A primary and a secondary neural network are applied to secondary structure and structural class prediction for a database of 681 non-homologous protein chains. A new method of decoding the outputs of the secondary structure prediction network is used to produce an estimate of the probability of finding each type of secondary structure at every position in the sequence. In addition to providing...

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of th...

The functional properties of proteins depend upon their 3D structures, therefore, it is advantageous to deduce the 3D structure of a protein from its amino acid sequence. This is a difficult task because there are 20 different amino acids that can be combined into “many more different proteins than there are atoms in the known universe” [2]. De novo prediction methods often involve a first step...

The strong coupling between secondary and tertiary structure formation in protein folding is neglected in most structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary structures can be used to improve secondary structure prediction. The architecture of a neural network for secondary structure prediction that utilizes multiple seq...

UNLABELLED An interactive protein secondary structure prediction Internet server is presented. The server allows a single sequence or multiple alignment to be submitted, and returns predictions from six secondary structure prediction algorithms that exploit evolutionary information from multiple sequences. A consensus prediction is also returned which improves the average Q3 accuracy of predict...

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning exte...

In this paper a technique to improve protein secondary structure prediction is proposed. The approach is based on the idea of combining the results of a set of prediction tools, choosing the most correct parts of each prediction. The correctness of the resulting prediction is measured referring to accuracy parameters used in several editions of CASP. Experimental evaluations validating the prop...

In this paper, a method for secondary structure with support vector machines is presented. The system used two layers of support vector machines, with a weighted cost function to balance the uneven class memberships. Using this method, prediction accuracy reaches 71.5%, comparable to the best techniques avaliable.

UNLABELLED Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter...

Certificate This is to certify that, Varun Aggarwal, (104/ECE/2000) a student of NSIT, Delhi, India did his summer training under me at Stockholm Bioinformatics Center for the months of June-July 2003. He worked on two projects documented in this report. Acknowledgement I will like to thanks Dr. Bob MacCallum for giving me this opportunity to work with his group. I hugely benefited and wish to ...