Genome-wide codon usage profiling of ocular infective Chlamydia trachomatis serovars and drug target identification

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Analysis of codon usage bias in mitochondrial CO gene among platyhelminthes

The phenomenon of non-uniform usage of the synonymous codons, where some codons are given more preference to others, is known as codon usage bias (CUB). CUB is known to be determined by two major evolutionary forces i.e. mutation pressure and selection. We used various approaches to understand the codon usage pattern in mitochondrial CO (MT-CO) genes involved in complex IV of the respiratory chain (RC) as no work was reported yet. Our present study revealed that CUB was relatively high and the coding sequences were rich in A and T. Correspondence analysis further indicated that A/T compositional properties under mutational pressure might be affecting the codon usage pattern and was different in different classes for MT-CO gene. A highly significant correlation between A% and A3%, T% and T3%, G% and G3%, C% and C3%, GC% and GC3% in all the classes indicated that compositional constraints under mutational pressure and natural selection might affect the CUB. Neutrality plot indicated that both natural selection and mutational bias affected the CUB, where, natural selection played the major role as compared to mutational pressure.

Genome-wide analysis of the synonymous codon usage pattern of Streptococcus suis

Streptococcus suis (S. suis) is a gram-positive coccus that causes disease in humans and animals. The codon usage pattern of bacteria reveals a range of evolutionary changes that assist them to enhance tolerance to environments. To better understand the genetic features during the evolution of S. suis, we performed codon usage analysis. Nine pathogenic strains of different serotypes and different geographical distribution were analyzed to better understand the differences in their evolutionary process. Nucleotide compositions and relative synonymous codon usage (RSCU) analysis revealed that A/T-ending codons are dominant in S. suis. Neutrality analysis, correspondence analysis and ENC-plot results revealed that natural selection is the predominant element prompting codon usage. Cluster analysis based on RSCU was roughly consistent with the dendrogram rooted genomic BLAST analysis. Comparison of synonymous codon usage pattern between S. suis and susceptible hosts (H. sapiens and S. scrofa) revealed that the codon usage of S. suis is separated from the synonymous codon usage of susceptible hosts. The CAI values implied that S. suis includes a series of predicted highly expressed coding sequences contained in metabolism and transcriptional regulation, revealing the necessity of this pathogen to deal with various environmental conditions. The study of codon usage in S. suis may provide evidence involving the molecular evolution of bacteria and a better understanding of evolutionary relationships between S. suis and its corresponding hosts.

Structure-based drug target prioritisation and rational drug design for targeting Chlamydia trachomatis eye infections

Chlamydia trachomatis (C.t) is a major causative of infectious blindness in world. It is a real challenge to combat Chlamydial infection as it is an intracellular pathogen. Hence, it is essential to determine the most potential targets of C.t in order to inhibit or suppress its virulence during its infectious phase. Thus, in this study, the highly expressed-cum-most essential genes reported through our earlier study were reprioritized by structure-based comparative binding site analysis with host proteome. Therefore, computational approaches involving molecular modelling, large-scale binding site prediction and comparison, molecular dynamics simulation studies were performed to narrow down the most potential targets. Furthermore, high-throughput virtual screening and ADMETox were also performed to identify potential hits that shall efficiently inhibit the prioritised targets. Hence, by this study we report Pyruvoyl-dependent arginine decarboxylase (PvlArgDC), DNA-repair protein (RecO) and porin (outer membrane protein) as the most viable targets of C.t which can be potentially targeted by compounds, NSC_13086, MFCD00276409, MFCD05662003, respectively. AbbreviationsC.tChlamydia trachomatisSTDSexually transmitted diseaseHTVSHigh-throughput virtual screeningADMEToxAbsorption, Distribution, Metabolism, Excretion and ToxicityPMPocketMatchMDMolecular Dynamics simulationSPStandard precisionXPExtra precisionMMGBSAMolecular mechanics energies combined with generalised Born and surface area continuum solvationOMPOuter membrane proteinPvlArgDCPyruvoyl-dependent arginine decarboxylaseRecORecombination protein O.Communicated by Ramaswamy H. Sarma.

PocketPipe: A computational pipeline for integratedPocketome prediction and comparison

Functional characterisation of proteins often depends on specific interactions with other molecules. In the drug discovery scenario, the ability of a protein to bind with drug-like molecule with a high affinity is referred as druggability. Deciphering such druggable binding pockets on proteins plays an important role in structure-based drug designing studies. Moreover, availability of plethora of structural data poses a need automated pipelines which can efficiently integrate robust algorithms towards large-scale pocket identification and comparison. These pipelines have direct applicability on off-target analysis, drug repurposing and structural prioritization of drug targets in pathogenic microbes. However, currently there is a paucity of such efficient pipelines. Hence, by this study a highly optimized shell script based pipeline (PocketPipe) has been developed with seamless integration of robust algorithms namely, P2Rank (predicts binding sites based on machine learning) and PocketMatch-v2.1 (compares binding pockets by residue-based method), for pocketome generation and comparison, respectively. The process of input workflow and various steps carried out by PocketPipe and the output results are well documented in the operating manual. On execution, the pipeline features seamless operability, high scalability, dynamic file handling and results parsing. PocketPipe is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/PocketPipe

Identification of potential drugs targeting L,L-diaminopimelate aminotransferase of Chlamydia trachomatis: An integrative pharmacoinformatics approach

Chlamydia trachomatis (C.t) is a gram-negative obligate intracellular bacteria, which is a major causative of infectious blindness and sexually transmitted diseases. A surge in multidrug resistance among chlamydial species has posed a challenge to adopt alternative drug targeting strategies. Recently, in C.t, L,L-diaminopimelate aminotransferase (CtDAP-AT) is proven to be a potential drug target due its essential role in cell survival and host nonspecificity. Hence, in this study, a multilevel precision-based virtual screening of CtDAP-AT was performed to identify potential inhibitors, wherein, an integrative stringent scoring and filtration were performed by coupling, glide docking score, binding free energy, ADMET (absorption, distribution, metabolism, and excretion, toxicity) prediction, density functional theory (quantum mechanics), and molecular dynamics simulation (molecular mechanics). On cumulative analysis, NSC_5485 (1,3-bis((7-chloro-4-quinolinyl)amino)-2-propanol) was found to be the most potential lead, as it showed higher order significance in terms of binding affinity, bonded interactions, favorable ADMET, chemical reactivity, and greater stabilization during complex formation. This is the first report on prioritization of small molecules from National Cancer Institute (NCI) and Maybridge data sets (341 519 compounds) towards targeting CtDAP-AT. Thus, the proposed compound shall aid in effective combating of a broad spectrum of C.t infections as it surpassed all the levels of prioritization.

Comparative analysis of expansin gene codon usage patterns among eight plant species

Expansins are essential components of plant cell wall and play an important role in plant growth, development, and stress resistance via loosening function. To understand the codon usage pattern of expansin genes, we gained the sequence data of expansin genes from eight plant species. Statistics analysis showed obvious codon characteristics between monocot and dicot plants. Comparably, expansin genes in monocot plants had really higher GC content, more high-frequency codons, and more optimal codons than that in dicot plants. Several monocot plants performed somehow as dicot plants in a few characters. Codon information of expansin genes might contribute to the understanding of the relationship and evolution clues between monocot and dicot plants. It further gained insight into the improvement of the gene expression and roles.