Johny Ijaq

Bio: Johny Ijaq is an academic researcher from Osmania University. The author has contributed to research in topics: Proteome & UniProt. The author has an hindex of 4, co-authored 9 publications receiving 90 citations.

Annotation and curation of uncharacterized proteins- challenges

Abstract: Hypothetical Proteins are the proteins that are predicted to be expressed from an open reading frame (ORF), constituting a substantial fraction of proteomes in both prokaryotes and eukaryotes. Genome projects have led to the identification of many therapeutic targets, the putative function of the protein and their interactions. In this review we have enlisted various methods. Annotation linked to structural and functional prediction of hypothetical proteins assist in the discovery of new structures and functions serving as markers and pharmacological targets for drug designing, discovery and screening. Mass spectrometry is an analytical technique for validating protein characterisation. Matrix-assisted laser desorption ionization–mass spectrometry (MALDI-MS) is an efficient analytical method. Microarrays and Protein expression profiles help understanding the biological systems through a systems-wide study of proteins and their interactions with other proteins and non-proteinaceous molecules to control complex processes in cells and tissues and even whole organism. Next generation sequencing technology accelerates multiple areas of genomics research.

Protocol for Molecular Dynamics Simulations of Proteins

Abstract: [Abstract] Molecular dynamics (MD) simulations have become one of the most important tools in understanding the behavior of bio-molecules on nanosecond to microsecond time scales. In this protocol, we provide a general approach and standard setup protocol for MD simulations by using the Gromacs MD suite.

A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.

Abstract: Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs. In our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67). With the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.

Genome-Wide Mining, Characterization and Development of miRNA-SSRs in Arabidopsis thaliana

Abstract: Simple Sequence Repeats (SSRs), also known as microsatellites are short tandem repeats of DNA sequences that are 1-6 bp long. In plants, SSRs serve as a source of important class of molecular markers because of their hypervariabile and co-dominant nature, making them useful both for the genetic studies and marker-assisted breeding. The SSRs are widespread throughout the genome of an organism, so that a large number of SSR datasets are available, most of them from either protein-coding regions or untranslated regions. It is only recently, that their occurrence within microRNAs (miRNA) genes has received attention. As is widely known, miRNA themselves are a class of non-coding RNAs (ncRNAs) with varying length of 19-22 nucleotides (nts), which play an important role in regulating gene expression in plants under different biotic and abiotic stresses. In this communication, we describe the results of a study, where miRNA-SSRs in full length pre-miRNA sequences of Arabidopsis thaliana were mined. The sequences were retrieved by annotations available at EnsemblPlants using BatchPrimer3 server with miRNA-SSR flanking primers found to be well distributed. Our analysis shows that miRNA-SSRs are relatively rare in protein-coding regions but abundant in non-coding region. All the observed 147 di-, tri-, tetra-, penta- and hexanucleotide SSRs were located in non-coding regions of all the 5 chromosomes of A. thaliana. While we confirm that miRNA-SSRs were commonly spread across the full length pre-miRNAs, we envisage that such studies would allow us to identify newly discovered markers for breeding studies.

Systems Challenges of Hepatic Carcinomas: A Review

Abstract: Hepatocellular Carcinoma (HCC) is ubiquitous in its prevalence in most of the developing countries. In the era of systems biology, multi-omics has evinced an extensive approach to define the underlying mechanism of disease progression. HCC is a multifactorial disease and the investigation of progression of liver cirrhosis becomes much extensive with cultivating omics approaches. We have performed a comprehensive review about such challenges in multi-omics approaches that are concerned to identify the immunological, genetics and epidemiological factors associated with HCC.

Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches.

Abstract: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel corona virus that causes corona virus disease 2019 (COVID-19). The COVID-19 rapidly spread across the nations with high mortality rate even as very little is known to contain the virus at present. In the current study, we report novel natural metabolites namely, ursolic acid, carvacrol and oleanolic acid as the potential inhibitors against main protease (Mpro) of COVID-19 by using integrated molecular modeling approaches. From a combination of molecular docking and molecular dynamic (MD) simulations, we found three ligands bound to protease during 50 ns of MD simulations. Furthermore, the molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations showed that these chemical molecules have stable and favourable energies causing strong binding with binding site of Mpro protein. All these three molecules, namely, ursolic acid, carvacrol and oleanolic acid, have passed the ADME (Absorption, Distribution, Metabolism, and Excretion) property as well as Lipinski's rule of five. The study provides a basic foundation and suggests that the three phytochemicals, viz. ursolic acid, carvacrol and oleanolic acid could serve as potential inhibitors in regulating the Mpro protein's function and controlling viral replication. Communicated by Ramaswamy H. Sarma.

Marine natural compounds as potents inhibitors against the main protease of SARS-CoV-2-a molecular dynamic study.

Abstract: Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA (ssRNA) virus, responsible for severe acute respiratory disease (COVID-19). A large number of natural compounds ...

In silico investigation of phytoconstituents from Indian medicinal herb ' Tinospora cordifolia (giloy)' against SARS-CoV-2 (COVID-19) by molecular dynamics approach.

Abstract: The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different pos...

An assessment of genome annotation coverage across the bacterial tree of life

Abstract: Although gene-finding in bacterial genomes is relatively straightforward, the automated assignment of gene function is still challenging, resulting in a vast quantity of hypothetical sequences of unknown function. But how prevalent are hypothetical sequences across bacteria, what proportion of genes in different bacterial genomes remain unannotated, and what factors affect annotation completeness? To address these questions, we surveyed over 27 000 bacterial genomes from the Genome Taxonomy Database, and measured genome annotation completeness as a function of annotation method, taxonomy, genome size, 'research bias' and publication date. Our analysis revealed that 52 and 79 % of the average bacterial proteome could be functionally annotated based on protein and domain-based homology searches, respectively. Annotation coverage using protein homology search varied significantly from as low as 14 % in some species to as high as 98 % in others. We found that taxonomy is a major factor influencing annotation completeness, with distinct trends observed across the microbial tree (e.g. the lowest level of completeness was found in the Patescibacteria lineage). Most lineages showed a significant association between genome size and annotation incompleteness, likely reflecting a greater degree of uncharacterized sequences in 'accessory' proteomes than in 'core' proteomes. Finally, research bias, as measured by publication volume, was also an important factor influencing genome annotation completeness, with early model organisms showing high completeness levels relative to other genomes in their own taxonomic lineages. Our work highlights the disparity in annotation coverage across the bacterial tree of life and emphasizes a need for more experimental characterization of accessory proteomes as well as understudied lineages.