Identification of hot spots within druggable binding regions by computational solvent mapping of proteins

Bioinformatics Graduate Program, Boston University, 24 Cummington Street, Boston, Massachusetts, USA.
Journal of Medicinal Chemistry (Impact Factor: 5.48). 04/2007; 50(6):1231-40. DOI: 10.1021/jm061134b
Source: PubMed

ABSTRACT Here we apply the computational solvent mapping (CS-Map) algorithm toward the in silico identification of hot spots, that is, regions of protein binding sites that are major contributors to the binding energy and, hence, are prime targets in drug design. The CS-Map algorithm, developed for binding site characterization, moves small organic functional groups around the protein surface and determines their most energetically favorable binding positions. The utility of CS-Map algorithm toward the prediction of hot spot regions in druggable binding pockets is illustrated by three test systems: (1) renin aspartic protease, (2) a set of previously characterized druggable proteins, and (3) E. coli ketopantoate reductase. In each of the three studies, existing literature was used to verify our results. Based on our analyses, we conclude that the information provided by CS-Map can contribute substantially to the identification of hot spots, a necessary predecessor of fragment-based drug discovery efforts.

1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Influenza A continues to be a major public health concern due to its ability to cause epidemic and pandemic disease outbreaks in humans. Computational investigations of structural dynamics of the major influenza glycoproteins, especially the neuraminidase (NA) enzyme, are able to provide key insights beyond what is currently accessible with standard experimental techniques. In particular, all-atom molecular dynamics simulations reveal the varying degrees of flexibility for such enzymes. Here we present an analysis of the relative flexibility of the ligand- and receptor-binding area of three key strains of influenza A: highly pathogenic H5N1, the 2009 pandemic H1N1, and a human N2 strain. Through computational solvent mapping, we investigate the various ligand- and receptor-binding "hot spots" that exist on the surface of NA which interacts with both sialic acid receptors on the host cells and antiviral drugs. This analysis suggests that the variable cavities found in the different strains and their corresponding capacities to bind ligand functional groups may play an important role in the ability of NA to form competent reaction encounter complexes with other species of interest, including antiviral drugs, sialic acid receptors on the host cell surface, and the hemagglutinin protein. Such considerations may be especially useful for the prediction of how such complexes form and with what binding capacity.
    Journal of molecular and genetic medicine: an international journal of biomedical research 01/2012; 6:293-300. DOI:10.4172/1747-0862.1000052
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.
    Journal of Molecular Recognition 01/2009; 23(2):209-19. DOI:10.1002/jmr.984 · 2.34 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Assessing whether a protein structure is a good target or not before actually doing structure-based drug design on it is an important step to speed up the ligand discovery process. This is known as the "druggability" or "ligandability" assessment problem that has attracted increasing interest in recent years. The assessment typically includes the detection of ligand-binding sites on the protein surface and the prediction of their abilities to bind drug-like small molecules. A brief summary of the established methods of binding sites detection and druggability(ligandability) prediction, as well as a detailed description of the CAVITY approach developed in the authors' group was given. CAVITY showed good performance on ligand-binding site detection, and were successfully used to predict both the ligandabilities and druggabilities of the detected binding sites.
    Current pharmaceutical design 10/2012; DOI:10.2174/1381612811319120019 · 3.29 Impact Factor


Available from