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: 5.45). 04/2007; 50(6):1231-40. DOI: 10.1021/jm061134b
Source: PubMed


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.

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    • "Identification of hot spots and binding pockets was done using a computational fragment probe mapping methodology [19], [44], [45] (see also the Supporting Information text file S1). During this process, 15 different small molecules (fragment probes) (Fig. 1B) were docked onto the surfaces of the whole conformational set as described in the Supporting Information text file S1. "
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    ABSTRACT: The misfolding of intrinsically disordered proteins such as α-synuclein, tau and the Aβ peptide has been associated with many highly debilitating neurodegenerative syndromes including Parkinson's and Alzheimer's diseases. Therapeutic targeting of the monomeric state of such intrinsically disordered proteins by small molecules has, however, been a major challenge because of their heterogeneous conformational properties. We show here that a combination of computational and experimental techniques has led to the identification of a drug-like phenyl-sulfonamide compound (ELN484228), that targets α-synuclein, a key protein in Parkinson's disease. We found that this compound has substantial biological activity in cellular models of α-synuclein-mediated dysfunction, including rescue of α-synuclein-induced disruption of vesicle trafficking and dopaminergic neuronal loss and neurite retraction most likely by reducing the amount of α-synuclein targeted to sites of vesicle mobilization such as the synapse in neurons or the site of bead engulfment in microglial cells. These results indicate that targeting α-synuclein by small molecules represents a promising approach to the development of therapeutic treatments of Parkinson's disease and related conditions.
    PLoS ONE 02/2014; 9(2):e87133. DOI:10.1371/journal.pone.0087133 · 3.23 Impact Factor
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    • "Experimental data regarding the binding energies for a limited number of complexes is available mainly by Alanine Scanning Database (ASEdb) [12] and Binding Interface Database (BID) [31]. To complement experimental studies, computational methods are constantly being developed; using energy contribution [32], [33], [34], sequence [18], [19], [35], [36] and structure [16], [37], [38], [39], [40], [41] based information sources, mostly with learning tools [20], [36], [42], [43], [44] and simulation methods [45], [46]. Many servers are available, such as ISIS [47], FOLDEF [32], ROBETTA [33], K-FADE/K-CON/ROBETTA [42], MAPPIS [48], HotPoint [49], HotSprint [36], and pyDockNIP [50]. "
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    ABSTRACT: It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.
    PLoS ONE 09/2013; 8(9):e74320. DOI:10.1371/journal.pone.0074320 · 3.23 Impact Factor
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    • "Favorable binding regions of small organic probe molecules are determined via the following steps: (1) rigid body fragment docking using a fast Fourier transform approach, (2) minimization and rescoring of fragmentprotein complexes, (3) clustering and ranking of low-energy fragment-protein complexes, (4) consensus site determination . Populated consensus sites found by FTMap have been shown to agree with ligand binding sites identified using experimental methods (Landon et al, 2007; Brenke et al, 2009; Landon et al, 2009). "
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