Computational identification and analysis of noncoding RNAs - Unearthing the buried treasures in the genome

The central dogma of molecular biology states that the genetic information flows from DNA to RNA to protein. This dogma has exerted a substantial influence on our understanding of the genetic activities in the cells. Under this influence, the prevailing assumption until the recent past was that genes are basically repositories for protein coding information, and proteins are responsible for most of the important biological functions in all cells. In the meanwhile, the importance of RNAs has remained rather obscure, and RNA was mainly viewed as a passive intermediary that bridges the gap between DNA and protein. Except for classic examples such as tRNAs (transfer RNAs) and rRNAs (ribosomal RNAs), functional noncoding RNAs were considered to be rare. However, this view has experienced a dramatic change during the last decade, as systematic screening of various genomes identified myriads of noncoding RNAs (ncRNAs), which are RNA molecules that function without being translated into proteins [11], [40]. It has been realized that many ncRNAs play important roles in various biological processes. As RNAs can interact with other RNAs and DNAs in a sequence-specific manner, they are especially useful in tasks that require highly specific nucleotide recognition [11]. Good examples are the miRNAs (microRNAs) that regulate gene expression by targeting mRNAs (messenger RNAs) [4], [20], and the siRNAs (small interfering RNAs) that take part in the RNAi (RNA interference) pathways for gene silencing [29], [30]. Recent developments show that ncRNAs are extensively involved in many gene regulatory mechanisms [14], [17]. The roles of ncRNAs known to this day are truly diverse. These include transcription and translation control, chromosome replication, RNA processing and modification, and protein degradation and translocation [40], just to name a few. These days, it is even claimed that ncRNAs dominate the genomic output of the higher organisms such as mammals, and it is being suggested that the greater portion of their genome (which does not encode proteins) is dedicated to the control and regulation of cell development [27]. As more and more evidence piles up, greater attention is paid to ncRNAs, which have been neglected for a long time. Researchers began to realize that the vast majority of the genome that was regarded as “junk,” mainly because it was not well understood, may indeed hold the key for the best kept secrets in life, such as the mechanism of alternative splicing, the control of epigenetic variations and so forth [27]. The complete range and extent of the role of ncRNAs are not so obvious at this point, but it is certain that a comprehensive understanding of cellular processes is not possible without understanding the functions of ncRNAs [47]

Understanding diversity of human innate immunity receptors: analysis of surface features of leucine-rich repeat domains in NLRs and TLRs.

BackgroundThe human innate immune system uses a system of extracellular Toll-like receptors (TLRs) and intracellular Nod-like receptors (NLRs) to match the appropriate level of immune response to the level of threat from the current environment. Almost all NLRs and TLRs have a domain consisting of multiple leucine-rich repeats (LRRs), which is believed to be involved in ligand binding. LRRs, found also in thousands of other proteins, form a well-defined "horseshoe"-shaped structural scaffold that can be used for a variety of functions, from binding specific ligands to performing a general structural role. The specific functional roles of LRR domains in NLRs and TLRs are thus defined by their detailed surface features. While experimental crystal structures of four human TLRs have been solved, no structure data are available for NLRs.ResultsWe report a quantitative, comparative analysis of the surface features of LRR domains in human NLRs and TLRs, using predicted three-dimensional structures for NLRs. Specifically, we calculated amino acid hydrophobicity, charge, and glycosylation distributions within LRR domain surfaces and assessed their similarity by clustering. Despite differences in structural and genomic organization, comparison of LRR surface features in NLRs and TLRs allowed us to hypothesize about their possible functional similarities. We find agreement between predicted surface similarities and similar functional roles in NLRs and TLRs with known agonists, and suggest possible binding partners for uncharacterized NLRs.ConclusionDespite its low resolution, our approach permits comparison of molecular surface features in the absence of crystal structure data. Our results illustrate diversity of surface features of innate immunity receptors and provide hints for function of NLRs whose specific role in innate immunity is yet unknown

Detection of the TCDD binding-fingerprint within the Ah receptor ligand binding domain by structurally driven mutagenesis and functional analysis

The aryl hydrocarbon receptor (AhR) is a ligand-dependent, basic helix-loop-helix Per-Arnt-Sim (PAS)-containing transcription factor that can bind and be activated by structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Our previous three-dimensional homology model of the mouse AhR (mAhR) PAS B ligand binding domain allowed identification of the binding site and its experimental validation. We have extended this analysis by conducting comparative structural modeling studies of the ligand binding domains of six additional highaffinity mammalian AhRs. These results, coupled with site-directed mutagenesis and AhR functional analysis, have allowed detection of the "TCDD binding-fingerprint" of conserved residues within the ligand binding cavity necessary for high-affinity TCDD binding and TCDD-dependent AhR transformation DNA binding. The essential role of selected residues was further evaluated using molecular docking simulations of TCDD with both wild-type and mutant mAhRs. Taken together, our results dramatically improve our understanding of the molecular determinants of TCDD binding and provide a basis for future studies directed toward rationalizing the observed species differences in AhR sensitivity to TCDD and understanding the mechanistic basis for the dramatic diversity in AhR ligand structure. © 2009 American Chemical Society

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Comment: 28 pages, 13 figure

An Alternative Model of Amino Acid Replacement

The observed correlations between pairs of homologous protein sequences are typically explained in terms of a Markovian dynamic of amino acid substitution. This model assumes that every location on the protein sequence has the same background distribution of amino acids, an assumption that is incompatible with the observed heterogeneity of protein amino acid profiles and with the success of profile multiple sequence alignment. We propose an alternative model of amino acid replacement during protein evolution based upon the assumption that the variation of the amino acid background distribution from one residue to the next is sufficient to explain the observed sequence correlations of homologs. The resulting dynamical model of independent replacements drawn from heterogeneous backgrounds is simple and consistent, and provides a unified homology match score for sequence-sequence, sequence-profile and profile-profile alignment.Comment: Minor improvements. Added figure and reference