Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies.

Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.