Prediction Of Ligand Binding Modes Research Articles

Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement.

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

FastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation.

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

MDM2 case study: computational protocol utilising protein flexibility and data mining improves ligand binding mode predictions

Recovery of the P53 tumour suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs, in particular, have become essential during computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to proteins based on binding free energy. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 based on RMSD values using AutoDock and AutoDock Vina employing protein binding site flexibility. Additional analysis suggests a data mining protocol using linear regression can isolate the particular flexible bonds necessary for future optimum docking results. The implementation of a flexible receptor protocol based on 'a priori' knowledge obtained from data mining will improve accuracy and reduce costs of high-throughput virtual screenings of potential cancer drugs targeting MDM2.

MDM2 case study: computational protocol utilising protein flexibility and data mining improves ligand binding mode predictions

Recovery of the P53 tumour suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs, in particular, have become essential during computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to proteins based on binding free energy. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 based on RMSD values using AutoDock and AutoDock Vina employing protein binding site flexibility. Additional analysis suggests a data mining protocol using linear regression can isolate the particular flexible bonds necessary for future optimum docking results. The implementation of a flexible receptor protocol based on 'a priori' knowledge obtained from data mining will improve accuracy and reduce costs of high-throughput virtual screenings of potential cancer drugs targeting MDM2.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking.

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Ligand Binding Mode Prediction by Docking: Mdm2/Mdmx Inhibitors as a Case Study

The p53-binding domains of Mdm2 and Mdmx, two negative regulators of the tumor suppressor p53, are validated targets for cancer therapeutics, but correct binding poses of some proven inhibitors, particularly the nutlins, have been difficult to obtain with standard docking procedures. Virtual screening pipelines typically draw from a database of compounds represented with 1D or 2D structural information from which one or more 3D conformations must be generated. These conformations are then passed to a docking algorithm that searches for optimal binding poses on the target protein. This work tests alternative pipelines using several commonly used conformation generation programs (LigPrep, ConfGen, MacroModel, and Corina/Rotate) and docking programs (GOLD, Glide, MOE-dock, and AutoDock Vina) for their ability to reproduce known poses for a series of Mdmx and/or Mdm2 inhibitors, including several nutlins. Most combinations of these programs using default settings fail to find correct poses for the nutlins but succeed for all other compounds. Docking success for the nutlin class requires either computationally intensive conformational exploration or an "anchoring" procedure that incorporates knowledge of the orientation of the central imidazoline ring.

Mapping histamine H4receptor–ligand binding modes

Computational prediction of ligand binding modes in G protein-coupled receptors (GPCRs) remains a challenging task. Systematic consideration of different protein modelling templates, ligand binding poses, and ligand protonation states in extensive molecular dynamics (MD) simulation studies enabled the prediction of ligand-specific mutation effects in the histamine H4receptor, a key player in inflammation.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC=82.2 and ROC((2%))=45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.

Analysis of c‐Met Kinase Domain Complexes: A New Specific Catalytic Site Receptor Model for Defining Binding Modes of ATP‐Competitive Ligands

The receptor tyrosine kinase c-Met have multiple roles during cancer development and is currently considered as an important target for molecularly targeted therapies. Structural knowledge of how compounds interact on c-Met catalytic site could guide structure-based drug design strategies towards more effective and selective anticancer drug candidates. However, although 17 crystal structures of c-Met complexed with adenosine triphosphate (ATP)-competitive kinase inhibitors are publicly available (August 2009), there are still open questions regarding the prediction of ligand binding modes. We have applied molecular modeling and molecular mechanics to analyze the distribution of ligands interaction energy on c-Met residues, and deduced a new model of the active site allowing for an unambiguous identification of ligand binding modes. We demonstrate that the binding of known ligands on the c-Met catalytic site involves seven identified structurally-distinct areas. Five of these match the generic kinase ATP binding site model built by Novartis scientists in the 1990s, while the two others are distinct allosteric regions that can be exploited by second generation kinase inhibitors such as Gleevec. We show here that c-Met can accept both such kinds of allosteric inhibitors, a very unusual feature in the kinase family that opens new grounds for highly specific drug design.

ChemInform Abstract: Knowledge Based Prediction of Ligand Binding Modes and Rational Inhibitor Design for Kinase Drug Discovery

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Knowledge Based Prediction of Ligand Binding Modes and Rational Inhibitor Design for Kinase Drug Discovery

ADVERTISEMENT RETURN TO ISSUEPerspectiveNEXTKnowledge Based Prediction of Ligand Binding Modes and Rational Inhibitor Design for Kinase Drug DiscoveryArup K. Ghose*§, Torsten Herbertz§, Douglas A. Pippin, Joseph M. Salvino, and John P. MallamoView Author Information Department of Medicinal Chemistry, Cephalon, Inc., 145 Brandywine Parkway, West Chester, Pennsylvania 19380* To whom correspondence should be addressed. Phone: (610) 738-6882. Fax: (610) 422-0325. E-mail: [email protected]§These two authors contributed equally.Cite this: J. Med. Chem. 2008, 51, 17, 5149–5171Publication Date (Web):August 19, 2008Publication History Received24 April 2008Published online19 August 2008Published inissue 11 September 2008https://doi.org/10.1021/jm800475yCopyright © 2008 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views9935Altmetric-Citations125LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Binding modes,Chemical structure,Ligands,Noncovalent interactions,Peptides and proteins Get e-Alerts

Guided docking approaches to structure-based design and screening.

With the number of protein-ligand complexes available in the Protein Data Bank constantly growing, structure-based approaches to drug design and screening have become increasingly important. Alongside this explosion of structural information, a number of molecular docking methods have been developed over the last years with the aim of maximally exploiting all available structural and chemical information that can be derived from proteins, from ligands, and from protein-ligand complexes. In this respect, the term 'guided docking' is introduced to refer to docking approaches that incorporate some degree of chemical information to actively guide the orientation of the ligand into the binding site. To reflect the focus on the use of chemical information, a classification scheme for guided docking approaches is proposed. In general terms, guided docking approaches can be divided into indirect and direct approaches. Indirect approaches incorporate chemical information implicitly, having an effect on scoring but not on orienting the ligand during sampling. In contrast, direct approaches incorporate chemical information explicitly, thus actively guiding the orientation of the ligand during sampling. Direct approaches can be further divided into protein-based, mapping-based, and ligand-based approaches to reflect the source used to derive the features capturing the chemical information inside the protein cavity. Within each category, a representative list of docking approaches is discussed. In view of the limitations of current scoring functions, it was generally found that making optimal use of chemical information represents an efficient knowledge-based strategy for improving binding affinity estimations, ligand binding-mode predictions, and virtual screening enrichments obtained from protein-ligand docking.

ABB WINS US$180M SUBSEA SYSTEM ORDER

This review will focus on the construction, refinement, and validation of G-protein-coupled receptor (GPCR) structural models for the purpose of structure-based virtual screening (SBVS) and ligand design. The review will present a comparative analysis of GPCR crystal structures and their implication on GPCR (homology) modeling. The challenges associated with steps along the modeling workflow will be discussed: the use of experimental anchors to steer the modeling procedure, amino acid sequence alignment and template selection, receptor structure refinement, loop modeling, ligand-binding mode prediction, and virtual screening for novel ligands. An overview of several successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family.