SCOP-Aided Fragment Assembly Protein Structure Prediction

Abbass, Jad and Nebel, Jean-Christophe (2019) SCOP-Aided Fragment Assembly Protein Structure Prediction. In: 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA); 03 - 04 Jul 2019, Beirout, Lebanon. (Unpublished)

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Abstract

Despite some limited success, computational biology has not been able to produce reliable results in the field of protein structure prediction. Although the fragment assembly approach has shown a lot of potential, it still requires substantial improvements. Not only are its predictions largely inaccurate whenever a protein exceeds 150 amino acids in length, but also, even for short targets, inconsistencies of the energy function associated with the enormous search space too often lead to the generation of erroneous conformations. Moreover, as it relies on the creation of a large number of decoys, it is highly computational expensive. Based on its secondary structure content, a protein can generally be classified into one of the standard structural classes, i.e. all-alpha, all-beta or alpha-beta. Since structural class prediction has reached a prominent accuracy, it is proposed to amend the standard pipeline of fragment-based methods by including some constraints on the template proteins from which fragments are extracted. Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, the suggested customized method was evaluated on 67 former CASP targets ranging from 47 to 149 amino acids in length. Using SCOP-based structural class annotations, improvement of structure prediction performance is highly significant in terms of GDT (53 out of 67 targets show higher scores of 6.1% on average, p-value < 0.0005).

Item Type: Conference or Workshop Item (Paper)
Event Title: 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)
Uncontrolled Keywords: fragment assembly protein structure prediction; Rosetta; strucutural class; SCOP
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Depositing User: Kevin Hiscox
Date Deposited: 13 Jun 2019 08:12
Last Modified: 13 Jun 2019 08:12
URI: http://eprints.kingston.ac.uk/id/eprint/43466

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