Babbitt PC

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Full name : Babbitt Patricia C

First name : Patricia C

Mail : Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biomedical Research (QB3), University of California San Francisco, San Francisco, California

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Country : USA

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References (6)

Title : Biocuration in the structure-function linkage database: the anatomy of a superfamily - Holliday_2017_Database.(Oxford)_2017_
Author(s) : Holliday GL , Brown SD , Akiva E , Mischel D , Hicks MA , Morris JH , Huang CC , Meng EC , Pegg SC , Ferrin TE , Babbitt PC
Ref : Database (Oxford) , 2017 : , 2017
Abstract : With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD. Database URL: http://sfld.rbvi.ucsf.edu/.
ESTHER : Holliday_2017_Database.(Oxford)_2017_
PubMedSearch : Holliday_2017_Database.(Oxford)_2017_
PubMedID: 28365730

Title : Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat - Babbitt_2015_Database.(Oxford)_2015_bav063
Author(s) : Babbitt PC , Bagos PG , Bairoch A , Bateman A , Chatonnet A , Chen MJ , Craik DJ , Finn RD , Gloriam D , Haft DH , Henrissat B , Holliday GL , Isberg V , Kaas Q , Landsman D , Lenfant N , Manning G , Nagano N , Srinivasan N , O'Donovan C , Pruitt KD , Sowdhamini R , Rawlings ND , Saier MH, Jr. , Sharman JL , Spedding M , Tsirigos KD , Vastermark A , Vriend G
Ref : Database (Oxford) , 2015 :bav063 , 2015
Abstract : During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.
ESTHER : Babbitt_2015_Database.(Oxford)_2015_bav063
PubMedSearch : Babbitt_2015_Database.(Oxford)_2015_bav063
PubMedID: 26284514

Title : Key challenges for the creation and maintenance of specialist protein resources - Holliday_2015_Proteins_83_1005
Author(s) : Holliday GL , Bairoch A , Bagos PG , Chatonnet A , Craik DJ , Finn RD , Henrissat B , Landsman D , Manning G , Nagano N , O'Donovan C , Pruitt KD , Rawlings ND , Saier MH, Jr. , Sowdhamini R , Spedding M , Srinivasan N , Vriend G , Babbitt PC , Bateman A
Ref : Proteins , 83 :1005 , 2015
Abstract : As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value. Proteins 2015; 83:1005-1013. (c) 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
ESTHER : Holliday_2015_Proteins_83_1005
PubMedSearch : Holliday_2015_Proteins_83_1005
PubMedID: 25820941

Title : Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases - London_2015_Biochemistry_54_528
Author(s) : London N , Farelli JD , Brown SD , Liu C , Huang H , Korczynska M , Al-Obaidi NF , Babbitt PC , Almo SC , Allen KN , Shoichet BK
Ref : Biochemistry , 54 :528 , 2015
Abstract : Enzyme function prediction remains an important open problem. Though structure-based modeling, such as metabolite docking, can identify substrates of some enzymes, it is ill-suited to reactions that progress through a covalent intermediate. Here we investigated the ability of covalent docking to identify substrates that pass through such a covalent intermediate, focusing particularly on the haloalkanoate dehalogenase superfamily. In retrospective assessments, covalent docking recapitulated substrate binding modes of known cocrystal structures and identified experimental substrates from a set of putative phosphorylated metabolites. In comparison, noncovalent docking of high-energy intermediates yielded nonproductive poses. In prospective predictions against seven enzymes, a substrate was identified for five. For one of those cases, a covalent docking prediction, confirmed by empirical screening, and combined with genomic context analysis, suggested the identity of the enzyme that catalyzes the orphan phosphatase reaction in the riboflavin biosynthetic pathway of Bacteroides.
ESTHER : London_2015_Biochemistry_54_528
PubMedSearch : London_2015_Biochemistry_54_528
PubMedID: 25513739

Title : Biases in the experimental annotations of protein function and their effect on our understanding of protein function space - Schnoes_2013_PLoS.Comput.Biol_9_e1003063
Author(s) : Schnoes AM , Ream DC , Thorman AW , Babbitt PC , Friedberg I
Ref : PLoS Comput Biol , 9 :e1003063 , 2013
Abstract : The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the "few articles - many proteins" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.
ESTHER : Schnoes_2013_PLoS.Comput.Biol_9_e1003063
PubMedSearch : Schnoes_2013_PLoS.Comput.Biol_9_e1003063
PubMedID: 23737737

Title : Automated discovery of 3D motifs for protein function annotation - Polacco_2006_Bioinformatics_22_723
Author(s) : Polacco BJ , Babbitt PC
Ref : Bioinformatics , 22 :723 , 2006
Abstract : MOTIVATION: Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3-10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function.
RESULTS: We apply our method, GASPS, to the haloacid dehalogenase, enolase, amidohydrolase and crotonase superfamilies and to the serine proteases. The motifs found by GASPS are as good at function prediction as 3D motifs based on expert knowledge. The GASPS motifs with the greatest ability to predict protein function consist mainly of known functional residues. However, several residues with no known functional role are equally predictive. For four groups, we show that the predictive power of our 3D motifs is comparable with or better than approaches that use the entire fold (Combinatorial-Extension) or sequence profiles (PSI-BLAST). AVAILABILITY: Source code is freely available for academic use by contacting the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
ESTHER : Polacco_2006_Bioinformatics_22_723
PubMedSearch : Polacco_2006_Bioinformatics_22_723
PubMedID: 16410325