@article {19716, title = {TIGRFAMs and Genome Properties in 2013.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jan}, pages = {D387-95}, abstract = {

TIGRFAMs, available online at http://www.jcvi.org/tigrfams is a database of protein family definitions. Each entry features a seed alignment of trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cutoff scores that let automated annotation pipelines decide which proteins are members, and annotations for transfer onto member proteins. Most TIGRFAMs models are designated equivalog, meaning they assign a specific name to proteins conserved in function from a common ancestral sequence. Models describing more functionally heterogeneous families are designated subfamily or domain, and assign less specific but more widely applicable annotations. The Genome Properties database, available at http://www.jcvi.org/genome-properties, specifies how computed evidence, including TIGRFAMs HMM results, should be used to judge whether an enzymatic pathway, a protein complex or another type of molecular subsystem is encoded in a genome. TIGRFAMs and Genome Properties content are developed in concert because subsystems reconstruction for large numbers of genomes guides selection of seed alignment sequences and cutoff values during protein family construction. Both databases specialize heavily in bacterial and archaeal subsystems. At present, 4284 models appear in TIGRFAMs, while 628 systems are described by Genome Properties. Content derives both from subsystem discovery work and from biocuration of the scientific literature.

}, keywords = {Databases, Protein, Genome, Archaeal, Genome, Bacterial, Genomics, Internet, Markov chains, Molecular Sequence Annotation, Proteins, sequence alignment}, issn = {1362-4962}, doi = {10.1093/nar/gks1234}, author = {Haft, Daniel H and Jeremy D Selengut and Richter, Roland A and Harkins, Derek and Basu, Malay K and Beck, Erin} } @article {19721, title = {InterPro in 2011: new developments in the family and domain prediction database.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D306-12}, abstract = {

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

}, keywords = {Databases, Protein, Protein Structure, Tertiary, Proteins, Sequence Analysis, Protein, software, Terminology as Topic, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkr948}, author = {Hunter, Sarah and Jones, Philip and Mitchell, Alex and Apweiler, Rolf and Attwood, Teresa K and Bateman, Alex and Bernard, Thomas and Binns, David and Bork, Peer and Burge, Sarah and de Castro, Edouard and Coggill, Penny and Corbett, Matthew and Das, Ujjwal and Daugherty, Louise and Duquenne, Lauranne and Finn, Robert D and Fraser, Matthew and Gough, Julian and Haft, Daniel and Hulo, Nicolas and Kahn, Daniel and Kelly, Elizabeth and Letunic, Ivica and Lonsdale, David and Lopez, Rodrigo and Madera, Martin and Maslen, John and McAnulla, Craig and McDowall, Jennifer and McMenamin, Conor and Mi, Huaiyu and Mutowo-Muellenet, Prudence and Mulder, Nicola and Natale, Darren and Orengo, Christine and Pesseat, Sebastien and Punta, Marco and Quinn, Antony F and Rivoire, Catherine and Sangrador-Vegas, Amaia and Jeremy D Selengut and Sigrist, Christian J A and Scheremetjew, Maxim and Tate, John and Thimmajanarthanan, Manjulapramila and Thomas, Paul D and Wu, Cathy H and Yeats, Corin and Yong, Siew-Yit} } @article {17487, title = {Visual Exploration across Biomedical Databases}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {8}, year = {2011}, month = {2011/04//March}, pages = {536 - 550}, abstract = {Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.}, keywords = {Bioinformatics, Biomedical computing, biomedical databases, cross-database exploration, Data exploration and discovery, Data visualization, database management systems, Databases, Factual, DNA, graph theory, Information Storage and Retrieval, information visualization., Keyword search, medical computing, natural language processing, Proteins, semantic networks, semantics, sequences, text mining, User-Computer Interface, user-defined semantics, visual databases}, isbn = {1545-5963}, doi = {10.1109/TCBB.2010.1}, author = {Lieberman,M.D. and Taheri, S. and Guo,Huimin and Mirrashed,F. and Yahav,I. and Aris,A. and Shneiderman, Ben} } @article {19725, title = {Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010}, pages = {52}, abstract = {

BACKGROUND: Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets.

RESULTS: Here we illustrate and validate the ability of the SIMBAL method to find functionally relevant short sequence signatures by application to two well-characterized protein families. In the first example, we partitioned a family of ABC permeases using a metabolic background property (urea utilization). Thus, the TRUE set for this family comprised members whose genome of origin encoded a urea utilization system. By moving a sliding window across the sequence of a permease, and searching each subsequence in turn against the full set of partitioned proteins, the method found which local sequence signatures best correlated with the urea utilization trait. Mapping of SIMBAL "hot spots" onto crystal structures of homologous permeases reveals that the significant sites are gating determinants on the cytosolic face rather than, say, docking sites for the substrate-binding protein on the extracellular face. In the second example, we partitioned a protein methyltransferase family using gene proximity as a criterion. In this case, the TRUE set comprised those methyltransferases encoded near the gene for the substrate RF-1. SIMBAL identifies sequence regions that map onto the substrate-binding interface while ignoring regions involved in the methyltransferase reaction mechanism in general. Neither method for training set construction requires any prior experimental characterization.

CONCLUSIONS: SIMBAL shows that, in functionally divergent protein families, selected short sequences often significantly outperform their full-length parent sequence for making functional predictions by sequence similarity, suggesting avenues for improved functional classifiers. When combined with structural data, SIMBAL affords the ability to localize and model functional sites.

}, keywords = {algorithms, Amino Acid Sequence, Gene Expression Profiling, Molecular Sequence Data, Phylogeny, Proteins, Sequence Analysis, Protein, Structure-Activity Relationship}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-52}, author = {Jeremy D Selengut and Rusch, Douglas B and Haft, Daniel H} } @article {18695, title = {Avid interactions underlie the Lys63-linked polyubiquitin binding specificities observed for UBA domains}, journal = {Nature Structural \& Molecular Biology}, volume = {16}, year = {2009}, month = {2009///}, pages = {883 - 889}, abstract = {Ubiquitin (denoted Ub) receptor proteins as a group must contain a diverse set of binding specificities to distinguish the many forms of polyubiquitin (polyUb) signals. Previous studies suggested that the large class of ubiquitin-associated (UBA) domains contains members with intrinsic specificity for Lys63-linked polyUb or Lys48-linked polyUb, thus explaining how UBA-containing proteins can mediate diverse signaling events. Here we show that previously observed Lys63-polyUb selectivity in UBA domains is the result of an artifact in which the dimeric fusion partner, glutathione S-transferase (GST), positions two UBAs for higher affinity, avid interactions with Lys63-polyUb, but not with Lys48-polyUb. Freed from GST, these UBAs are either nonselective or prefer Lys48-polyUb. Accordingly, NMR experiments reveal no Lys63-polyUb{\textendash}specific binding epitopes for these UBAs. We reexamine previous conclusions based on GST-UBAs and present an alternative model for how UBAs achieve a diverse range of linkage specificities.}, keywords = {apoptosis, basic cellular processes, Biochemistry, biophysics, cell biology, cell cycle, cell surface proteins, cell-cell interactions, checkpoints, chromatin, chromatin remodeling, chromatin structure, content, DNA recombination, DNA repair, DNA replication, Gene expression, Genetics, intracellular signaling, journal, macromolecules, mechanism, membrane processes, molecular, molecular basis of disease, molecular biology, molecular interactions, multi-component complexes, nature publishing group, nature structural molecular biology, nucleic acids, protein degradation, protein folding, protein processing, Proteins, regulation of transcription, regulation of translation, RNA, RNA processing, RNAi, signal transduction, single molecule studies, structure and function of proteins, transcription, translation}, isbn = {1545-9993}, doi = {10.1038/nsmb.1637}, url = {http://www.nature.com/nsmb/journal/v16/n8/abs/nsmb.1637.html}, author = {Sims,Joshua J. and Haririnia,Aydin and Dickinson,Bryan C. and Fushman, David and Cohen,Robert E.} } @conference {15269, title = {A cooperative combinatorial Particle Swarm Optimization algorithm for side-chain packing}, booktitle = {IEEE Swarm Intelligence Symposium, 2009. SIS {\textquoteright}09}, year = {2009}, month = {2009/04/30/March}, pages = {22 - 29}, publisher = {IEEE}, organization = {IEEE}, abstract = {Particle Swarm Optimization (PSO) is a well-known, competitive technique for numerical optimization with real-parameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to side-chain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three side-chain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1\% of the provably optimal solution in many proteins.}, keywords = {Algorithm design and analysis, Amino acids, combinatorial mathematics, cooperative combinatorial particle swarm optimization algorithm, Design optimization, Encoding, Feedback, numerical optimization, Optimization methods, particle swarm optimisation, Particle swarm optimization, Partitioning algorithms, Proteins, proteomics, proteomics optimization, Robustness, side-chain packing}, isbn = {978-1-4244-2762-8}, doi = {10.1109/SIS.2009.4937840}, author = {Lapizco-Encinas,G. and Kingsford, Carl and Reggia, James A.} } @article {18721, title = {Structure of the S5a:K48-Linked Diubiquitin Complex and Its Interactions with Rpn13}, journal = {Molecular Cell}, volume = {35}, year = {2009}, month = {2009/08/14/}, pages = {280 - 290}, abstract = {SummaryDegradation by the proteasome typically requires substrate ubiquitination. Two ubiquitin receptors exist in the proteasome, S5a/Rpn10 and Rpn13. Whereas Rpn13 has only one ubiquitin-binding surface, S5a binds ubiquitin with two independent ubiquitin-interacting motifs (UIMs). Here, we use nuclear magnetic resonance (NMR) and analytical ultracentrifugation to define at atomic level resolution how S5a binds K48-linked diubiquitin, in which K48 of~one ubiquitin subunit (the {\textquotedblleft}proximal{\textquotedblright} one) is covalently bonded to G76 of the other (the {\textquotedblleft}distal{\textquotedblright} subunit). We demonstrate that S5a{\textquoteright}s UIMs bind the two subunits simultaneously with a preference for UIM2 binding to the proximal subunit while UIM1 binds to the distal one. In addition, NMR experiments reveal that Rpn13 and S5a bind K48-linked diubiquitin simultaneously with subunit specificity, and a model structure of S5a and Rpn13 bound to K48-linked polyubiquitin is provided. Altogether, our data demonstrate that S5a is highly adaptive and cooperative toward binding ubiquitin chains. }, keywords = {Proteins}, isbn = {1097-2765}, doi = {10.1016/j.molcel.2009.06.010}, url = {http://www.sciencedirect.com/science/article/pii/S1097276509004018}, author = {Zhang,Naixia and Wang,Qinghua and Ehlinger,Aaron and Randles,Leah and Lary,Jeffrey W. and Kang,Yang and Haririnia,Aydin and Storaska,Andrew J. and Cole,James L. and Fushman, David and Walters,Kylie J.} } @article {18724, title = {Together, Rpn10 and Dsk2 Can Serve as a Polyubiquitin Chain-Length Sensor}, journal = {Molecular Cell}, volume = {36}, year = {2009}, month = {2009/12/24/}, pages = {1018 - 1033}, abstract = {SummaryAs a signal for substrate targeting, polyubiquitin meets various layers of receptors upstream to the 26S proteasome. We obtained structural information on two receptors, Rpn10 and Dsk2, alone and in complex with (poly)ubiquitin or with each other. A~hierarchy of affinities emerges with Dsk2 binding monoubiquitin tighter than Rpn10 does, whereas Rpn10 prefers the ubiquitin-like domain of Dsk2 to monoubiquitin, with increasing affinities for longer polyubiquitin chains. We demonstrated the formation of ternary complexes of both receptors simultaneously with (poly)ubiquitin and found that, depending on the ubiquitin chain length, the orientation of the resulting complex is entirely different, providing for alternate signals. Dynamic rearrangement provides a chain-length sensor, possibly explaining how accessibility of Dsk2 to the proteasome is limited unless it carries a properly tagged cargo. We propose a mechanism for a malleable ubiquitin signal that depends both on chain length and combination of receptors to produce tetraubiquitin as an efficient signal threshold. }, keywords = {Proteins, signaling}, isbn = {1097-2765}, doi = {10.1016/j.molcel.2009.11.012}, url = {http://www.sciencedirect.com/science/article/pii/S1097276509008260}, author = {Zhang,Daoning and Chen,Tony and Ziv,Inbal and Rosenzweig,Rina and Matiuhin,Yulia and Bronner,Vered and Glickman,Michael H. and Fushman, David} } @article {18699, title = {Diverse polyubiquitin interaction properties of ubiquitin-associated domains}, journal = {Nature Structural \& Molecular Biology}, volume = {12}, year = {2005}, month = {2005///}, pages = {708 - 714}, abstract = {The ubiquitin-associated (UBA) domain occurs frequently in proteins involved in ubiquitin-dependent signaling pathways. Although polyubiquitin chain binding is considered to be a defining feature of the UBA domain family, the generality of this property has not been established. Here we have surveyed the polyubiquitin interaction properties of 30 UBA domains, including 16 of 17 occurrences in budding yeast. The UBA domains sort into four classes that include linkage-selective polyubiquitin binders and domains that bind different chains (and monoubiquitin) in a nondiscriminatory manner; one notable class (30\%) did not bind any ubiquitin ligand surveyed. The properties of a given UBA domain are conserved from yeast to mammals. Their functional relevance is further suggested by the ability of an ectopic UBA domain to alter the specificity of a deubiquitylating enzyme in a predictable manner. Conversely, non-UBA sequences can modulate the interaction properties of a UBA domain.}, keywords = {apoptosis, basic cellular processes, Biochemistry, biophysics, cell biology, cell cycle, cell surface proteins, cell-cell interactions, checkpoints, chromatin, chromatin remodeling, chromatin structure, content, DNA recombination, DNA repair, DNA replication, Gene expression, Genetics, intracellular signaling, journal, macromolecules, mechanism, membrane processes, molecular, molecular basis of disease, molecular biology, molecular interactions, multi-component complexes, nature publishing group, nature structural molecular biology, nucleic acids, protein degradation, protein folding, protein processing, Proteins, regulation of transcription, regulation of translation, RNA, RNA processing, RNAi, signal transduction, single molecule studies, structure and function of proteins, transcription, translation}, isbn = {1545-9993}, doi = {10.1038/nsmb962}, url = {http://www.nature.com/nsmb/journal/v12/n8/full/nsmb962.html}, author = {Raasi,Shahri and Varadan,Ranjani and Fushman, David and Pickart,Cecile M.} } @article {18704, title = {Impact of Cl- and Na+ ions on simulated structure and dynamics of βARK1 PH domain}, journal = {Proteins: Structure, Function, and Bioinformatics}, volume = {35}, year = {1999}, month = {1999///}, pages = {206 - 217}, abstract = {A nonzero net charge of proteins at pH 7 is usually compensated by the addition of charge-balancing counter ions during molecular dynamics simulation, which reduces electrostatic interactions. For highly charged proteins, like the βARK1 PH domain used here, it seems reasonable to also add explicit salt ions. To assess the impact of explicit salt ions, two molecular dynamics simulations of solvated βARK1 PH domain have been carried out with different numbers of Cl- and Na+ ions, based on the Cornell et al. force field and the Ewald summation, which was used in the treatment of long-range electrostatic interactions. Initial positions of ions were obtained from the AMBER CION program. Increasing the number of ions alters the average structure in loop regions, as well as the fluctuation amplitudes of dihedral angles. We found unnaturally strong interactions between side chains in the absence of salt ions. The presence of salt ions reduces these electrostatic interactions. The time needed for the equilibration of the ionic environment around the protein, after initial placement of ions close to oppositely charged side chains, is in the nanosecond time range, which can be shortened by using a higher ionic strength. Our results also suggest selecting those methods that do not place the ions initially close to the protein surface. Proteins 1999;35:206{\textendash}217. {\textcopyright} 1999 Wiley-Liss, Inc.}, keywords = {counter ions, electrostatic interaction, equilibration, GRK2 PH domain, hydrogen bonds, ionic solvent, ionic strength, molecular dynamics simulation, Proteins}, isbn = {1097-0134}, doi = {10.1002/(SICI)1097-0134(19990501)35:2<206::AID-PROT7>3.0.CO;2-A}, url = {http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0134(19990501)35:2<206::AID-PROT7>3.0.CO;2-A/abstract}, author = {Pfeiffer,Stefania and Fushman, David and Cowburn,David} }