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 (S. Saraswathi, et al., [1]). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced 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 prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.

The Influence of Flanking Secondary Structures on Amino Acid Content and Typical Lengths of 3/10 Helices

“…In general, all the pure random coil regions are enriched by Gly relative to the connecting bridges containing 3/10 helices. Such strong helix-formers as Ala, Gln, and Leu [ 20 ] are found significantly more frequently only in certain types of connecting bridges containing 3/10 helices than in pure coil.…”

Hot spot prediction in protein-protein interactions by an ensemble system

“…On the other hand, the study of hot spot residues can also be used to predict the secondary structure of proteins. Saraswathi et al found that different amino acid distributions play a crucial role in determining secondary structures [3]. In previous studies, hot spot *Correspondence: pchen.ustc10@yahoo.com; wangbing@ustc.edu 1 Institute of Physical Science and Information Technology, Anhui University, 230601 Hefei, Anhui, China 2 School of Electrical and Information Engineering, Anhui University of Technology, 243032 Ma'anshan, Anhui, China Full list of author information is available at the end of the article residues were identified by experimental methods, such as alanine mutagenesis scanning [4].…”