secondary structure of protein download free

Baldi et al., 1999 Baldi P., Brunak S., Frasconi P., Soda G., Pollastri G., Exploiting the past and the future in protein secondary structure prediction, Bioinformatics 15 ( 1999) 937– 946.

Babaei et al., 2010 Babaei S., Geranmayeh A., Seyyedsalehi S.A., Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks, Comput.

Anfinsen et al., 1961 Anfinsen C.B., Haber E., White F.H., The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain, Proc.

Altschul et al., 1997 Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res.

Adamczak et al., 2005 Adamczak R., Porollo A., Meller J., Combining prediction of secondary structure and solvent accessibility in proteins, Proteins 59 ( 3) ( 2005) 467– 475, 10.1002/prot.20441.

It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction.

This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. An informative introduction to protein secondary structure and artificial neural networks is also included for context. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico.

Secondary structure of protein download free