Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index.
BACKGROUND
Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure--by using sequence information only--is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence.


RESULTS
Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins.


CONCLUSIONS
Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset.
Sikder
Abdur R
AR
Advanced Networks Research Group, School of Information Technologies, J12, University of Sydney, NSW 2006, Australia. sikder@it.uysd.edu.au
Zomaya
Albert Y
AY
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D030562:Databases, Protein; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D040681:Structural Homology, Protein