Barry Honig

Howard Hughes Medical Institute, Ashburn, Virginia, United States

Are you Barry Honig?

Claim your profile

Publications (298)2023.65 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Current Opinion in Structural Biology 06/2015; 32. DOI:10.1016/j.sbi.2015.01.007 · 8.75 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Type I cadherin cell-adhesion proteins are similar in sequence and structure and yet are different enough to mediate highly specific cell-cell recognition phenomena. It has previously been shown that small differences in the homophilic and heterophilic binding affinities of different type I family members can account for the differential cell-sorting behavior. Here we use a combination of X-ray crystallography, analytical ultracentrifugation, surface plasmon resonance and double electron-electron resonance (DEER) electron paramagnetic resonance spectroscopy to identify the molecular determinants of type I cadherin dimerization affinities. Small changes in sequence are found to produce subtle structural and dynamical changes that impact relative affinities, in part via electrostatic and hydrophobic interactions, and in part through entropic effects because of increased conformational heterogeneity in the bound states as revealed by DEER distance mapping in the dimers. These findings highlight the remarkable ability of evolution to exploit a wide range of molecular properties to produce closely related members of the same protein family that have affinity differences finely tuned to mediate their biological roles.
    Proceedings of the National Academy of Sciences 09/2014; 111(40). DOI:10.1073/pnas.1416737111 · 9.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In organellogenesis of the chloroplast from endosymbiotic cyanobacteria, the establishment of protein-targeting mechanisms to the chloroplast should have been pivotal. However, it is still mysterious how these mechanisms were established and how they work in plant cells. Here we show that AKR2A, the cytosolic targeting factor for chloroplast outer membrane (COM) proteins, evolved from the ankyrin repeat domain (ARD) of the host cell by stepwise extensions of its N-terminal domain and that two lipids, monogalactosyldiacylglycerol (MGDG) and phosphatidylglycerol (PG), of the endosymbiont were selected to function as the AKR2A receptor. Structural analysis, molecular modeling, and mutational analysis of the ARD identified two adjacent sites for coincidental and synergistic binding of MGDG and PG. Based on these findings, we propose that the targeting mechanism of COM proteins was established using components from both the endosymbiont and host cell through a modification of the protein-protein-interacting ARD into a lipid binding domain.
    Developmental Cell 09/2014; 30(5):598-609. DOI:10.1016/j.devcel.2014.07.026 · 10.37 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Individual mammalian neurons stochastically express distinct repertoires of α, β, and γ protocadherin (Pcdh) proteins, which function in neural circuit assembly. We report that all three subfamilies of clustered Pcdhs can engage in specific homophilic interactions, that cell surface delivery of Pcdhα isoforms requires cis interactions with other Pcdhs, and that the extracellular cadherin domain EC6 plays a critical role in this process. Examination of homophilic interactions between specific combinations of multiple Pcdh isoforms revealed that Pcdh combinatorial recognition specificities depend on the identity of all of the expressed isoforms. A single mismatched Pcdh isoform can interfere with these combinatorial homophilic interactions. A theoretical analysis reveals that assembly of Pcdh isoforms into multimeric recognition units and the observed tolerance for mismatched isoforms can generate cell surface diversity sufficient for single-cell identity. However, the competing demands of nonself discrimination and self-recognition place limitations on the mechanisms by which homophilic recognition units can function.
    Cell 08/2014; 158(5):1045-59. DOI:10.1016/j.cell.2014.07.012 · 33.12 Impact Factor
  • Donald Petrey, Barry Honig
    [Show abstract] [Hide abstract]
    ABSTRACT: The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
    Annual Review of Biophysics 05/2014; 43:193-210. DOI:10.1146/annurev-biophys-051013-022726 · 12.25 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present OnTheFly (http://bhapp.c2b2.columbia.edu/OnTheFly/index.php), a database comprising a systematic collection of transcription factors (TFs) of Drosophila melanogaster and their DNA-binding sites. TFs predicted in the Drosophila melanogaster genome are annotated and classified and their structures, obtained via experiment or homology models, are provided. All known preferred TF DNA-binding sites obtained from the B1H, DNase I and SELEX methodologies are presented. DNA shape parameters predicted for these sites are obtained from a high throughput server or from crystal structures of protein-DNA complexes where available. An important feature of the database is that all DNA-binding domains and their binding sites are fully annotated in a eukaryote using structural criteria and evolutionary homology. OnTheFly thus provides a comprehensive view of TFs and their binding sites that will be a valuable resource for deciphering non-coding regulatory DNA.
    Nucleic Acids Research 11/2013; 42(D1). DOI:10.1093/nar/gkt1165 · 8.81 Impact Factor
  • Source
  • Source
  • Source
  • Source
  • Source
  • [Show abstract] [Hide abstract]
    ABSTRACT: Epithelial cadherin (E-cadherin), a member of the classical cadherin family, mediates calcium-dependent homophilic cell-cell adhesion. Crystal structures of classical cadherins reveal an adhesive dimer interface featuring reciprocal exchange of N-terminal β-strands between two protomers. Previous work has identified a putative intermediate (called the "X-dimer") in the dimerization pathway of wild-type E-cadherin EC1-EC2 domains, based on crystal structures of mutants not capable of strand swapping and on deceleration of binding kinetics by mutations at the X-dimer interface. In the present work, NMR relaxation dispersion spectroscopy is used to directly observe and characterize intermediate states without the need to disrupt the strand-swapped binding interface by mutagenesis. The results indicate that E-cadherin forms strand-swapped dimers predominantly by a mechanism in which formation of a weak and short-lived X-dimer-like state precedes the conformational changes required for formation of the mature strand-swapped dimeric structure. Disruption of this intermediate state through mutation reduces both association and dissociation rates by factors of ∼10(4), while minimally perturbing affinity. The X-dimer interface lowers the energy barrier associated with strand swapping and enables E-cadherins to form strand-swapped dimers at a rate consistent with residence times in adherens junctions.
    Proceedings of the National Academy of Sciences 09/2013; 110(41):16462-7. DOI:10.1073/pnas.1314303110 · 9.81 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The equilibrium constants of trans and cis dimerization of membrane bound (2D) and freely moving (3D) adhesion receptors are expressed and compared using elementary statistical-thermodynamics. Both processes are mediated by the binding of extracellular subdomains whose range of motion in the 2D environment is reduced upon dimerization, defining a thin reaction shell where dimer formation and dissociation take place. We show that the ratio between the 2D and 3D equilibrium constants can be expressed as a product of individual factors describing, respectively, the spatial ranges of motions of the adhesive domains, and their rotational freedom within the reaction shell. The results predicted by the theory are compared to those obtained from a novel, to our knowledge, dynamical simulations methodology, whereby pairs of receptors perform realistic translational, internal, and rotational motions in 2D and 3D. We use cadherins as our model system. The theory and simulations explain how the strength of cis and trans interactions of adhesive receptors are affected both by their presence in the constrained intermembrane space and by the 2D environment of membrane surfaces. Our work provides fundamental insights as to the mechanism of lateral clustering of adhesion receptors after cell-cell contact and, more generally, to the formation of lateral microclusters of proteins on cell surfaces.
    Biophysical Journal 03/2013; 104(6):1221-9. DOI:10.1016/j.bpj.2013.02.009 · 3.83 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a "structural BLAST" approach to infer function with high genomic coverage. Applications are described to the prediction of protein-protein and protein-ligand interactions. In the context of protein-protein interactions, our structure-based prediction algorithm, PrePPI, has comparable accuracy to high-throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure-derived information with non-structural evidence (e.g., co-expression) to assign a likelihoodfor each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function,including systems level biological applications where it has previously played little role.
    Protein Science 01/2013; DOI:10.1002/pro.2225 · 2.86 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. To date, structural information has had only limited impact on genome-scale efforts to predict protein–protein interactions (PPIs). A new algorithm, PrePPI, will be introduced that combines structural information with nonstructural clues and that is comparable in accuracy to high-throughput experiments. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models and the exploitation of both close and remote geometric relationships between proteins. More generally, the “structural BLAST” approach encapsulated in PrePPI significantly expands the range of application of protein structure in the annotation of protein function.
    Journal of biomolecular Structure & Dynamics 01/2013; 31. DOI:10.1080/07391102.2013.786422 · 2.98 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We describe a systematic determination of Drosophila melanogaster transcription factor DNA-binding specificities. We annotated and classified all transcription factors (TFs) predicted in the Drosophila melanogaster genome (Pfreundt, U. et al. 2009) and collected the known preferred DNA binding sites of the TFs based on the B1H (Zhu, L.J. et al. 2011), DNaseI (Bergman C.M. et al. 2005), and SELEX (Slattery, M. et al. 2011) methods. Then, we identified the sequence and shape preferences for all DNA-binding proteins (Kuziemko A et al. 2011) and also characterized the shapes of their preferred DNA binding sites using structural models. The identification of the preferred DNA binding sites, and their shapes, for all DNA binding proteins will provide an unprecedented and extremely valuable database for anyone attempting to decipher noncoding regulatory DNA. Specifically, we showed that using structural criteria such as the width of the minor groove (Rohs R. et al. 2010), we distinguished DNA sequences bound by proteins which possess a Homeodomain from other proteins that possess a ZNF-C2H2 domain or ETS domains (pictured bellow from right to left). Furthermore, based on key TF–DNA interactions from the template structure stored in PDB, we superpose query DNA structure onto the template. Therefore, we obtain a homology model, where TF is from the template and DNA is from the query. This model will provide potential structural basis to study TF’s DNA-binding specificity. The insights from such a study could help in selecting the best DNA candidates to be bound by a certain TF for experimental testing. Finding such pairs will help to characterize the unique properties of protein–DNA interfaces and identify new drug target sites.
    Journal of biomolecular Structure & Dynamics 01/2013; 31. DOI:10.1080/07391102.2013.786509 · 2.98 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Protein-DNA binding is mediated by the recognition of the chemical signatures of the DNA bases and the 3D shape of the DNA molecule. Because DNA shape is a consequence of sequence, it is difficult to dissociate these modes of recognition. Here, we tease them apart in the context of Hox-DNA binding by mutating residues that, in a co-crystal structure, only recognize DNA shape. Complexes made with these mutants lose the preference to bind sequences with specific DNA shape features. Introducing shape-recognizing residues from one Hox protein to another swapped binding specificities in vitro and gene regulation in vivo. Statistical machine learning revealed that the accuracy of binding specificity predictions improves by adding shape features to a model that only depends on sequence, and feature selection identified shape features important for recognition. Thus, shape readout is a direct and independent component of binding site selection by Hox proteins. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of biomolecular Structure & Dynamics 01/2013; 31. DOI:10.1080/07391102.2013.786502 · 2.98 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein-protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability > 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.
    Nucleic Acids Research 11/2012; 41(D1). DOI:10.1093/nar/gks1231 · 8.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification, as well as from manual curation of experiments on individual systems. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein-protein interactions (PPIs). Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.
    Nature 09/2012; 490(7421):556-60. DOI:10.1038/nature11503 · 42.35 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Nectins are immunoglobulin superfamily glycoproteins that mediate intercellular adhesion in many vertebrate tissues. Homophilic and heterophilic interactions between nectin family members help mediate tissue patterning. We determined the homophilic binding affinities and heterophilic specificities of all four nectins and the related protein nectin-like 5 (Necl-5) from human and mouse, revealing a range of homophilic interaction strengths and a defined heterophilic specificity pattern. To understand the molecular basis of their adhesion and specificity, we determined the crystal structures of natively glycosylated full ectodomains or adhesive fragments of all four nectins and Necl-5. All of the crystal structures revealed dimeric nectins bound through a stereotyped interface that was previously proposed to represent a cis dimer. However, conservation of this interface and the results of targeted cross-linking experiments showed that this dimer probably represents the adhesive trans interaction. The structure of the dimer provides a simple molecular explanation for the adhesive binding specificity of nectins.
    Nature Structural & Molecular Biology 08/2012; 19(9):906-15. DOI:10.1038/nsmb.2366 · 11.63 Impact Factor

Publication Stats

33k Citations
2,023.65 Total Impact Points

Institutions

  • 2001–2015
    • Howard Hughes Medical Institute
      Ashburn, Virginia, United States
  • 1979–2014
    • Columbia University
      • • Department of Biochemistry and Molecular Biophysics
      • • Center for Computational Biology and Bioinformatics
      • • Department of Chemistry
      • • Department of Biological Sciences
      New York, New York, United States
  • 1980–2008
    • University of Illinois, Urbana-Champaign
      Urbana, Illinois, United States
  • 2006
    • CUNY Graduate Center
      New York City, New York, United States
  • 2002–2005
    • Weill Cornell Medical College
      • Department of Microbiology and Immunology
      New York, New York, United States
    • William Penn University
      Filadelfia, Pennsylvania, United States
    • University of Texas at Austin
      • Department of Chemistry and Biochemistry
      Austin, Texas, United States
  • 1975–2002
    • Hebrew University of Jerusalem
      • Department of Physical Chemistry
      Yerushalayim, Jerusalem, Israel
  • 2000
    • College of William and Mary
      Williamsburg, Virginia, United States
  • 1997
    • Brandeis University
      • Department of Biological Physics
      Волтам, Massachusetts, United States
  • 1995
    • University of Pennsylvania
      • Department of Biochemistry and Biophysics
      Filadelfia, Pennsylvania, United States
  • 1994
    • Yale University
      • Department of Molecular Biophysics and Biochemistry
      New Haven, CT, United States
    • Whitehead Institute for Biomedical Research
      • Department of Biology
      Cambridge, Massachusetts, United States
  • 1988
    • University of California, Irvine
      • Department of Physiology & Biophysics
      Irvine, CA, United States
  • 1982
    • Urbana University
      Urbana, Illinois, United States
  • 1977
    • Weizmann Institute of Science
      • Department of Structural Biology
      Israel