MOTIVATION Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. METHOD We present an approach that reveals subtle but systematic differences in the ...

With respect to the fact that the prediction of Protein secondary structure based on amino acids is very important, therefore, this study tries to present a new method based on the fuzzy combinational structure of a set of feed-forward neural networks so that the prediction accuracy of Protein secondary structure can be improved compared with the existing methods. Neural networks used in this p...

In molecular biology, sequence alignment is a fundamental but powerful technique. Biologists find the similarity, the difference and even the function of the input sequences (DNA, RNA and protein sequences) by it. With various purposes, there are many algorithms to align two sequences based on different criteria. Though there are various ways to align macromolecular sequences, a sequence alignm...

The protein structure prediction problem is one of the most studied problems in Computational Biology. It can be reasonably abstracted as a minimization problem. The function to be minimized depends on the distances between the various amino-acids composing the protein and on their types. Even with strong approximations, the problem is shown to be computationally intractable. However, the solut...

In the present study we attempted to correlate the structure and function of the cry11a (72 kDa) and cry4a (135 kDa) proteins of Bacillus thuringiensis var israelensis. Homology modeling and secondary structure predictions were done to locate most probable regions for finding helices or strands in these proteins. The JPRED (JPRED consensus secondary structure prediction server) secondary struct...

Background: Establishing theories for designing arbitrary protein structures is complicated and depends on understanding the principles for protein folding, which is affected by applied features. Computer algorithms can reach high precision and stability in computationally designing enzymes and binders by applying informative features obtained from natural structures. Methods: In this study, a ...

Understanding secondary structures of RNAs helps to determine their chemical and biological properties. Only a small number of RNA structures have been determined currently since such structure determination experiments are timeconsuming and expensive. As a result, scientists start to rely on RNA secondary structure prediction. Unlike protein structure prediction, predicting RNA secondary struc...

Protein structure prediction is one of the most important problems in modern computational biology. Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machine (SVM) etc., to focus on the prediction of the secondary structures. In th...

The prediction of protein secondary structure is an important step in the prediction of protein tertiary structure. A new protein secondary structure prediction method, SVMpsi, was developed to improve the current level of prediction by incorporating new tertiary classifiers and their jury decision system, and the PSI-BLAST PSSM profiles. Additionally, efficient methods to handle unbalanced dat...

SUMMARY Protein secondary structure and solvent accessibility predictions are a fundamental intermediate step towards protein structure and function prediction. We present new systems for the ab initio prediction of protein secondary structure and solvent accessibility, Porter 4.0 and PaleAle 4.0. Porter 4.0 predicts secondary structure correctly for 82.2% of residues. PaleAle 4.0's accuracy is...