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SUMMARY:Biologically inspired de novo protein structure prediction - Profe
 ssor Charlotte Deane\, Professor of Structural Bioinformatics & Head of De
 partment\,  Department of Statistics University of Oxford
DTSTART:20180117T103000Z
DTEND:20180117T113000Z
UID:TALK98128@talks.cam.ac.uk
CONTACT:Patrick Flagmeier
DESCRIPTION:Protein structures can elucidate functional understanding\, ex
 plain disease mechanisms and inform drug design. However\, experimental st
 ructure determination is costly\, and technically difficult. However\, whi
 le the three-dimensional structure of proteins is difficult to obtain amin
 o acid sequences are easily available and far outnumber solved structures.
  There are two main methods for protein structure prediction template base
 d and de novo. Current de novo protein structure prediction methods are he
 uristics limited by the enormous search space\, with successful prediction
  largely restricted to small\, single domain proteins. \nThe three key com
 ponents of most de novo methods for protein structure prediction are the f
 ragment library\, the “energy” function and the search method. In this
  talk I will give an overview of my groups work on improving each of these
  stages. Firstly\, describing the development of a novel fragment library 
 Flib that uses predicted secondary structure to determine library generati
 on strategy [1]. Secondly\, giving a comparison of the different co-evolut
 ion contact predictors in terms of their ability to improve protein struct
 ure prediction [2]. Finally demonstrating how sequential prediction approa
 ches using SAINT2 can improve both search heuristics and final model quali
 ty [3]. \n\n[1] Saulo H P de Oliveira\, Jiye Shi\, Charlotte M Deane\, Bui
 lding a better fragment library for de novo protein structure prediction\,
  Plos One\, 2015\, 10(4)\, e0123998\n[2] Saulo H P de Oliveira\, Jiye Shi\
 , Charlotte M Deane\, Comparing co-evolution methods and their application
  to template-free protein structure prediction\, Bioinformatics\, 2017\; 3
 3 (3): 373-381. \n[3] Saulo H P de Oliveira\, Eleanor C. Law\, Jiye Shi\, 
 Charlotte M. Deane\, Sequential search leads to faster\, more efficient fr
 agment-based de novo protein structure prediction\, Bioinformatics\, 2017\
 , btx722
LOCATION:Department of Chemistry\, Cambridge\, Pfizer lecture theatre
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