Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.23.16 (HIV-1 protease)
 2,107 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The ability to predict ligand binding modes without the aid of wet-lab experiments may accelerate and reduce the cost of drug discovery research. Despite significant recent progress, virtual screening has not yet eliminated the need for wet-lab experiments. For example, after a lead compound has been identified, the precise binding mode is still typically determined by experimental structural biology. This structural knowledge is then employed to guide lead optimization. We present a step toward improving protein-ligand binding mode prediction for a set of ligands known to interact with a common protein. There is thus an important distinction between this work and traditional virtual screening algorithms. Whereas traditional approaches attempt to identify binding ligands from a large database of available compounds, our approach aims to more accurately predict the binding mode for a set of ligands which are already known to bind the target protein. The approach is based on the hypothesis that each active site contains a set of interaction points which binding ligands tend to exploit. In a more traditional context, these interaction points make up a pharmacophoric map. Our algorithm first performs traditional protein-ligand docking for each known binder. The ranked lists of candidate binding modes are then evaluated to identify a set of poses maximally self-consistent with respect to a pharmacophoric map generated from the same poses. We have extensively demonstrated the application of the algorithm to four protein systems (thrombin, cyclin-dependent kinase 2, dihydrofolate reductase, and HIV-1 protease) and attained predictions with an average RMSD < 2.5 A for all tested systems. This represents a typical improvement of 0.5-1.0 A (up to 25%) RMSD over the naive virtual docking predictions. Our algorithm is independent of the docking method and may significantly improve binding mode prediction of virtual docking experiments.
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PMID:Predicting multiple ligand binding modes using self-consistent pharmacophore hypotheses. 1971 52

The number of known three-dimensional protein sequences is orders of magnitude higher than the number of known protein structures. This is a result of an increase in large-scale genomic sequencing projects, the inability of proteins to crystallize or crystals to diffract well, or a simple lack of resources. An alternative is to use one of a variety of available homology modeling programs to produce a computational model of a protein. Protein models are produced using information from known protein structures found to be similar. Here, we compare the ability of a number of popular homology modeling programs to produce quality models from user-defined target-template sequence alignments over a range of circumstances including low sequence identity, variable sequence length, and when interfaced with a protein or small molecule. Programs evaluated include Prime, SWISS-MODEL, MOE, MODELLER, ROSETTA, Composer, ORCHESTRAR, and I-TASSER. Proteins to be modeled were chosen to test a range of sequence identities, sequence lengths, and protein motifs and all are of scientific importance. These include HIV-1 protease, kinases, dihydrofolate reductase, a viral capsid protein, and factor Xa among others. For the most part, the programs produce results that are similar. For example, all programs are able to produce reasonable models when sequence identities are >30% and all programs have difficulties producing complete models when sequence identities are lower. However, certain programs fare slightly better than others in certain situations and we attempt to provide insight on this topic.
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PMID:Comparison of common homology modeling algorithms: application of user-defined alignments. 2232 32

HIV-1 protease performs a vital step in the propagation of the HIV virus and is therefore an important drug target in the treatment of AIDS. It consists of a homodimer, with access to the active site limited by two protein flaps. NMR studies have identified two time scales of motions that occur in these flaps, and it is thought that the slower of these is responsible for a conformational change that makes the protein ligand-accessible. This motion occurs on a time scale outside that achievable using traditional molecular dynamics simulations. Reversible Digitally Filtered Molecular Dynamics (RDFMD) is a method that amplifies low frequency motions associated with conformational change and has recently been applied to, among others, E. coli dihydrofolate reductase, inducing a conformational change between known crystal structures. In this paper, the conformational motions of HIV-1 protease produced during MD and RDFMD simulations are presented, including movement between the known semiopen and closed conformations, and the opening and closing of the protein flaps.
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PMID:Conformational Motions of HIV-1 Protease Identified Using Reversible Digitally Filtered Molecular Dynamics. 2660 21


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