An Olfactory Receptor Pseudogene whose Function emerged in Humans

Human olfactory receptor, hOR17-210, is identified as a pseudogene in the human genome. Experimental data has shown however, that the gene product of cloned hOR17-210 cDNA was able to bind an odorant-binding protein and is narrowly tuned for excitation by cyclic ketones. Supported by experimental results, we used the bioinformatics methods of sequence analysis, computational protein modeling and docking, to show that functionality in this receptor is retained due to sequence-structure features not previously observed in mammalian ORs. This receptor does not possess the first two transmembrane helical domains (of seven typically seen in GPCRs). It however, possesses an additional TM that has not been observed in other human olfactory receptors. By incorporating these novel structural features, we created two putative models for this receptor. We also docked odor ligands that were experimentally shown to bind hOR17-210 model. We show how and why structural modifications of OR17-210 do not hinder this receptor's functionality. Our studies reveal that novel gene rearrangement that result in sequence and structural diversity in has a bearing on OR and GPCR function and evolution

Introduction to protein folding for physicists

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons underlying the protein folding problem enormous relevance and summarize the present-day status of the methods aimed to solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly implicit) definition. Finally, we review the 'special flavor' of statistical mechanics that is typically used to study the astronomically large phase spaces of macromolecules. Throughout the whole work, much material that is found scattered in the literature has been put together here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co

Evaluation of protein surface roughness index using its heat denatured aggregates

Recent research works on potential of different protein surface describing parameters to predict protein surface properties gained significance for its possible implication in extracting clues on protein's functional site. In this direction, Surface Roughness Index, a surface topological parameter, showed its potential to predict SCOP-family of protein. The present work stands on the foundation of these works where a semi-empirical method for evaluation of Surface Roughness Index directly from its heat denatured protein aggregates (HDPA) was designed and demonstrated successfully. The steps followed consist, the extraction of a feature, Intensity Level Multifractal Dimension (ILMFD) from the microscopic images of HDPA, followed by the mapping of ILMFD into Surface Roughness Index (SRI) through recurrent backpropagation network (RBPN). Finally SRI for a particular protein was predicted by clustering of decisions obtained through feeding of multiple data into RBPN, to obtain general tendency of decision, as well as to discard the noisy dataset. The cluster centre of the largest cluster was found to be the best match for mapping of Surface Roughness Index of each protein in our study. The semi-empirical approach adopted in this paper, shows a way to evaluate protein's surface property without depending on its already evaluated structure

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background: Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results: We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/ fDETECT/. Conclusions: fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-ofthe-art tools and is especially suitable for the analysis of large protein sets

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl