Structure-based modeling of protein: DNA specificity

Molecular characterization, putative structure and function, and expression profile of OAS1 gene in the endometrium of goats (Capra hircus)

An interferon-inducible gene, 2'-5'-oligoadenylate synthetase-1 (OAS1), plays an essential role in uterine receptivity and conceptus development by controlling cell growth and differentiation in addition to anti-viral activities. As OAS1 gene has not yet been studied in caprine (cp), so present study was designed with the aim to amplify, sequence, characterize and in-silico analyze the coding sequence of the cpOAS1. Further, expression profile of cpOAS1 was performed by quantitative real-time PCR and western blot in the endometrium of pregnant and cyclic does. An 890 bp fragment of the cpOAS1 was amplified and sequenced. Nucleotide and deduced amino acid sequences revealed 99.6-72.3% identities with that of ruminants and non-ruminants. A constructed phylogenetic tree revealed that Ovis aries and Capra hircus differ from large ungulates. Various post-translational modifications (PTMs), 21 phosphorylation, two sumoylation, eight cysteines and 14 immunogenic sites were found in the cpOAS1. The domain, OAS1_C, is found in the cpOAS1 which carries anti-viral enzymatic activity, cell growth, and differentiation. Among the interacted proteins with cpOAS1, Mx1 and ISG17 well-known proteins are found that have anti-viral activity and play an important role during early pregnancy in ruminants. CpOAS1 protein (42/46 kDa and/or 69/71 kDa) was detected in the endometrium of pregnant and cyclic does. Both cpOAS1 mRNA and protein were expressed maximally (P<0.05) in the endometrium during pregnancy as compared to cyclic does. In conclusion, the cpOAS1 sequence is almost similar in structure and probably in function also to other species along with its higher expression during early pregnancy.

Identification of key genes involved in calcific aortic valve disease based on integrated bioinformatics analysis

The calcific aortic valve disease (CAVD) develops as an aortic valve sclerosis and progresses to an advanced form of stenosis. In many biological fields, bioinformatics becomes a fundamental component. The key mechanisms involved in CAVD are discovered with the use of bioinformatics to investigate gene function and pathways. We downloaded the original data (GSE51472) from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). After standardization, 2978 differentially expressed genes (DEGs) were identified from the data sets GSE51472 containing samples from normal, calcified, and sclerotic aortic valves. Analysis of DEGs based on the series test of clusters (STCs) revealed the two most significant patterns. Based on the result of the STC, the functional enrichment analysis of gene ontology (GO) was conducted to investigate the molecular function (MF), biological process (BP), and cell compound (CC) of the DEGs. With a p value of 0.01, DEGs associated with "chronic inflammation," "T-cell receptor complexes," and "antigen binding" had the highest significance within BP, CC, and MF. DEG enrichment in signaling pathways was analyzed using KEGG pathway enrichment. Using a p < 0.05 level of significance, the most enriched biological pathways related to CAVD were "Chemokine signaling pathway," "Cytokine-cytokine receptor interaction," "Tuberculosis," "PI3K-Akt signaling pathway," and "Transcriptional misregulation in cancer." Finally, the construction of gene co-expression networks and pathway networks illustrated the pathogensis of CAVD. TLR2, CD86, and TYROBP were identified as hub genes for the development of CAVD. Moreover, "MAPK signaling pathway," "Apoptosis," and "Pathways in cancer" were regarded as the core pathways among the samples of normal, sclerotic and calcified aortic valve samples.

BMT: Bioinformatics mini toolbox for comprehensive DNA and protein analysis

Background Bioinformatics tools are of great significance and are used in different spheres of life sciences. There are wide variety of tools available to perform primary analysis of DNA and protein but most of them are available on different platforms and many remain undetected. Accessing these tools separately to perform individual task is uneconomical and inefficient. Objective Our aim is to bring different bioinformatics models on a single platform to ameliorate scientific research. Hence, our objective is to make a tool for comprehensive DNA and protein analysis. Methods To develop a reliable, straight-forward and standalone desktop application we used state of the art python packages and libraries. Bioinformatics Mini Toolbox (BMT) is combination of seven tools including FastqTrimmer, Gene Prediction, DNA Analysis, Translation, Protein analysis and Pairwise and Multiple alignment. Results FastqTrimmer assists in quality assurance of NGS data. Gene prediction predicts the genes by homology from novel genome on the basis of reference sequence. Protein analysis and DNA analysis calculates physiochemical properties of nucleotide and protein sequences, respectively. Translation translates the DNA sequence into six open reading frames. Pairwise alignment performs pairwise global and local alignment of DNA and protein sequences on the basis or multiple matrices. Multiple alignment aligns multiple sequences and generates a phylogenetic tree. Conclusion We developed a tool for comprehensive DNA and protein analysis. The link to download BMT is https://github.com/nasiriqbal012/BMT_SETUP.git.

Optimization of Protein Thermostability and Exploitation of Recognition Behavior to Engineer Altered Protein-DNA Recognition

The redesign of a macromolecular binding interface and corresponding alteration of recognition specificity is a challenging endeavor that remains recalcitrant to computational approaches. This is particularly true for the redesign of DNA binding specificity, which is highly dependent upon bending, hydrogen bonds, electrostatic contacts, and the presence of solvent and counterions throughout the molecular interface. Thus, redesign of protein-DNA binding specificity generally requires iterative rounds of amino acid randomization coupled to selections. Here, we describe the importance of scaffold thermostability for protein engineering, coupled with a strategy that exploits the protein's specificity profile, to redesign the specificity of a pair of meganucleases toward three separate genomic targets. We determine and describe a series of changes in protein sequence, stability, structure, and activity that accumulate during the engineering process, culminating in fully retargeted endonucleases.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

DNA-protein interaction: identification, prediction and data analysis

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

Transcription factor-DNA binding: beyond binding site motifs

Sequence-specific transcription factors (TFs) regulate gene expression by binding to cis-regulatory elements in promoter and enhancer DNA. While studies of TF-DNA binding have focused on TFs' intrinsic preferences for primary nucleotide sequence motifs, recent studies have elucidated additional layers of complexity that modulate TF-DNA binding. In this review, we discuss technological developments for identifying TF binding preferences and highlight recent discoveries that elaborate how TF interactions, local DNA structure, and genomic features influence TF-DNA binding. We highlight novel approaches for characterizing functional binding site motifs that promise to inform our understanding of how TF binding controls gene expression and ultimately contributes to phenotype.