ORIGINAL PAPER 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 deter-mine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have devel-oped a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generat-ed by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and ad-vanced particle swarm optimization (PSO) are used with this data to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. Secondary structure predictions and their accuracy are usually presented for three secondary structure elements: α-helix, β-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure predic-tion. We investigate the influence of the composition, physico-chemical properties and position specific occur-rence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present de-tailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering