Protein structure prediction is the major problem in the field of bioinformatics or Computation biology. Recently many researchers used various data mining and machine learning tool for protein structure prediction. My intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and our objective is to enhance the prediction of 1D and 2D protei...

Prediction of protein secondary structure is an important step on the way to spell out its three dimensional structure and its function. This paper describes a new technique for prediction of secondary structure of protein based on contemporary machine learning methodology and data mining approach. More than one method has been developed to predict the protein secondary structure from the amino...

Protein structure is generally more conserved than sequence, but for regions that can adopt different structures in different environments, does this hold true? Understanding how structurally disordered regions evolve altered secondary structure element propensities as well as conformational flexibility among paralogs are fundamental questions for our understanding of protein structural evoluti...

Recent studies suggest that reducing the induction temperature would improve the quality of some recombinant inclusion bodies (IB) by providing a native-like secondary structure and leading to an improvement in protein recovery. This study focused on optimizing the solubilization condition of Reteplase, a recombinant protein with 9 disulfide bonds. The influence of lowering induction temperatur...

Recent studies suggest that reducing the induction temperature would improve the quality of some recombinant inclusion bodies (IB) by providing a native-like secondary structure and leading to an improvement in protein recovery. This study focused on optimizing the solubilization condition of Reteplase, a recombinant protein with 9 disulfide bonds. The influence of lowering induction temperatur...

A small set of protein fragments can represent adequately all known local protein structure. This set of fragments, along with a construction scheme that assembles these fragments into structures, defines a discrete (relatively small) conformation space, which approximates protein structures accurately. We generate protein decoys by sampling geometrically valid structures from this conformation...

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder ...

As there are quite a few difficulties for us to predict a protein structural class directly from its primary sequence, the protein structural prediction based on the predicted secondary structure will undoubtedly be the first choice we would like to take. Protein structural classes are generally defined as four classes: α, β, α/β, α +β. The protein secondary structure describes the local struct...

A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 predi...

Recent genome science has been developing from two sides: one is biological evolution due to mutation of DNA sequence and the other is biological function of protein molecules formed according to amino acid sequences. We have devoted ourselves to elucidate the mechanism of protein folding, and to develop the method for protein structure prediction [1, 2]. Our method has successfully yielded the...