CORE--Shared Resources Informatics And Data Management
Adam Godzik, Associate Professor
Burnham Institute For Medical Research La Jolla, Ca 92037
Grant 5P30CA030199-279019 from National Cancer Institute IRG: NCI
Abstract: High-throughput technologies such as large scale sequencing, DNA expression arrays, and high throughput proteomics have explosively increased the amount of data and the need for data management and analysis. The Burnham Institute Cancer Center requires effective exchange of information and coordination of research due to its strong collaborative culture and a large number of multi-investigator projects. The Informatics and Data Management Facility provides resources needed to integrate informatics and data management for the Institute in general, for the Cancer Drug Discovery Initiative in particular, and specifically for the five facilities that depend heavily on informatics (Gene Analysis, Proteomics, Chemical Library Screening, High Throughput Cell Analysis Facilities, and the Informatics and Data Management Facility itself). The Informatics and Data Management Facility will provide the following services 1) Data storage, archiving, and retrieval, 2) Data analysis, 3) Data annotation, publishing, and mining, 4) Biostatistics, 5) Molecular modeling, structure prediction, and custom databases, 6) Installation and maintenance of applications, and 7) Training. Emerging concepts of database confederation - database wrapping or meta-databases - have immediate applications in this environment. Dedicated databases, some publicly available on the Internet, are the answer to managing, accessing and integrating data, and publishing that data in a way that conforms to standards and is readily usable. We propose creating an Institute-wide data management layer that would coordinate various data-intensive projects at the Institute via a common interface, adding elements of database confederation with various publicly available resources. The central concept in this proposed structure would be a ?Molecule page?, i.e., a database record containing links to all of the available information (from gene to function) to Institute researchers. At least two such systems, Molecular Pages at the Alliance for Cell Signaling and central annotation database for the Joint Center for Structural Genomics, have been developed at the nearby San Diego Supercomputer Center, with direct integration and support from the Burnham faculty members.
Keywords: biomedical facility, computer center, computer data analysis, data management, cancer information system, computer program /software, neoplasm /cancer, oncology, high throughput technology
Sponsored Links Excellgen http://Excellgen.com
CORE--Shared Resources Informatics & Data Management
Adam Godzik, Associate Professor
Burnham Institute For Medical Research
la Jolla, Ca 92037
Grant 5P30CA030199-289019 from National Cancer Institute IRG: NCI
Abstract: High-throughput technologies such as large scale sequencing, DNA expression arrays, and high throughput proteomics have explosively increased the amount of data and the need for data management and analysis. The Burnham Institute Cancer Center requires effective exchange of information and coordination of research due to its strong collaborative culture and a large number of multi-investigator projects. The Informatics and Data Management Facility provides resources needed to integrate informatics and data management for the Institute in general, for the Cancer Drug Discovery Initiative in particular, and specifically for the five facilities that depend heavily on informatics (Gene Analysis, Proteomics, Chemical Library Screening, High Throughput Cell Analysis Facilities, and the Informatics and Data Management Facility itself). The Informatics and Data Management Facility will provide the following services 1) Data storage, archiving, and retrieval, 2) Data analysis, 3) Data annotation, publishing, and mining, 4) Biostatistics, 5) Molecular modeling, structure prediction, and custom databases, 6) Installation and maintenance of applications, and 7) Training. Emerging concepts of database confederation - database wrapping or meta-databases - have immediate applications in this environment. Dedicated databases, some publicly available on the Internet, are the answer to managing, accessing and integrating data, and publishing that data in a way that conforms to standards and is readily usable. We propose creating an Institute-wide data management layer that would coordinate various data-intensive projects at the Institute via a common interface, adding elements of database confederation with various publicly available resources. The central concept in this proposed structure would be a ?Molecule page?, i.e., a database record containing links to all of the available information (from gene to function) to Institute researchers. At least two such systems, Molecular Pages at the Alliance for Cell Signaling and central annotation database for the Joint Center for Structural Genomics, have been developed at the nearby San Diego Supercomputer Center, with direct integration and support from the Burnham faculty members
Keywords: biomedical facility, computer center, computer data analysis, data management cancer information system, computer program /software, neoplasm /cancer, oncology high throughput technology
CORE--Shared Resources Informatics And Data Management
Adam Godzik, Associate Professor
Burnham Institute For Medical Research La Jolla, Ca 92037
Grant 5P30CA030199-269019 from National Cancer Institute IRG: NCI
Abstract: High-throughput technologies such as large scale sequencing, DNA expression arrays, and high throughput proteomics have explosively increased the amount of data and the need for data management and analysis. The Burnham Institute Cancer Center requires effective exchange of information and coordination of research due to its strong collaborative culture and a large number of multi-investigator projects. The Informatics and Data Management Facility provides resources needed to integrate informatics and data management for the Institute in general, for the Cancer Drug Discovery Initiative in particular, and specifically for the five facilities that depend heavily on informatics (Gene Analysis, Proteomics, Chemical Library Screening, High Throughput Cell Analysis Facilities, and the Informatics and Data Management Facility itself). The Informatics and Data Management Facility will provide the following services 1) Data storage, archiving, and retrieval, 2) Data analysis, 3) Data annotation, publishing, and mining, 4) Biostatistics, 5) Molecular modeling, structure prediction, and custom databases, 6) Installation and maintenance of applications, and 7) Training. Emerging concepts of database confederation - database wrapping or meta-databases - have immediate applications in this environment. Dedicated databases, some publicly available on the Internet, are the answer to managing, accessing and integrating data, and publishing that data in a way that conforms to standards and is readily usable. We propose creating an Institute-wide data management layer that would coordinate various data-intensive projects at the Institute via a common interface, adding elements of database confederation with various publicly available resources. The central concept in this proposed structure would be a ?Molecule page?, i.e., a database record containing links to all of the available information (from gene to function) to Institute researchers. At least two such systems, Molecular Pages at the Alliance for Cell Signaling and central annotation database for the Joint Center for Structural Genomics, have been developed at the nearby San Diego Supercomputer Center, with direct integration and support from the Burnham faculty members.
Keywords: biomedical facility, computer center, computer data analysis, data management, cancer information system, computer program /software, neoplasm /cancer, oncology, high throughput technology
Grants awarded to Adam Godzik
Improved Algorithms For Comparative Modeling
Adam Godzik, Associate Professor
University Of California San Diego 9500 Gilman Dr, Dept 0934 La Jolla, Ca 920930934
Grant 1P01GM063208-01A10004 from National Institute Of General Medical Sciences IRG: ZRG1
Abstract: The goal of the research described in this proposal is to develop a new generation of algorithms for comparative modeling, with a specific goal to improve the quality of models based on very distant homologies. This is a necessary step to provide a link between protein sequences coming from genome projects and understanding functions of these proteins, which can only be provided by the detailed knowledge of their three- dimensional structure. Enzymatic reactions, recognition of substrates, interactions between proteins-they all happen on the molecular level and whether we want to just understand or to modify, inhibit or enhance them, we need to look at and understand biological systems on the level of their molecular three dimensional structure. Unfortunately, none of the currently available algorithms is able to make the models more similar to the actual structures that they are to the templates they are built from. Actually in most cases the modeling processes partly destroys the similarity. The research described here aims to change it by combining two approaches Development of empirical rules of how protein structures change in response to change in sequence and applying them to modify the template structure. Development of new tools for evaluation of three dimensional models of proteins In short, the plan is to use he first approach to generate a number of possible variants of the structure of the protein being modeled, while using the second approach to choose the best possible one. These overall goals will be accomplished by systematic analysis of changes in structures in families of homologous proteins to develop empirical rules of how protein structures changes in response to changes in sequence. The database of structures of homologous proteins at various levels of sequence divergence will be built and each structure will be decomposed into a hierarchy of subsystems built from smaller elements. This will allow seeking simple rules describing changes in structure, such as identification of "pivoting moves." With a set of rules like that it will be possible to modify the structure of a modeling template to make it more similar to the final structure of the modeling target. Many possibilities will be generated and a system of model evaluation algorithms will choose the bet model among the as many as a thousand possibilities. A final goal of this proposal is to automate the algorithms to the point that they could be implemented in a fully automatic way on a WEB server. Improved algorithms, will be made publicly available on the group fold prediction server. Existing databases of sold predictions will be continuously updated and extended to include various interesting protein families.
Keywords: computer program /software, computer simulation, computer system design /evaluation, mathematical model, model design /development, protein sequence, protein structure, structural biology, structural model, Internet, information system, protein folding, statistics /biometry, computer data analysis
Project start date: 2002-04-01
Project end date: 2007-03-31
FUNCTION DIVERGENCE IN PROTEIN FAMILIES
Adam Godzik, Associate Professor
Burnham Institute For Medical Research
la Jolla, Ca 92037
Grant 5R01GM060049-04 from National Institute Of General Medical Sciences IRG: BBCA
Abstract: The goal of the proposed research is to study functional similarities and differences between proteins with broadly similar structures, but no recognizable sequence similarity. Automated structure comparison programs, together with a new generation of fold prediction algorithms, provide us with many examples of proteins that do, or are likely to, belong to the same structural class. At the same time, such broad structural similarity may or may not be connected to any functional similarity. A database of protein structure and function will be built, allowing a search for groups of proteins with certain structural and functional features. Such groups will then be analyzed to identify structurally and functionally important regions in the sequence and to match them with levels of structural and functional similarity/ divergence. A combination of comparative modeling and structure analysis of protein structures will be used and forged into a standardized set of theoretical tools and procedures that could be predicted using threading and sequence analysis, but whose sequence divergence is large enough to expect significant functional differences. The overall goal of the research described in this project is to bring an understanding of the sequence/structure/function relationship or selected proteins to a level where the detailed predictions of function and mode of action for newly sequenced proteins could be accomplished. With the flood of new sequences coming from large scale genome sequencing projects, it is identifying the possible function of these proteins that would make these data so important
Keywords: artificial intelligence, molecular biology information system, protein sequence, protein structure function, structural biology DNA binding protein, RNA binding protein, active site, binding protein, computer system design /evaluation, open reading frame, periplasm, protein folding, structural model, thermodynamics computer program /software, computer simulation
Project start date: 1999-02-01
Project end date: 2004-01-31
5R01GM060049-04 (2002): $202646
5R01GM060049-03 (2001): $196860
5R01GM060049-02 (2000): $191240
5R01GM060049-05 (2003): $208609
JOINT CENTER FOR MOLECULAR MODELING
Adam Godzik, Professor
Burnham Institute For Medical Research, La Jolla, Ca 92037
Abstract: New tools based on graph theory have revolutionized genome analyses, providing better ways to identify and classify rearrangements of genomic fragments. The same tools recently also provided a major breakthrough in multiple sequence alignment. Here we propose to apply these tools to protein structure analysis and use the resulting insights into protein structure evolution to increase model quality in comparative modeling. Structure comparison between distant homologs show clearly that the dominant paradigm in structure comparison, that a protein structure could be divided into an invariant core and flexible loops breaks down below 40%-50% sequence identity threshold. Instead, significant rearrangements can happen anywhere in the structure, with secondary structure elements undergoing significant shifts and movements. As a result, standard protocol in comparative modeling, based on sequence mounting on a rigid core structure, must fail for such homologs. Structural differences between homologs are driven, as is the entire folding process, by free energy of the system, but because of serious deficiencies in current force fields and computational approaches, energy-based predictions of such changes are not successful. In this grant we propose to improve the quality of comparative modeling by first discovering and then applying empirical rules of protein structure changes. Rapid growth of the number of known protein structures, fueled in part by technical advances in high throughput structure determination spearheaded by the Protein Structure Initiative, resulted in increasingly dense coverage of the structural space of many folds. This provides a rich learning base to discover such empirical rule, provided a right formalism to describe protein structure changes can be developed. In preliminary analyses we have shown that in a next approximation after the invariant core/flexible loops, protein structure can be described as built from rigid subdomains, and simple rearrangements of these subdomains account for almost half of the structural differences between distant homologs. Moreover, proteins can only adopt structures lying in a specific low dimensionality subspace of the entire conformational space. To improve the quality of models from comparative modeling, we plan to identify conserved subdomains for all known folds and to describe the allowed subspaces by analyzing already known structures from these folds. In the next step we will use this information to generate possible variants of the template structure and use model evaluation tools to identify the one most similar to the [sic]
Keywords: Accounting; Adopted; Algorithms; Articulation; Biochemical Pathway; Core Protein; DISSEC; DNA Sequence Rearrangement; Development; Dissection; Distant; EC 2.7; Elements; Endopeptidases; Esteroproteases; Evaluation; Evolution; Experimental Models; Experimental Models, Other; Family; Fingerprint; Free Energy; GeneHomolog; Genome; Genomics; Goals; Grant; Graph; Homolog; Homologous Gene; Homologue; Joints; Kinases; Learning; Metabolic Networks; Methods; Modeling; Models, Experimental; Molecular Configuration; Molecular Conformation; Molecular Stereochemistry; Movement; Nucleic Acid Biochemistry, Molecular Modeling; Numbers; PSI; Peptidases; Peptide Hydrolases; Peptide Peptidohydrolases; Phosphatases; Phosphohydrolases; Phosphomonoesterases; Phosphoric Monoester Hydrolases; Phosphotransferases; Process; Proteases; Protein Structure Initiative; Protein/Amino Acid Biochemistry, Molecular Modeling; Proteinases; Proteins; Proteolytic Enzyme; Proteolytic Enzymes; Protocol; Protocols documentation; Rearrangement; Relative; Relative (related person); Reliance; SEQ-AN; Score; Sequence Alignment; Sequence Analyses; Sequence Analysis; Standards; Standards of Weights and Measures; Structure; System; System, LOINC Axis 4; Testing; Transphosphorylases; Variant; Variation; base; body movement; comparative; conformation; conformational state; design; designing; gene product; improved; insight; molecular modeling; new approaches; novel; novel approaches; novel strategies; novel strategy; protease; protein structure; proteinase; rapid growth; theories; tool
Project start date: 2006-04-01
Project end date: 2011-03-31
Budget start date: 1-APR-2008
Budget end date: 31-MAR-2011
PFA/PA: RFA-GM-05-008
5P20GM076221-03 (2008): $0
5P20GM076221-02 (2007): $671577
1P20GM076221-01 (2006): $700000
DEVELOPMENT OF A COMPREHENSIVE SYSTEM FOR DISTANT HOMOLOGY ANALYSIS
Adam Godzik
Burnham Institute For Medical Research, La Jolla, Ca 92037
Grant 5R01GM087218-02 from National Institute Of General Medical Sciences
Abstract: In 2000, our Fold and Function Assignment System (FFAS) server pioneered protein profile-profile algorithms, applying them to protein structure prediction. Since then, the basic ideas underpinning these algorithms have been used in over 20 distant homology recognition algorithms, and the public FFAS server is used by almost 1,000 registered users running hundreds of jobs per day, applying the results not only in protein structure prediction, but also in function prediction, target selection in structural genomics, and general analysis of diverse protein families. With ever increasing flow of new protein sequence, many of them representing new, uncharacterized families, the importance of distant homology recognition is constantly growing. We propose enhancing the FFAS structure and interface to match these new types of applications, developing the server into a major resource for studying broad and diversified protein families. We plan to extend the usefulness and maintainability of FFAS by restructuring its code using modem programming practices to develop a modular, multistage program ready to be integrated with other servers, as well as use other programs, developed both inside and outside our group, to improve quality of data at each step of the prediction process and also to export intermediate results to the user for analysis. By extending the set of analysis and visualization tools integrated into the FFAS server and improving its user interface, we want to make it easier to be used by a generally trained biologist. Finally, we plan to perform a significant hardware update to avoid delays in providing annotations for user-submitted sequences
Keywords: Active Sites; Adopted; Algorithms; Amino Acid Sequence; Apoptosis Response Protein; Benchmarking; Best Practice Analysis; Binding; Binding (Molecular Function); Code; Coding System; Communication; Communities; Computer Systems Development; Data Banks; Data Bases; Data Quality; Databank, Electronic; Databanks; Database, Electronic; Databases; Development; Development, Computer Systems; Disease; Disorder; Distant; Documentation; Evolution; Extensible Markup Language; Family; Frequencies (time pattern); Frequency; Functional Metagenomics; Goals; Grant; Graphical interface; Human Genome; Hypothetical Protein; Imagery; Internet; Jobs; Journals; Libraries; Licensing; Magazine; Manuscripts; Metagenomics; Methods; Modeling; Molecular; Molecular Biology, Protein Sequencing; Molecular Interaction; Occupations; PAWR; PAWR protein; PRKC, Apoptosis, WT1, Regulator; Peer Review; Peptide Sequence Determination; Principal Investigator; Process; Professional Postions; Programs (PT); Programs [Publication Type]; Prostate Apoptosis Response Protein 4; Protein Family; Protein Sequencing; Protein Structure, Primary; Proteins; Protocol; Protocols documentation; Publications; Research Resources; Resources; Running; Sampling; Scientific Publication; Sequence Determinations, Amino Acid; Sequence Determinations, Protein; Site; Source; Structure; System; System, LOINC Axis 4; Systems Development; Testing; Time; Training; Transcriptional Repressor PAR4; Update; Visualization; WT1-Interacting Protein; WWW; XML; base; clinical data repository; clinical data warehouse; data exchange; data repository; design; designing; disease/disorder; experiment; experimental research; experimental study; flexibility; gene product; genome sequencing; graphic user interface; graphical user interface; improved; meetings; methods to study multiple-level influences; multilevel analysis; multilevel model; multilevel modeling; open source; par-4 protein; pathogen; programs; protein function; protein profiling; protein sequence; protein structure; protein structure function; protein structure prediction; relational database; research study; software systems; structural biology; structural genomics; tool; usability; user-friendly; web; world wide web
Relevance: By providing reliable and easily updated annotations for many uncharacterized proteins that come from genomes of human pathogens or commensal bacterial flora (or any other source), FFAS allows us to predict many molecular aspects of functions of proteins involved in diseases as well as help formulate specific hypotheses about functions of such proteins and molecular mechanisms of diseases
Project start date: 2009-09-30
Project end date: 2011-08-31
Budget start date: 1-SEP-2010
Budget end date: 31-AUG-2011
PFA/PA: PAR-08-010
5R01GM087218-02 (2010): $477500
1R01GM087218-01 (2009): $477500
Sponsored Links Excellgen http://Excellgen.com
CORE--Shared Resources Informatics & Data Management
Adam Godzik, Associate Professor
Burnham Institute For Medical Research La Jolla, Ca 92037
Grant 2P30CA030199-249019 from National Cancer Institute IRG: NCI
Abstract: High-throughput technologies such as large scale sequencing, DNA expression arrays, and high throughput proteomics have explosively increased the amount of data and the need for data management and analysis. The Burnham Institute Cancer Center requires effective exchange of information and coordination of research due to its strong collaborative culture and a large number of multi-investigator projects. The Informatics and Data Management Facility provides resources needed to integrate informatics and data management for the Institute in general, for the Cancer Drug Discovery Initiative in particular, and specifically for the five facilities that depend heavily on informatics (Gene Analysis, Proteomics, Chemical Library Screening, High Throughput Cell Analysis Facilities, and the Informatics and Data Management Facility itself). The Informatics and Data Management Facility will provide the following services 1) Data storage, archiving, and retrieval, 2) Data analysis, 3) Data annotation, publishing, and mining, 4) Biostatistics, 5) Molecular modeling, structure prediction, and custom databases, 6) Installation and maintenance of applications, and 7) Training. Emerging concepts of database confederation - database wrapping or meta-databases - have immediate applications in this environment. Dedicated databases, some publicly available on the Internet, are the answer to managing, accessing and integrating data, and publishing that data in a way that conforms to standards and is readily usable. We propose creating an Institute-wide data management layer that would coordinate various data-intensive projects at the Institute via a common interface, adding elements of database confederation with various publicly available resources. The central concept in this proposed structure would be a ?Molecule page?, i.e., a database record containing links to all of the available information (from gene to function) to Institute researchers. At least two such systems, Molecular Pages at the Alliance for Cell Signaling and central annotation database for the Joint Center for Structural Genomics, have been developed at the nearby San Diego Supercomputer Center, with direct integration and support from the Burnham faculty members.
Keywords: biomedical facility, computer center, computer data analysis, data management, cancer information system, computer program /software, neoplasm /cancer, oncology, high throughput technology
Project start date: 2004-06-28
Project end date: 2009-04-30
TRANSPORTPDB: CENTER FOR THE X-RAY STRUCTURE DETERMINATION OF HUMAN TRANSPORTERS
Adam Godzik
California Institute Of Technology, Office Of Sponsored Research, Mail Code 201-15, Pasadena, Ca 91125
Keywords: Human; Human, General; Man (Taxonomy); Man, Modern; Radiation, X-Rays; Radiation, X-Rays, Gamma-Rays; Roentgen Rays; Structure; X-Radiation; X-Rays; Xrays
Project start date: 2010-09-30
Project end date: 2015-06-30
Budget start date: 30-SEP-2010
Budget end date: 30-JUN-2011
PFA/PA: RFA-GM-10-006
1U54GM094610-01_5908 (2010): $171516