method is available as part of the protein-protein docking server ClusPro at https://peptidock.cluspro.org/nousername.php.
midas@laufercenter.org or oraf@ekmd.huji.ac.il.
Supplementary data are available at Bioinformatics online.
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