Ten simple rules for selecting a postdoctoral position.
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Algorithms↗
Biomedical subjects
Philip E Bourne
Publications and source records attributed to Philip E Bourne.
Conserved motifs in voltage-sensing and pore-forming modules of voltage-gated ion channel proteins.
Voltage-gated ion channels (VGCs) mediate selective diffusion of ions across cell membranes to enable many vital cellular processes. Three-dimensional structure data are lacking for VGC proteins; hence, to better understand their function, there is a need to identify the conserved motifs using sequence analysis methods. In this study, we have used a profile-to-profile alignment method to identify several new conserved motifs specific to each transmembrane segment (TMS) of the voltage-sensing and the pore-forming modules of Ca2+, Na+, and K+ channel subfamilies. For Ca2+ and Na+, the functional theme of motif conservation is similar in all segments while they differ with those of the K+ channel proteins. Nevertheless, the conservation is strikingly similar in the S4 segment of the voltage-sensing module across all subfamilies. In each subfamily and for each TMS, we have identified conserved motifs/residues and correlated their functional significance and disease associations in human, using mutational data from the literature.
Amino Acid Motifs↗
Modern proteomes contain putative imprints of ancient shifts in trace metal geochemistry.
Because of the rise in atmospheric oxygen 2.3 billion years ago (Gya) and the subsequent changes in oceanic redox state over the last 2.3-1 Gya, trace metal bioavailability in marine environments has changed dramatically. Although theorized to have influenced the biological usage of metals leaving discernable genomic signals, a thorough and quantitative test of this hypothesis has been lacking. Using structural bioinformatics and whole-genome sequences, the Fe-, Zn-, Mn-, and Co-binding metallomes of 23 Archaea, 233 Bacteria, and 57 Eukarya were constructed. These metallomes reveal that the overall abundances of these metal-binding structures scale to proteome size as power laws with a unique set of slopes for each Superkingdom of Life. The differences in the power describing the abundances of Fe-, Mn-, Zn-, and Co-binding proteins in the proteomes of Prokaryotes and Eukaryotes are similar to the theorized changes in the abundances of these metals after the oxygenation of oceanic deep waters. This phenomenon suggests that Prokarya and Eukarya evolved in anoxic and oxic environments, respectively, a hypothesis further supported by structures and functions of Fe-binding proteins in each Superkingdom. Also observed is a proliferation in the diversity of Zn-binding protein structures involved in protein-DNA and protein-protein interactions within Eukarya, an event unlikely to occur in either an anoxic or euxinic environment where Zn concentrations would be vanishingly low. We hypothesize that these conserved trends are proteomic imprints of changes in trace metal bioavailability in the ancient ocean that highlight a major evolutionary shift in biological trace metal usage.
Animals↗
Ten simple rules for reviewers.
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Authorship↗
Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.
BACKGROUND: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear. RESULTS: We explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models. CONCLUSION: When attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.
Algorithms↗
Curation of complex, context-dependent immunological data.
BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.
Allergy and Immunology↗
Partitioning protein structures into domains: why is it so difficult?
This analysis takes an in-depth look into the difficulties encountered by automatic methods for domain decomposition from three-dimensional structure. The analysis involves a multi-faceted set of criteria including the integrity of secondary structure elements, the tendency toward fragmentation of domains, domain boundary consistency and topology. The strength of the analysis comes from the use of a new comprehensive benchmark dataset, which is based on consensus among experts (CATH, SCOP and AUTHORS of the 3D structures) and covers 30 distinct architectures and 211 distinct topologies as defined by CATH. Furthermore, over 66% of the structures are multi-domain proteins; each domain combination occurring once per dataset. The performance of four automatic domain assignment methods, DomainParser, NCBI, PDP and PUU, is carefully analyzed using this broad spectrum of topology combinations and knowledge of rules and assumptions built into each algorithm. We conclude that it is practically impossible for an automatic method to achieve the level of performance of human experts. However, we propose specific improvements to automatic methods as well as broadening the concept of a structural domain. Such work is prerequisite for establishing improved approaches to domain recognition. (The benchmark dataset is available from http://pdomains.sdsc.edu).
Computational Biology↗
Wiggle-predicting functionally flexible regions from primary sequence.
The Wiggle series are support vector machine-based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity.
Algorithms↗
Multipolar representation of protein structure.
BACKGROUND: That the structure determines the function of proteins is a central paradigm in biology. However, protein functions are more directly related to cooperative effects at the residue and multi-residue scales. As such, current representations based on atomic coordinates can be considered inadequate. Bridging the gap between atomic-level structure and overall protein-level functionality requires parameterizations of the protein structure (and other physicochemical properties) in a quasi-continuous range, from a simple collection of unrelated amino acids coordinates to the highly synergistic organization of the whole protein entity, from a microscopic view in which each atom is completely resolved to a "macroscopic" description such as the one encoded in the three-dimensional protein shape. RESULTS: Here we propose such a parameterization and study its relationship to the standard Euclidian description based on amino acid representative coordinates. The representation uses multipoles associated with residue Calpha coordinates as shape descriptors. We demonstrate that the multipoles can be used for the quantitative description of the protein shape and for the comparison of protein structures at various levels of detail. Specifically, we construct a (dis)similarity measure in multipolar configuration space, and show how such a function can be used for the comparison of a pair of proteins. We then test the parameterization on a benchmark set of the protein kinase-like superfamily. We prove that, when the biologically relevant portions of the proteins are retained, it can robustly discriminate between the various families in the set in a way not possible through sequence or conventional structural representations alone. We then compare our representation with the Cartesian coordinate description and show that, as expected, the correlation with that representation increases as the level of detail, measured by the highest rank of multipoles used in the representation, approaches the dimensionality of the fold space. CONCLUSION: The results described here demonstrate how a granular description of the protein structure can be achieved using multipolar coefficients. The description has the additional advantage of being immediately generalizable for any residue-specific property therefore providing a unitary framework for the study and comparison of the spatial profile of various protein properties.
Algorithms↗
Exploiting sequence and structure homologs to identify protein-protein binding sites.
A rapid increase in the number of experimentally derived three-dimensional structures provides an opportunity to better understand and subsequently predict protein-protein interactions. In this study, structurally conserved residues were derived from multiple structure alignments of the individual components of known complexes and the assigned conservation score was weighted based on the crystallographic B factor to account for the structural flexibility that will result in a poor alignment. Sequence profile and accessible surface area information was then combined with the conservation score to predict protein-protein binding sites using a Support Vector Machine (SVM). The incorporation of the conservation score significantly improved the performance of the SVM. About 52% of the binding sites were precisely predicted (greater than 70% of the residues in the site were identified); 77% of the binding sites were correctly predicted (greater than 50% of the residues in the site were identified), and 21% of the binding sites were partially covered by the predicted residues (some residues were identified). The results support the hypothesis that in many cases protein interfaces require some residues to provide rigidity to minimize the entropic cost upon complex formation.
Adrenodoxin↗
The RCSB PDB information portal for structural genomics.
The RCSB Protein Data Bank (PDB) offers online tools, summary reports and target information related to the worldwide structural genomics initiatives from its portal at http://sg.pdb.org. There are currently three components to this site: Structural Genomics Initiatives contains information and links on each structural genomics site, including progress reports, target lists, target status, targets in the PDB and level of sequence redundancy; Targets provides combined target information, protocols and other data associated with protein structure determination; and Structures offers an assessment of the progress of structural genomics based on the functional coverage of the human genome by PDB structures, structural genomics targets and homology models. Functional coverage can be examined according to enzyme classification, gene ontology (biological process, cell component and molecular function) and disease.
Databases, Protein↗
Outcome of a workshop on archiving structural models of biological macromolecules.
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Genomics↗
Ten simple rules for getting grants.
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Financing, Government↗
Structural evolution of the protein kinase-like superfamily.
The protein kinase family is large and important, but it is only one family in a larger superfamily of homologous kinases that phosphorylate a variety of substrates and play important roles in all three superkingdoms of life. We used a carefully constructed structural alignment of selected kinases as the basis for a study of the structural evolution of the protein kinase-like superfamily. The comparison of structures revealed a "universal core" domain consisting only of regions required for ATP binding and the phosphotransfer reaction. Remarkably, even within the universal core some kinase structures display notable changes, while still retaining essential activity. Hence, the protein kinase-like superfamily has undergone substantial structural and sequence revision over long evolutionary timescales. We constructed a phylogenetic tree for the superfamily using a novel approach that allowed for the combination of sequence and structure information into a unified quantitative analysis. When considered against the backdrop of species distribution and other metrics, our tree provides a compelling scenario for the development of the various kinase families from a shared common ancestor. We propose that most of the so-called "atypical kinases" are not intermittently derived from protein kinases, but rather diverged early in evolution to form a distinct phyletic group. Within the atypical kinases, the aminoglycoside and choline kinase families appear to share the closest relationship. These two families in turn appear to be the most closely related to the protein kinase family. In addition, our analysis suggests that the actin-fragmin kinase, an atypical protein kinase, is more closely related to the phosphoinositide-3 kinase family than to the protein kinase family. The two most divergent families, alpha-kinases and phosphatidylinositol phosphate kinases (PIPKs), appear to have distinct evolutionary histories. While the PIPKs probably have an evolutionary relationship with the rest of the kinase superfamily, the relationship appears to be very distant (and perhaps indirect). Conversely, the alpha-kinases appear to be an exception to the scenario of early divergence for the atypical kinases: they apparently arose relatively recently in eukaryotes. We present possible scenarios for the derivation of the alpha-kinases from an extant kinase fold.
Adenosine Triphosphate↗
An ontology for immune epitopes: application to the design of a broad scope database of immune reactivities.
BACKGROUND: Epitopes can be defined as the molecular structures bound by specific receptors, which are recognized during immune responses. The Immune Epitope Database and Analysis Resource (IEDB) project will catalog and organize information regarding antibody and T cell epitopes from infectious pathogens, experimental antigens and self-antigens, with a priority on NIAID Category A-C pathogens (http://www2.niaid.nih.gov/Biodefense/bandc_priority.htm) and emerging/re-emerging infectious diseases. Both intrinsic structural and phylogenetic features, as well as information relating to the interactions of the epitopes with the host's immune system will be catalogued. DESCRIPTION: To effectively represent and communicate the information related to immune epitopes, a formal ontology was developed. The semantics of the epitope domain and related concepts were captured as a hierarchy of classes, which represent the general and specialized relationships between the various concepts. A complete listing of classes and their properties can be found at http://www.immuneepitope.org/ontology/index.html. CONCLUSION: The IEDB's ontology is the first ontology specifically designed to capture both intrinsic chemical and biochemical information relating to immune epitopes with information relating to the interaction of these structures with molecules derived from the host immune system. We anticipate that the development of this type of ontology and associated databases will facilitate rigorous description of data related to immune epitopes, and might ultimately lead to completely new methods for describing and modeling immune responses.
Journal Article↗
Functional coverage of the human genome by existing structures, structural genomics targets, and homology models.
The bias in protein structure and function space resulting from experimental limitations and targeting of particular functional classes of proteins by structural biologists has long been recognized, but never continuously quantified. Using the Enzyme Commission and the Gene Ontology classifications as a reference frame, and integrating structure data from the Protein Data Bank (PDB), target sequences from the structural genomics projects, structure homology derived from the SUPERFAMILY database, and genome annotations from Ensembl and NCBI, we provide a quantified view, both at the domain and whole-protein levels, of the current and projected coverage of protein structure and function space relative to the human genome. Protein structures currently provide at least one domain that covers 37% of the functional classes identified in the genome; whole structure coverage exists for 25% of the genome. If all the structural genomics targets were solved (twice the current number of structures in the PDB), it is estimated that structures of one domain would cover 69% of the functional classes identified and complete structure coverage would be 44%. Homology models from existing experimental structures extend the 37% coverage to 56% of the genome as single domains and 25% to 31% for complete structures. Coverage from homology models is not evenly distributed by protein family, reflecting differing degrees of sequence and structure divergence within families. While these data provide coverage, conversely, they also systematically highlight functional classes of proteins for which structures should be determined. Current key functional families without structure representation are highlighted here; updated information on the "most wanted list" that should be solved is available on a weekly basis from http://function.rcsb.org:8080/pdb/function_distribution/index.html.
Databases, Protein↗
Assigning new GO annotations to protein data bank sequences by combining structure and sequence homology.
Accompanying the discovery of an increasing number of proteins, there is the need to provide functional annotation that is both highly accurate and consistent. The Gene Ontology (GO) provides consistent annotation in a computer readable and usable form; hence, GO annotation (GOA) has been assigned to a large number of protein sequences based on direct experimental evidence and through inference determined by sequence homology. Here we show that this annotation can be extended and corrected for cases where protein structures are available. Specifically, using the Combinatorial Extension (CE) algorithm for structure comparison, we extend the protein annotation currently provided by GOA at the European Bioinformatics Institute (EBI) to further describe the contents of the Protein Data Bank (PDB). Specific cases of biologically interesting annotations derived by this method are given. Given that the relationship between sequence, structure, and function is complicated, we explore the impact of this relationship on assigning GOA. The effect of superfolds (folds with many functions) is considered and, by comparison to the Structural Classification of Proteins (SCOP), the individual effects of family, superfamily, and fold.
Algorithms↗