Computational methods for structural-functional studies of protein

Ut Southwestern Medical Center, Dallas TX

Investigators

Linked publications, trials & patents

Abstract

DESCRIPTION (provided by applicant): Computational analysis of proteins is an essential shortcut to random experimentation. Multiple sequence alignments (MSAs) reveal evolutionary history of a protein family, govern predictions of 3D structures and functions and guide experimental design. Accuracy of these alignments is critical for the accuracy of conclusions from their analysis. With the finding from the previous round of the grant, we significantly advanced the power of sequence similarity search and improved the accuracy of MSA. Using these techniques, we aided biological discoveries in dozens of collaborations with experimentalists, analyzed medically important protein families and implemented a number of public web-servers. For the next funding period we propose to: 1) Build on our advances to perfect homology search and multiple sequence alignment. Sequence profile search will be improved by more sound statistics and by averaging scores over predicted homologs of found hits. Sequence alignment will be corrected in regions that interact less closely with the rest of the protein and segments that require large adjustments. 2) Maintain, improve and integrate our protein sequence analysis servers. During the first funding period of the grant, in addition to improving our sequence search and alignment web-servers, we developed three new servers for predicting a number of characteristics for a protein sequence, finding literature about a protein and visualizing relationships between proteins as networks, and compiled a searchable database of clinical mutations. We will integrate these servers into a single sequence analysis stop, augmented with other information, such as expression patterns, protein interactions, human polymorphism and known diseases. 3) Develop an Atlas of clinical mutations in proteins, freely available for browsing and download without login requirements. Each out of 25,000 known mutations will have a dedicated web-page with mutation's characteristics and predictions about its negative effects.