Customised fragments libraries for protein structure prediction based on structural class annotations

Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone

How genomes could transform surveillance and facial recognition

Despite decades of research, automatic visual surveillance systems have not delivered the results initially promised to the public. A key issue behind this, which remains unsolved, is the variability, complexity and inconsistency of the images being produced. The science of genomics offers insights that could solve this problem, and also prompt advances in facial recognition. </jats:p

Profile hidden Markov models for foreground object modelling

Accurate background/foreground segmentation is a preliminary process essential to most visual surveillance applications. With the increasing use of freely moving cameras, strategies have been proposed to refine initial segmentation. In this paper, it is proposed to exploit the Vide-omics paradigm, and Profile Hidden Markov Models in particular, to create a new type of object descriptors relying on spatiotemporal information. Performance of the proposed methodology has been evaluated using a standard dataset of videos captured by moving cameras. Results show that usage of the proposed object descriptors allows better foreground extraction than standard approaches

Probabilistic grammatical model of protein language and its application to helix-helix contact site classification

BACKGROUND: Hidden Markov Models power many state‐of‐the‐art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium‐ and long‐range residue‐residue interactions. This requires an expressive power of at least context‐free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. RESULTS: In this work, we present a probabilistic grammatical framework for problem‐specific protein languages and apply it to classification of transmembrane helix‐helix pairs configurations. The core of the model consists of a probabilistic context‐free grammar, automatically inferred by a genetic algorithm from only a generic set of expert‐based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix‐helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites. CONCLUSIONS: We demonstrated that our probabilistic context‐free framework for analysis of protein sequences outperforms the state of the art in the task of helix‐helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human‐readable. Thus they could provide biologically meaningful information for molecular biologists

Rosetta and the journey to predict proteins' structures, 20 years on

For two decades, Rosetta has consistently been at the forefront of protein structure prediction. While it has become a very large package comprising programs, scripts, and tools, for different types of macromolecular modelling such as ligand docking, protein-protein docking, protein design, and loop modelling, it started as the implementation of an algorithm for ab initio protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the literature to describe that algorithm and its contribution to the third edition of the community wide Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers have been contributing to deciphering ’the second half of the genetic code’. Although the focus of Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is associated with its fragment-assembly protein structure prediction approach. Following a presentation of the main concepts underpinning its foundation, especially sequence-structure correlation and usage of fragments, we review the main stages of its developments and highlight the milestones it has achieved in terms of protein structure prediction, particularly in CASP