Our broad objective is to contribute to the functional characterization of proteins based on structural considerations. When an experimentally determined atomic structure of a protein is not available, homology or comparative modeling can often predict a useful model of a given sequence by relying on its similarity to proteins of known structure. We focus on improving the accuracy and applicability of comparative modeling, making it useful to as many scientists as possible, and illustrating its utility.
The aims are: 1. Increase the sensitivity of detecting known protein structures related to the modeled sequence, by consideration of multiple sequences and structures, protein-protein interactions, and a statistical significance test. 2. Minimize errors in protein sequence-structure alignments, by an iterative process of alignment, model building, and model assessment, as well as by structure and multiple sequence information. 3. Minimize model errors in sidechains, core regions and loops, by relying on satisfaction of spatial restraints from template structures and energy terms from molecular mechanics, including an implicit solvation model. 4. Improve prediction of errors in comparative models, by relying on multivariate model assessment functions. 5. Maximize the utility of comparative modeling to the biomedical community, by implementing our methods in MODELLER, maximizing their speed, automating the protocols, assessing their accuracy, increasing the ease of use, distributing the program, and educating users. 6. Facilitate structure determination of the first complete mammalian ribosome from Canis lupus, by providing comparative models of proteins and fitting them into the electron cryo-microscopy map of the ribosomal particle at ~8A resolution. Achieving these aims will augment other projects in our laboratory, including development and application of a software system for automated large-scale comparative modeling, protocols for functional annotation of proteins, and methods for structure characterization of macromolecular assemblies. More broadly, these aims are especially timely due to the advent of structural and functional genomics of proteins and their complexes, which will benefit from increased accuracy, applicability, and efficiency of comparative modeling.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM054762-11
Application #
6925244
Study Section
Special Emphasis Panel (ZRG1-BPC-Q (02))
Program Officer
Wehrle, Janna P
Project Start
1996-06-01
Project End
2009-06-30
Budget Start
2005-07-01
Budget End
2006-06-30
Support Year
11
Fiscal Year
2005
Total Cost
$304,992
Indirect Cost
Name
University of California San Francisco
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Webb, Benjamin; Sali, Andrej (2016) Comparative Protein Structure Modeling Using MODELLER. Curr Protoc Bioinformatics 54:5.6.1-5.6.37
Webb, Benjamin; Lasker, Keren; Velázquez-Muriel, Javier et al. (2014) Modeling of proteins and their assemblies with the Integrative Modeling Platform. Methods Mol Biol 1091:277-95
Webb, Benjamin; Sali, Andrej (2014) Comparative Protein Structure Modeling Using MODELLER. Curr Protoc Bioinformatics 47:5.6.1-32
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Schlessinger, A; Yee, S W; Sali, A et al. (2013) SLC classification: an update. Clin Pharmacol Ther 94:19-23
Geier, Ethan G; Schlessinger, Avner; Fan, Hao et al. (2013) Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1. Proc Natl Acad Sci U S A 110:5480-5
Dong, Guang Qiang; Fan, Hao; Schneidman-Duhovny, Dina et al. (2013) Optimized atomic statistical potentials: assessment of protein interfaces and loops. Bioinformatics 29:3158-66
Fan, Hao; Hitchcock, Daniel S; Seidel 2nd, Ronald D et al. (2013) Assignment of pterin deaminase activity to an enzyme of unknown function guided by homology modeling and docking. J Am Chem Soc 135:795-803
Klammt, Christian; Maslennikov, Innokentiy; Bayrhuber, Monika et al. (2012) Facile backbone structure determination of human membrane proteins by NMR spectroscopy. Nat Methods 9:834-9

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