Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

Abstract

The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic Indices (sto-TPGIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining of molecular and macromolecular chemical structures within large databases. In the work described here, we review and comment on the several applications of MARCH-INSIDE to the Medicinal Chemistry of Antimicrobial agents as well as their molecular targets. First we revised the use of classic sto-TIs to predict antiparasite compounds for the treatment of Fascioliasis. Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict new antibacterial, antiviral and anti-coccidial compounds. After that, we review multi-target sto-TIs (mt-sto-TIs), which unifying QSAR models predicting antifungal, antibacterial, or anti-parasite drugs with multiple targets (microbial species). We also discussed the uses of mt-sto-TIs to assemble drug-drug similarity Complex Networks of antimicrobial compounds based on molecular structure. Last, we review the use of MARCH-INSIDE to generate macromolecular TIs and TPGIs for proteins or RNA targets for antimicrobial drugs.

Similarity/dissimilarity studies of protein sequences based on a new 2D graphical representation

“…In recent reviews, González-Díaz et al discussed the applications of all these and other graphs. [38][39][40] The amino acid sequence of a protein is the key to understanding its structure and function in the cell. However, the first graphical representation of proteins emerged only very recently and few representations were outlined.…”

S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal Sciences

“…These TIs are usually derived from node-node adjacency or other types of matrices associated to the CN [7,8]. Even though many TIs have been described only for molecular graphs of type a) many of them have been extended to be used in all types of CN [5,[9][10][11][12][13]. In any case, the development of new types of CN and graph representations or new TIs to describe them is an emerging field of science.…”

Rational design of peptide vaccines against the Zika virus through sequence descriptors: Techniques and Problems

“…Developing upon initial work on DNA sequences, these descriptors have been used to characterize genes to genomes [5] and proteins [6] to proteomes [7], and have seen applications in many areas. While Nandy [8][9][10], Nandy and Nandy [11] and Larionov et al [12] have used 2D graphical systems to determine DNA systematics, Liao et al [13,14] used the novel techniques for alignment-free phylogeny to determine sequence ancestry, Wiesner and Wiesnerova [15] found the new methodology giving better insights into germ-plasm identifiers, Gonzalez-Diaz and his group presented several papers using the concept for alignment-free prediction of polygalacturonases [16], alternative "in silico" technique for chemical research in toxicology [17] and predicting antimicrobial drugs and targets [18], Nandy et al [19] were able to model influenza hemagglutinin and neuraminidase interdependence which provided predictability to new possible viral assortments [20]. The technique of numerical characterization was extended to proteins initially by Randic et al [21] and led to several approaches being proposed [6,22,23], analyzing phylogenetic relationships between protein families [24], hydropathy profiles of amino acids [25] and others.…”