BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

MOTIVATION: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. RESULTS: We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. AVAILABILITY AND IMPLEMENTATION: BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.

BACKGROUND: Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. RESULTS: In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. CONCLUSIONS: Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Solid-Support Directional (SSD) RNA-Seq as a Companion Method to CLIP-Seq.

CLIP-Seq (Deep Sequencing after in vivo Crosslinking and Immunoprecipitation, HITS-CLIP) has emerged as a key method for the study of RNA-binding proteins (RBPs), as it can scrutinize the RNAs bound by an RBP in vivo, with minimum manipulation of biological samples. CLIP-Seq is best used to reveal changes of the RNA cargo of an RBP and differences on binding patterns of the bound RNAs in living cells in different genetic backgrounds or after experimental treatment, rather than simply identifying RNA species. It is therefore crucial that a reference of the steady state levels of the RNAs present in the samples used for the CLIP-Seq experiment is included in the bioinformatic analysis. A simple directional RNA-Seq method was developed that uses the same oligonucleotides and the same PCR amplification steps as our CLIP-Seq method, which therefore can be analyzed using the same bioinformatic pipeline as the CLIP-Seq data. This greatly simplifies and streamlines the analysis process, and at the same time reduces the chances of protocol-specific artifacts and biases interfering with data interpretation. Some considerations on ways to integrate CLIP-Seq and RNA-Seq analyses are also provided herein.

Effect of complement component 5 polymorphisms on mastitis resistance in Egyptian buffalo and cattle.

Mastitis is one of the costliest diseases affecting the world's dairy industry. The important contribution of complement Component 5 (C5) to phagocytosis, which plays a major role in the defence of the bovine mammary gland against infection, makes this component of innate immunity a potential contributor in defending udder against mastitis. The objectives of this study were to sequence and analyse the whole coding region of the C5 gene in Egyptian buffalo and cattle, to detect any nucleotide variations (polymorphisms) and to investigate their associations with milk somatic cell score (SCS) as an indicator of mastitis in dairy animals. We sequenced a buffalo C5 cDNA fragment of 5336 bp (KP221293) and a cattle C5 cDNA fragment of 5303 bp (KP221294), which included the whole coding region and 3-UTR. Buffalo and cattle C5 cDNA shared sequence identity of 99%. The predicted complement C5 proteins consist of 1677 amino acid residues in both animals, one amino acid less than in humans and three amino acids more than in mouse C5 protein. Comparing cDNA sequences of different animals revealed nine novel SNPs in buffalo and seven SNPs in cattle, with two of them being novel. The association analysis revealed that five SNPs in buffalo are highly associated with SCS; indicating the contribution of complement C5 variants in buffalo mastitis resistance. No significant associations were detected between C5 variants and SCS in cattle. This is the first report about C5 variants in buffalo and its association with SCS.