Motivation: The 3D structure of a protein sequence can be assembled from the substructures corresponding to small segments of this sequence. For each small sequence segment, there are only a few more likely substructures. We call them the 'structural alphabet' for this segment. Classical approaches such as ROSETTA used sequence profile and secondary structure information, to predict structural fragments. In contrast, we utilize more structural information, such as solvent accessibility and contact capacity, for finding structural fragments. Results: Integer linear programming technique is applied to derive the best combination of these sequence and structural information items. This approach generates significantly more accurate and succinct structural alphabets with more than 50% improvement over the previous accuracies. With these novel structural alphabets, we are able to construct more accurate protein structures than the state-of-art ab initio protein structure prediction programs such as ROSETTA. We are also able to reduce the Kolodny's library size by a factor of 8, at the same accuracy.
|Original language||English (US)|
|State||Published - Jul 2008|
Bibliographical noteFunding Information:
D.B. was also partially supported by a Chinese Government Scholarship Program and an NSFC grant 60496320.
Funding: This work is supported by NSERC OGP0046506, the Canada Research Chair program and MITACS, and was made possible by the facilities of the SHARCNET (www.sharcnet.ca).
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics