Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.

ABSTRACT: Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-ba

SUBMITTER: Saraswathi S 

PROVIDER: S-EPMC4163568 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature