Protein model construction and evaluation.

The prediction of protein secondary and tertiary structure is becoming increasingly important as the number of sequences available to the biological community far exceeds the number of unique native structures. The following chapters describe the conception, construction, evaluation and application of a series of algorithms for the prediction and evaluation of two and three-dimensional protein structure. In chapter 1 a brief overview of protein structure and the resources required to predict protein features is given. Chapter 2 describes the investigation of sequence identity and alignments on the prediction of two-dimensional protein structure in the form of long and short range protein contacts a feature which is known to correlate with solvent accessibility. It also describes the identification of a feature which is referred to as the 'Empty Quarter' which forms the basis of an evaluation function described in Chapter 3 and developed in Chapter 4. Chapter 3 introduces the Dynamic Domain Threading method used during round six of the CASP exercise. Phobic, a protein evaluation function based on predicted solvent accessibility is described in Chapter 4. The de novo prediction of a/p proteins is described in Chapter 5, the method introduces a new approach to the old problem of combinatorial modelling and breaks the size limit previously imposed on de novo prediction. The final experimental chapter describes the prediction of solvent accessibility and secondary structure using a novel combination of the fuzzy k-nearest neighbour and support vector machine. Chapter 7 closes this piece of work with a review of the field and suggests potential improvements to the way work is conducted

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe