Docking of the 5CITEP inhibitor to snapshots of a 2 ns HIV-1 integrase MD trajectory indicated a previously uncharacterized trench adjacent to the active site that intermittently opens. Further docking studies of novel ligands with the potential to bind to both regions showed greater selective affinity when able to bind to the trench. Our ranking of ligands is open to experimental testing, and our approach suggests a new target for HIV-1 therapeutics.
5CINECA Supercomputing Centre. 6University of Modena and Reggio Emilia.7Christian-Albrechts-University Kiel.8Michigan State University. International course and report were conceived by Pietro Cozzini and Glen E. Kellogg. * To whom correspondence should be addressed. For G.E.K.: Department of Medicinal Chemistry, Virginia Commonwealth University, Box 980540, Richmond, VA 23298-0540; (phone) 804-828-6452; (fax) 804-827-3664; (e-mail) glen.kellogg@vcu.edu. For P.C.: Department of General and Inorganic Chemistry, University of Parma, Via G.P. Usberti 17/A 43100, Parma, Italy; (phone) +39-0521-905669; (fax) +39-0521-905556; (e-mail) pietro.cozzini@unipr.it. NIH Public Access IntroductionStructure-based drug discovery has played an important role in medicinal chemistry 1 beginning nearly when the first X-ray crystal structure of the myoglobin and hemoglobin proteins at nearatomic resolution were described by Perutz, Kendrew and colleagues. 2-5 Even though only static structures were (and still generally are) used for most Structure-Based Drug Design (SBDD), and indeed most molecular modeling, the importance of flexibility was recognized immediately: hemoglobin has two rather different structures, "tense" and "relaxed", depending on its oxygenation, although in recent years a family of relaxed hemoglobin structures with different tertiary structure conformations have been reported. 6 In fact, all proteins are inherently flexible systems. This flexibility is frequently essential for function (e.g., as in hemoglobin). Proteins have an intrinsic ability to undergo functionally relevant conformational transitions under native state conditions, 7,8 on a wide range of scales, both in time and space. 9 In adenylate kinase large conformational changes due to movements of the nucleotide 'lids'-rate-limiting for overall catalytic turnover 10,11 -are 'linked' with relatively small-amplitude atomic fluctuations on the ps timescale such that changes in the local backbone conformation are required for lid closure. 12 Nuclear receptors are modular proteins where a significant degree of conformational flexibility is essential to biological function. Most of the pharmacology of nuclear receptor ligands has been discussed on the basis of their ability to stabilize (or displace) a short α-helix segment (known as H12 or AF-2) localized at the carboxy terminus of the receptor in (or from) its conformation in the protein "active" form. 13-15 Available X-ray crystal structures show a surprisingly wide range of structural diversity in ligands binding to, and inhibiting, nuclear receptor proteins such as the farnesoid X-receptor (FXR). 16,17 Protein dynamics is also a key component of intramolecular and intermolecular communication/signaling mechanisms and an essential requirement for the function of Gprotein coupled receptors (GPCRs), which are the largest known superfamily of membrane proteins. GPCRs regulate cell activity by transmitting extracellular signals to the inside of cells and respond to these signals by catalyzing nucleotide e...
A major shortcoming of empirical scoring functions for protein-ligand complexes is the low degree of correlation between predicted and experimental binding affinities, as frequently observed not only for large and diverse data sets but also for SAR series of individual targets. Improvements can be envisaged by developing new descriptors, employing larger training sets of higher quality, and resorting to more sophisticated regression methods. Herein, we describe the use of SFCscore descriptors to develop an improved scoring function by means of a PDBbind training set of 1005 complexes in combination with random forest for regression. This provided SFCscore(RF) as a new scoring function with significantly improved performance on the PDBbind and CSAR-NRC HiQ benchmarks in comparison to previously developed SFCscore functions. A leave-cluster-out cross-validation and performance in the CSAR 2012 scoring exercise point out remaining limitations but also directions for further improvements of SFCscore(RF) and empirical scoring functions in general.
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