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Barry Honig

Publications and source records attributed to Barry Honig.

At least 19 recordsLinked to original sources
PubMed2007

Prediction of side-chain conformations on protein surfaces.

An approach is described that improves the prediction of the conformations of surface side chains in crystal structures, given the main-chain conformation of a protein. A key element of the methodology involves the use of the colony energy. This phenomenological term favors conformations found in frequently sampled regions, thereby approximating entropic effects and serving to smooth the potential energy surface. Use of the colony energy significantly improves prediction accuracy for surface side chains with little additional computational cost. Prediction accuracy was quantified as the percentage of side-chain dihedral angles predicted to be within 40 degrees of the angles measured by X-ray diffraction. Use of the colony energy in predictions for single side chains improved the prediction accuracy for chi(1) and chi(1+2) from 65 and 40% to 74 and 59%, respectively. Several other factors that affect prediction of surface side-chain conformations were also analyzed, including the extent of conformational sampling, details of the rotamer library employed, and accounting for the crystallographic environment. The prediction of conformations for polar residues on the surface was generally found to be more difficult than those for hydrophobic residues, except for polar residues participating in hydrogen bonds with other protein groups. For surface residues with hydrogen-bonded side chains, the prediction accuracy of chi(1) and chi(1+2) was 79 and 63%, respectively. For surface polar residues, in general (all side-chain prediction), the accuracy of chi(1) and chi(1+2) was only 73 and 56%, respectively. The most accurate results were obtained using the colony energy and an all-atom description that includes neighboring molecules in the crystal (protein chains and hetero atoms). Here, the accuracy of chi(1) and chi(1+2) predictions for surface side chains was 82 and 73%, respectively. The root mean square deviations obtained for hydrogen-bonding surface side chains were 1.64 and 1.81 A, with and without consideration of crystal packing effects, respectively.

Crystallography, X-Ray↗
PubMed2007

Poisson-Boltzmann calculations of nonspecific salt effects on protein-protein binding free energies.

The salt dependence of the binding free energy of five protein-protein hetero-dimers and two homo-dimers/tetramers was calculated from numerical solutions to the Poisson-Boltzmann equation. Overall, the agreement with experimental values is very good. In all cases except one involving the highly charged lactoglobulin homo-dimer, increasing the salt concentration is found both experimentally and theoretically to decrease the binding affinity. To clarify the source of salt effects, the salt-dependent free energy of binding is partitioned into screening terms and to self-energy terms that involve the interaction of the charge distribution of a monomer with its own ion atmosphere. In six of the seven complexes studied, screening makes the largest contribution but self-energy effects can also be significant. The calculated salt effects are found to be insensitive to force-field parameters and to the internal dielectric constant assigned to the monomers. Nonlinearities due to high charge densities, which are extremely important in the binding of proteins to negatively charged membrane surfaces and to nucleic acids, make much smaller contributions to the protein-protein complexes studied here, with the exception of highly charged lactoglobulin dimers. Our results indicate that the Poisson-Boltzmann equation captures much of the physical basis of the nonspecific salt dependence of protein-protein complexation.

Binding Sites↗
PubMed2006

Calculation of pKas in RNA: on the structural origins and functional roles of protonated nucleotides.

pK(a) calculations based on the Poisson-Boltzmann equation have been widely used to study proteins and, more recently, DNA. However, much less attention has been paid to the calculation of pK(a) shifts in RNA. There is accumulating evidence that protonated nucleotides can stabilize RNA structure and participate in enzyme catalysis within ribozymes. Here, we calculate the pK(a) shifts of nucleotides in RNA structures using numerical solutions to the Poisson-Boltzmann equation. We find that significant shifts are predicted for several nucleotides in two catalytic RNAs, the hairpin ribozyme and the hepatitis delta virus ribozyme, and that the shifts are likely to be related to their functions. We explore how different structural environments shift the pK(a)s of nucleotides from their solution values. RNA structures appear to use two basic strategies to shift pK(a)s: (a) the formation of compact structural motifs with structurally-conserved, electrostatic interactions; and (b) the arrangement of the phosphodiester backbone to focus negative electrostatic potential in specific regions.

Catalysis↗
PubMed2006

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering.

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.

Algorithms↗
PubMed2006

An unusual Zn-finger/FH2 domain protein controls a left/right asymmetric neuronal fate decision in C. elegans.

Gene regulatory networks that control the terminally differentiated state of a cell are, by and large, only superficially understood. In a mutant screen aimed at identifying regulators of gene batteries that define the differentiated state of two left/right asymmetric C. elegans gustatory neurons, ASEL and ASER, we have isolated a mutant, fozi-1, with a novel mixed-fate phenotype, characterized by de-repression of ASEL fate in ASER. fozi-1 codes for a protein that functions in the nucleus of ASER to inhibit the expression of the LIM homeobox gene lim-6, neuropeptide-encoding genes and putative chemoreceptors of the GCY gene family. The FOZI-1 protein displays a highly unusual domain architecture, that combines two functionally essential C2H2 zinc-finger domains, which are probably involved in transcriptional regulation, with a formin homology 2 (FH2) domain, normally found only in cytosolic regulators of the actin cytoskeleton. We demonstrate that the FH2 domain of FOZI-1 has lost its actin polymerization function but maintains its phylogenetically ancient ability to homodimerize. fozi-1 genetically interacts with several transcription factors and micro RNAs in the context of specific regulatory network motifs. These network motifs endow the system with properties that provide insights into how cells adopt their stable terminally differentiated states.

Alleles↗
PubMed2006

VISTAL--a new 2D visualization tool of protein 3D structural alignments.

UNLABELLED: We offer a tool, denoted VISTAL, for two-dimensional visualization of protein structural alignments. VISTAL describes aligned structures as a series of matched secondary structure elements, colored according to the three-dimensional distance of their Calpha atoms. AVAILABILITY: VISTAL can be downloaded from http://trantor.bioc.columbia.edu/~kolodny/software.html.

Algorithms↗
PubMed2006

On the nature of cavities on protein surfaces: application to the identification of drug-binding sites.

In this article we introduce a new method for the identification and the accurate characterization of protein surface cavities. The method is encoded in the program SCREEN (Surface Cavity REcognition and EvaluatioN). As a first test of the utility of our approach we used SCREEN to locate and analyze the surface cavities of a nonredundant set of 99 proteins cocrystallized with drugs. We find that this set of proteins has on average about 14 distinct cavities per protein. In all cases, a drug is bound at one (and sometimes more than one) of these cavities. Using cavity size alone as a criterion for predicting drug-binding sites yields a high balanced error rate of 15.7%, with only 71.7% coverage. Here we characterize each surface cavity by computing a comprehensive set of 408 physicochemical, structural, and geometric attributes. By applying modern machine learning techniques (Random Forests) we were able to develop a classifier that can identify drug-binding cavities with a balanced error rate of 7.2% and coverage of 88.9%. Only 18 of the 408 cavity attributes had a statistically significant role in the prediction. Of these 18 important attributes, almost all involved size and shape rather than physicochemical properties of the surface cavity. The implications of these results are discussed. A SCREEN Web server is available at http://interface.bioc.columbia.edu/screen.

Binding Sites↗
PubMed2006

Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction.

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.

Computational Biology↗
PubMed2006

On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins.

In this study, we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins and that acceptable models (with C alpha-RMSD values to the native of 2 A or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted to maximize the accuracy of homology models of membrane proteins.

Algorithms↗
PubMed2006

Type II cadherin ectodomain structures: implications for classical cadherin specificity.

Type I and II classical cadherins help to determine the adhesive specificities of animal cells. Crystal-structure determination of ectodomain regions from three type II cadherins reveals adhesive dimers formed by exchange of N-terminal beta strands between partner extracellular cadherin-1 (EC1) domains. These interfaces have two conserved tryptophan side chains that anchor each swapped strand, compared with one in type I cadherins, and include large hydrophobic regions unique to type II interfaces. The EC1 domains of type I and type II cadherins appear to encode cell adhesive specificity in vitro. Moreover, perturbation of motor neuron segregation with chimeric cadherins depends on EC1 domain identity, suggesting that this region, which includes the structurally defined adhesive interface, encodes type II cadherin functional specificity in vivo.

Amino Acid Sequence↗
PubMed2006

Searching for neuronal left/right asymmetry: genomewide analysis of nematode receptor-type guanylyl cyclases.

Functional left/right asymmetry ("laterality") is a fundamental feature of many nervous systems, but only very few molecular correlates to functional laterality are known. At least two classes of chemosensory neurons in the nematode Caenorhabditis elegans are functionally lateralized. The gustatory neurons ASE left (ASEL) and ASE right (ASER) are two bilaterally symmetric neurons that sense distinct chemosensory cues and express a distinct set of four known chemoreceptors of the guanylyl cyclase (gcy) gene family. To examine the extent of lateralization of gcy gene expression patterns in the ASE neurons, we have undertaken a genomewide analysis of all gcy genes. We report the existence of a total of 27 gcy genes encoding receptor-type guanylyl cyclases and of 7 gcy genes encoding soluble guanylyl cyclases in the complete genome sequence of C. elegans. We describe the expression pattern of all previously uncharacterized receptor-type guanylyl cyclases and find them to be highly biased but not exclusively restricted to the nervous system. We find that >41% (11/27) of all receptor-type guanylyl cyclases are expressed in the ASE gustatory neurons and that one-third of all gcy genes (9/27) are expressed in a lateral, left/right asymmetric manner in the ASE neurons. The expression of all laterally expressed gcy genes is under the control of a gene regulatory network composed of several transcription factors and miRNAs. The complement of gcy genes in the related nematode C. briggsae differs from C. elegans as evidenced by differences in chromosomal localization, number of gcy genes, and expression patterns. Differences in gcy expression patterns in the ASE neurons of C. briggsae arise from a difference in cis-regulatory elements and trans-acting factors that control ASE laterality. In sum, our results indicate the existence of a surprising multitude of putative chemoreceptors in the gustatory ASE neurons and suggest the existence of a substantial degree of laterality in gustatory signaling mechanisms in nematodes.

Alleles↗
PubMed2005

Protein structure prediction: inroads to biology.

In recent years, there has been significant progress in the ability to predict the three-dimensional structure of proteins from their amino acid sequence. Progress has been due to new methods to extract the growing amount of information in sequence and structure databases and improved computational descriptions of protein energetics. This review summarizes recent advances in these areas and describes a number of novel biological applications made possible by structure prediction. Despite remaining challenges, protein structure prediction is becoming an extremely useful tool in understanding phenomena in modern molecular and cell biology.

Algorithms↗
PubMed2005

An assessment of the accuracy of methods for predicting hydrogen positions in protein structures.

The addition of hydrogen atoms to models or experimental structures of proteins that contain only non-hydrogen atoms is a common step in crystallographic structure refinement, in theoretical studies of proteins, and in protein structure prediction. Accurate prediction of the hydrogen positions is essential, since they constitute around half of the atoms in proteins and hence contribute significantly to their energetics. Many computational tools exist for predicting hydrogen positions, although to date no quantitative comparison has been made of their accuracy or efficiency. Here we take advantage of the recent increase in ultra-high-resolution X-ray crystal structures (< 0.9 A resolution), as well as of a number of relatively high-resolution neutron diffraction structures (< 1.8 A resolution), to compare the quality of the predictions generated by a large set of commonly used methods. These include CHARMM, CNS, GROMACS, MCCE, MolProbity, WHAT IF, and X-PLOR. The hydrogen atoms that lack a rotational degree of freedom are mostly, but not always, accurately predicted. For hydrogens with a rotational degree of freedom, all the methods give much less accurate predictions. The predictions for the hydroxyl hydrogens are analyzed in detail, particularly those buried within the protein, and some explanation is provided for the errors observed. The results provide a means to make informed decisions regarding the choice and implementation of methodologies for placing hydrogens on structures of proteins. They also point to shortcomings in current force fields and suggest the need for improved descriptions of hydrogen bonding energetics.

Amines↗
PubMed2005

Lozenge directly activates argos and klumpfuss to regulate programmed cell death.

We show that reducing the activity of the Drosophila Runx protein Lozenge (Lz) during pupal development causes a decrease in cell death in the eye. We identified Lz-binding sites in introns of argos (aos) and klumpfuss (klu) and demonstrate that these genes are directly activated targets of Lz. Loss of either aos or klu reduces cell death, suggesting that Lz promotes apoptosis at least in part by regulating aos and klu. These results provide novel insights into the control of programmed cell death (PCD) by Lz during Drosophila eye development.

Animals↗
PubMed2005

Comparative study of generalized Born models: protein dynamics.

In this work, we compare the results of molecular dynamics simulations involving the application of three generalized Born (GB) models to 10 different proteins. The three GB models, the Still, HCT, and modified analytical generalized Born models, were implemented in the computationally efficient gromacs package. The performance of each model was assessed from the backbone rms deviation from the native structure, the number of native hydrogen bonds retained in the simulation, and the experimental and calculated radius of gyration. Analysis of variance (ANOVA) was used to analyze the results of the simulations. The rms deviation measure was found to be unable to distinguish the quality of the results obtained with the three different GB models, whereas the number of native hydrogen bonds and radius of gyration yielded a statistically meaningful discrimination among models. Our results suggest that, of the three, modified analytical generalized Born yields the best agreement between calculated and experimentally derived structures. More generally, our study highlights the need both to evaluate the effects of different variables on the results of simulations and to verify that the results of molecular dynamics simulations are statistically meaningful.

Analysis of Variance↗
PubMed2005

Comparative study of generalized born models: Born radii and peptide folding.

In this study, we have implemented four analytical generalized Born (GB) models and investigated their performance in conjunction with the GROMOS96 force field. The four models include that of Still and co-workers, the HCT model of Cramer, Truhlar, and co-workers, a modified form of the AGB model of Levy and co-workers, and the GBMV2 model of Brooks and co-workers. The models were coded independently and implemented in the GROMOS software package and in TINKER. They were compared in terms of their ability to reproduce the results of Poisson-Boltzmann (PB) calculations and in their performance in the ab initio peptide folding of two peptides, one that forms a beta-hairpin in solution and one that forms an alpha-helix. In agreement with previous work, the GBMV2 model is most successful in reproducing PB results while the other models tend to underestimate the effective Born radii of buried atoms. In contrast, stochastic dynamics simulations on the folding of the two peptides, the C-terminus beta-hairpin of the B1 domain of protein G and the alanine-based alpha-helical peptide 3K(I), suggest that the simpler GB models are more effective in sampling conformational space. Indeed, the Still model used in conjunction with the GROMOS96 force field is able to fold the hairpin peptide to a native-like structure without the benefit of enhanced sampling techniques. This is due in part to the properties of the united-atom GROMOS96 force field which appears to be more flexible, and hence to sample more efficiently, than force fields such as OPLSAA. Our results suggest a general strategy which involves using different combinations of force fields and solvent models in different applications, for example, using GROMOS96 and a simple GB model in sampling and OPLSAA and a more accurate GB model in refinement. The fact that various methods have been implemented in a unified way should facilitate the testing and subsequent use of different methods to evaluate conformational free energies in different applications. Our results also bear on some general issues involved in peptide folding and structure prediction which are addressed in the Discussion.

Alanine↗