An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps

Abstract. An important unsolved problem in structural bioinformatics is that of protein structure prediction (PSP), the reconstruction of a biologically plausible three-dimensional structure for a given protein given only its amino acid sequence. The PSP problem is of enormous interest, because the function of proteins is a direct consequence of their three-dimensional structure. Approaches to solve the PSP use protein models that range from very realistic (all-atom) to very simple (on a lattice). Finer representations usually generate better candidate structures, but are computationally more costly than the simpler on-lattice ones. In this work we propose a combined approach that makes use of a simple and fast lattice protein structure prediction algorithm, REMC-HPPFP, to compute a number of coarse candidate structures. These are later refined by 3Distill, an off-lattice, residue-level protein structure predictor. We prove that the lattice algorithm is able to bootstrap 3Distill, which consequently converges much faster, allowing for shorter execution times without noticeably degrading the quality of the predictions. This novel method allows us to generate a large set of decoys of quality comparable to those computed by the off-lattice method alone, but using a fraction of the computations. As a result, our method could be used to build large databases of predicted decoys for analysis, or for selecting the best candidate structures through reranking techniques. Furthermore our method is generic, in that it can be applied to other algorithms than 3Distill

Secondary structure-based template selection for fragment-assembly protein structure prediction

Proteins play critical biochemical roles in all living organisms; in human beings, they are the targets of 50% of all drugs. Although the first protein structure was determined 60 years ago, experimental techniques are still time and cost consuming. Consequently, in silico protein structure prediction, which is considered a main challenge in computational biology, is fundamental to decipher conformations of protein targets. This thesis contributes to the state of the art of fragment-assembly protein structure prediction. This category has been widely and thoroughly studied due to its application to any type of targets. While the majority of research focuses on enhancing the functions that are used to score fragments by incorporating new terms and optimising their weights, another important issue is how to pick appropriate fragments from a large pool of candidate structures. Since prediction of the main structural classes, i.e. mainly-alpha, mainly-beta and alpha-beta, has recently reached quite a high level of accuracy, we have introduced a novel approach by decreasing the size of the pool of candidate structures to comprise only proteins that share the same structural class a target is likely to adopt. Picking fragments from this customised set of known structures not only has contributed in generating decoys with higher level of accuracy but also has eliminated irrelevant parts of the search space which makes the selection of first models a less complicated process, addressing the inaccuracies of energy functions. In addition to the challenge of adopting a unique template structure for all targets, another one arises whenever relying on the same amount of corrections and fine tunings; such a phase may be damaging to “easy’ targets, i.e. those that comprise a relatively significant percentage of alpha helices. Owing to the sequence-structure correlation based on which fragment-based protein structure prediction was born, we have also proposed a customised phase of correction based on the structural class prediction of the target in question. After using secondary structure prediction as a “global feature” of a target, i.e. structural classes, we have also investigated its usage as a “local feature” to customise the number of candidate fragments, which is currently the same at all positions. Relying on the known facts regarding diversity of short fragments of helices, sheets and loops, the fragment insertion process has been adjusted to make “changes” relative to the expected complexity of each region. We have proved in this thesis the extent to which secondary structure features can be used implicitly or explicitly to enhance fragment assembly protein structure prediction