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BEGIN:VEVENT
DTSTAMP:20260310T154818Z
DTSTART;VALUE=DATE-TIME:20180305T130000
DTEND;VALUE=DATE-TIME:20180305T140000
SUMMARY:Charlotte Deane (Oxford)
TZID:Europe/London
UID:20180305-8a17841a6012ba7a01606a79cd026208@warwick.ac.uk
CREATED:20171218T163415Z
DESCRIPTION:Biologically inspired de novo protein structure prediction Pr
 otein structures can elucidate functional understanding\, explain diseas
 e mechanisms and inform drug design. However\, experimental structure de
 termination is costly\, and technically difficult. However\, while the t
 hree-dimensional structure of proteins is difficult to obtain amino acid
  sequences are easily available and far outnumber solved structures. The
 re are two main methods for protein structure prediction template based 
 and de novo. Current de novo protein structure prediction methods are he
 uristics limited by the enormous search space\, with successful predicti
 on largely restricted to small\, single domain proteins. The three key c
 omponents of most de novo methods for protein structure prediction are t
 he fragment library\, the “energy” function and the search method. In th
 is talk I will give an overview of my groups work on improving each of t
 hese stages. Firstly\, describing the development of a novel fragment li
 brary Flib that uses predicted secondary structure to determine library 
 generation strategy [1]. Secondly\, giving a comparison of the different
  co-evolution contact predictors in terms of their ability to improve pr
 otein structure prediction [2]. Finally demonstrating how sequential pre
 diction approaches using SAINT2 can improve both search heuristics and f
 inal model quality [3]. [1] Saulo H P de Oliveira\, Jiye Shi\, Charlotte
  M Deane\, Building a better fragment library for de novo protein struct
 ure prediction\, Plos One\, 2015\, 10(4)\, e0123998 [2] Saulo H P de Oli
 veira\, Jiye Shi\, Charlotte M Deane\, Comparing co-evolution methods an
 d their application to template-free protein structure prediction\, Bioi
 nformatics\, 2017\; 33 (3): 373-381. [3] Saulo H P de Oliveira\, Eleanor
  C. Law\, Jiye Shi\, Charlotte M. Deane\, Sequential search leads to fas
 ter\, more efficient fragment-based de novo protein structure prediction
 \, Bioinformatics\, 2017\, btx722
LOCATION:Physical Sciences (PS0.17)
CATEGORIES:
LAST-MODIFIED:20180107T190431Z
ORGANIZER;CN=Peter Brommer:
END:VEVENT
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