Crystal structures of MTH1187 and its yeast ortholog YBL001c.
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Amino Acid Sequence↗
Biomedical subjects
Barry Honig
Publications and source records attributed to Barry Honig.
Target Explorer: An automated tool for the identification of new target genes for a specified set of transcription factors.
With the increasing number of eukaryotic genomes available, high-throughput automated tools for identification of regulatory DNA sequences are becoming increasingly feasible. Several computational approaches for the prediction of regulatory elements were recently developed. Here we combine the prediction of clusters of binding sites for transcription factors with context information taken from genome annotations. Target Explorer automates the entire process from the creation of a customized library of binding sites for known transcription factors through the prediction and annotation of putative target genes that are potentially regulated by these factors. It was specifically designed for the well-annotated Drosophila melanogaster genome, but most options can be used for sequences from other genomes as well. Target Explorer is available at http://trantor.bioc.columbia.edu/Target_Explorer/
Animals↗
Structural genomics: computational methods for structure analysis.
The success of structural genomics initiatives requires the development and application of tools for structure analysis, prediction, and annotation. In this paper we review recent developments in these areas; specifically structure alignment, the detection of remote homologs and analogs, homology modeling and the use of structures to predict function. We also discuss various rationales for structural genomics initiatives. These include the structure-based clustering of sequence space and genome-wide function assignment. It is also argued that structural genomics can be integrated into more traditional biological research if specific biological questions are included in target selection strategies.
Amino Acid Motifs↗
Solution structure of Vibrio cholerae protein VC0424: a variation of the ferredoxin-like fold.
The structure of Vibrio cholerae protein VC0424 was determined by NMR spectroscopy. VC0424 belongs to a conserved family of bacterial proteins of unknown function (COG 3076). The structure has an alpha-beta sandwich architecture consisting of two layers: a four-stranded antiparallel beta-sheet and three side-by-side alpha-helices. The secondary structure elements have the order alphabetaalphabetabetaalphabeta along the sequence. This fold is the same as the ferredoxin-like fold, except with an additional long N-terminal helix, making it a variation on this common motif. A cluster of conserved surface residues on the beta-sheet side of the protein forms a pocket that may be important for the biological function of this conserved family of proteins.
Amino Acid Sequence↗
On the role of the crystal environment in determining protein side-chain conformations.
The role of crystal packing in determining the observed conformations of amino acid side-chains in protein crystals is investigated by (1) analysis of a database of proteins that have been crystallized in different unit cells (space group or unit cell dimensions) and (2) theoretical predictions of side-chain conformations with the crystal environment explicitly represented. Both of these approaches indicate that the crystal environment plays an important role in determining the conformations of polar side-chains on the surfaces of proteins. Inclusion of the crystal environment permits a more sensitive measurement of the achievable accuracy of side-chain prediction programs, when validating against structures obtained by X-ray crystallography. Our side-chain prediction program uses an all-atom force field and a Generalized Born model of solvation and is thus capable of modeling simple packing effects (i.e. van der Waals interactions), electrostatic effects, and desolvation, which are all important mechanisms by which the crystal environment impacts observed side-chain conformations. Our results are also relevant to the understanding of changes in side-chain conformation that may result from ligand docking and protein-protein association, insofar as the results reveal how side-chain conformations change in response to their local environment.
Computer Simulation↗
Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction.
In this paper, we introduce a method to account for the shape of the potential energy curve in the evaluation of conformational free energies. The method is based on a procedure that generates a set of conformations, each with its own force-field energy, but adds a term to this energy that favors conformations that are close in structure (have a low rmsd) to other conformations. The sum of the force-field energy and rmsd-dependent term is defined here as the "colony energy" of a given conformation, because each conformation that is generated is viewed as representing a colony of points. The use of the colony energy tends to select conformations that are located in broad energy basins. The approach is applied to the ab initio prediction of the conformations of all of the loops in a dataset of 135 nonredundant proteins. By using an rmsd from a native criterion based on the superposition of loop stems, the average rmsd of 5-, 6-, 7-, and 8-residue long loops is 0.85, 0.92, 1.23, and 1.45 A, respectively. For 8-residue loops, 60 of 61 predictions have an rmsd of less than 3.0 A. The use of the colony energy is found to improve significantly the results obtained from the potential function alone. (The loop prediction program, "Loopy," can be downloaded at http://trantor.bioc.columbia.edu.)
Probability↗
On the role of electrostatic interactions in the design of protein-protein interfaces.
Here, the methods of continuum electrostatics are used to investigate the contribution of electrostatic interactions to the binding of four protein-protein complexes; barnase-barstar, human growth hormone and its receptor, subtype N9 influenza virus neuraminidase and the NC41 antibody, the Ras binding domain (RBD) of kinase cRaf and a Ras homologue Rap1A. In two of the four complexes electrostatics are found to strongly oppose binding (hormone-receptor and neuraminidase-antibody complexes), in one case the net effect is close to zero (barnase-barstar) and in one case electrostatics provides a significant driving force favoring binding (RBD-Rap1A). In order to help understand the wide range of electrostatic contributions that were calculated, the electrostatic free energy was partitioned into contributions of individual charged and polar residues, salt bridges and networks involving salt bridges and hydrogen bonds. Although there is no one structural feature that accounts for the differences between the four interfaces, the extent to which the desolvation of buried charges is compensated by the formation of hydrogen bonds and ion pairs appears to be an important factor. Structural features that are correlated with contribution of an individual residue to stability are also discussed. These include partial burial of a charged group in the free monomer, the formation of networks involving charged and polar amino acids, and the formation of partially exposed ion-pairs. The total electrostatic contribution to binding is found to be inversely correlated with buried total and non-polar surface area. This suggests that different interfaces can be designed to exploit electrostatic and hydrophobic forces in very different ways.
Antigen-Antibody Reactions↗
Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: applications to the molecular systems and geometric objects.
This article describes a number of algorithms that are designed to improve both the efficiency and accuracy of finite difference solutions to the Poisson-Boltzmann equation (the FDPB method) and to extend its range of application. The algorithms are incorporated in the DelPhi program. The first algorithm involves an efficient and accurate semianalytical method to map the molecular surface of a molecule onto a three-dimensional lattice. This method constitutes a significant improvement over existing methods in terms of its combination of speed and accuracy. The DelPhi program has also been expanded to allow the definition of geometrical objects such as spheres, cylinders, cones, and parallelepipeds, which can be used to describe a system that may also include a standard atomic level depiction of molecules. Each object can have a different dielectric constant and a different surface or volume charge distribution. The improved definition of the surface leads to increased precision in the numerical solutions of the PB equation that are obtained. A further improvement in the precision of solvation energy calculations is obtained from a procedure that calculates induced surface charges from the FDPB solutions and then uses these charges in the calculation of reaction field energies. The program allows for finite difference grids of large dimension; currently a maximum of 571(3) can be used on molecules containing several thousand atoms and charges. As described elsewhere, DelPhi can also treat mixed salt systems containing mono- and divalent ions and provide electrostatic free energies as defined by the nonlinear PB equation.
Journal Article↗
Electrostatic control of the membrane targeting of C2 domains.
Many proteins involved in signal transduction and vesicle trafficking contain C2 domains whose membrane association is often regulated by calcium. Here, finite-difference Poisson-Boltzmann calculations are used to describe the electrostatic interactions between C2 domains of known structure and phospholipid membranes. The results explain how calcium binding can drive the association of some C2 domains to negatively charged membranes and others to neutral, zwitterionic membranes. Nonspecific electrostatic interactions are shown to be a general feature of many C2 domains of known structure, including the calcium-independent C2 domain of the PTEN tumor suppressor.
Animals↗
Bioinformatics in structural genomics.
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Computational Biology↗