One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend. The determination of protein structure is experimental...

Summary: A key to understanding RNA function is to uncover its complex 3D structure. Experimental methods used for determining RNA 3D structures are technologically challenging and laborious, which makes the development of computational prediction methods of substantial interest. Previously, we developed the iFoldRNA server that allows accurate prediction of short (<50 nt) tertiary RNA structur...

This chapter gives a graceful introduction to problem of protein threedimensional structure prediction, and focuses on how to make structural sense out of a single input sequence with unknown structure, the ‘query’ or ‘target’ sequence. We give an overview of the different classes of modelling techniques, notably template-based and template free. We also discuss the way in which structural pred...

MOTIVATION Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate th...

The prediction of β-turn, despite the observation that one out of four residues in protein belongs to this structure element, has attracted considerably less attention comparing to secondary structure predictions. Neural network machine learning is a popular approach to address such a problem of structural bioinformatics. In this paper, we describe a new neural network model for β-turn predicti...

From 1 School of Life Sciences, Oxford Brookes University, Oxford, OX3 0BP, UK; 2 Leibniz-Institut für Molekulare Pharmakologie & Neurocure Initiative Charité Universitäts Medizin, 13125 Berlin, Germany; 3 Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford OX1 3QU; 4 Department of Pharmacology, University College London, London WC1...

NMR Structure Improvement: A Structural Bioinformatics & Visualization Approach. by Jeremy N. Block Department of Biochemistry Duke University Date:_______________________ Approved: ___________________________ David C. Richardson, Co-Supervisor ___________________________ Jane S. Richardson, Co-Supervisor ___________________________ John D. York ___________________________ Pei Zhou ____________...

Protein secondary structure prediction is a problem related to structural bioinformatics which deals with the prediction and analysis of macromolecules i.e. DNA, RNA and protein. It is an important step towards elucidating its three dimensional structure, as well as its function. Secondary structure of a protein can be predicted from its primary structures i.e. from the amino

Fragment-based analysis of protein three-dimensional (3D) structures has received increased attention in recent years. Here, we used a set of pentamer local structure alphabets (LSAs) recently derived in our laboratory to represent protein structures, i.e. we transformed the 3D structures into one-dimensional (1D) sequences of LSAs. We then applied Hidden Markov Model training to these LSA sequ...

Predicting the secondary structure of an RNA sequence is an important problem in structural bioinformatics. The general RNA folding problem, where the sequence to be folded may contain pseudoknots, is computationally intractable when no prior knowledge on the pseudoknot structures the sequence contains is available. In this paper, we consider stable stems in an RNA sequence and provide a new ch...