Computational screening for new inhibitors of<i>M. tuberculosis</i>mycolyltransferases antigen 85 group of proteins as potential drug targets

Gahoi, Shachi; Mandal, Rahul Shubhra; Ivanisenko, Nikita V.; Shrivastava, Priyanka; Jain, Sakshi; Singh, Ashish Kumar; Raghunandanan, Muthukurrusi Varieth; Kanchan, Swarna; Taneja, Bhupesh; Mandal, Chhabinath; Иванисенко, В. А.; Kumar, Anil; Kumar, Ram; Ramachandran, Srinivasan

doi:10.1080/07391102.2012.691343

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…Perhaps mycobacteria have evolved such strong force-dependence in Ag85 to help the bacteria resist physical stress during host colonization. Various small molecules that bind to Ag85 and inhibit their mycolyltransferase activity have been identified and the Ag85 complex is considered as a potential drug target [73][74][75][76][77][78][79][80]. Ensuing studies should investigate the potential of Ag85 inhibitors in the Ag85-Fn interaction.…”

Section: Discussionmentioning

confidence: 99%

Mechanical Forces between Mycobacterial Antigen 85 Complex and Fibronectin

Viljoen

Alsteens

Dufrêne

2020

Cells

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Adhesion to extracellular matrix proteins is an important first step in host invasion, employed by many bacterial pathogens. In mycobacteria, the secreted Ag85 complex proteins, involved in the synthesis of the cell envelope, are known to bind to fibronectin (Fn) through molecular forces that are currently unknown. In this study, single-molecule force spectroscopy is used to study the strength, kinetics and thermodynamics of the Ag85-Fn interaction, focusing on the multidrug-resistant Mycobacterium abscessus species. Single Ag85 proteins bind Fn with a strength of~75 pN under moderate tensile loading, which compares well with the forces reported for other Fn-binding proteins. The binding specificity is demonstrated by using free Ag85 and Fn peptides with active binding sequences. The Ag85-Fn rupture force increases with mechanical stress (i.e., loading rate) according to the Friddle-Noy-de Yoreo theory. From this model, we extract thermodynamic parameters that are in good agreement with previous affinity determinations by surface plasmon resonance. Strong bonds (up to~500 pN) are observed under high tensile loading, which may favor strong mycobacterial attachment in the lung where cells are exposed to high shear stress or during hematogenous spread which leads to a disseminated infection. Our results provide new insight into the pleiotropic functions of an important mycobacterial virulence factor that acts as a stress-sensitive adhesin.

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Section: Discussionmentioning

confidence: 99%

Mechanical Forces between Mycobacterial Antigen 85 Complex and Fibronectin

Viljoen

Alsteens

Dufrêne

2020

Cells

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“…To further validate the interaction stability, we performed 20 ns molecular dynamic (MD) simulation of the AphB-ligand complex in solution using GROMACS4.5 software56. The parameters for the simulation were set as per our previous publication57. The root mean square deviation (RMSD) and the H-bond interaction plots were generated from the trajectory output file using Microsoft Excel program.…”

Section: Methodsmentioning

confidence: 99%

Ribavirin suppresses bacterial virulence by targeting LysR-type transcriptional regulators

Mandal

Sinha

et al. 2016

Sci Rep

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Targeting bacterial virulence mechanisms without compromising bacterial growth is a promising strategy to prevent drug resistance. LysR-type transcriptional regulators (LTTRs) possess structural conservation across bacterial species and regulate virulence in numerous pathogens, making them attractive targets for antimicrobial agents. We targeted AphB, a Vibrio cholerae LTTR, which regulates the expression of genes encoding cholera toxin and toxin-co-regulated pilus for inhibitor designing. Since AphB ligand is unknown, we followed a molecular fragment-based approach for ligand designing using FDA-approved drugs and subsequent screen to identify molecules that exhibited high-affinity binding to AphB ligand-binding pocket. Among the identified compounds, ribavirin, an anti-viral drug, antagonized AphB functions. Ribavirin perturbed Vibrio cholerae pathogenesis in animal models. The inhibitory effects of the drug was limited to the bacteria expressing wild type AphB, but not its constitutively active mutant (AphBN100E), which represents the ligand-bound state, suggesting that ribavirin binds to the active site of AphB to exert its inhibitory role and there exists no AphB-independent mechanism of its action. Similarly, ribavirin suppressed the functions of Salmonella Typhi LTTR Hrg, indicating its broad spectrum efficacy. Moreover, ribavirin did not affect the bacterial viability in culture. This study cites an example of drug repurposing for anti-infective therapy.

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“…cruzi (the parasite that causes Chagas disease) was discovered by docking into a comparative model to dihydrofolate reductase in L. major, a related parasite (Zuccotto et al 2001). Since the crystal/ NMR structure of various drug targets are not available so far, comparative models of drug targets could also be used for computational screening of new inhibitors for Mycobacterium tuberculosis drug target proteins (Gahoi et al 2013).…”

Section: Caspmentioning

confidence: 99%

Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach

Kesheri

Kanchan²,

Chowdhury³

et al. 2014

Complex System Modelling and Control Through Intelligent Soft Computations

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Correct prediction of secondary and tertiary structure of proteins is one of the major challenges in bioinformatics/computational biological research. Predicting the correct secondary structure is the key to predict a good/satisfactory tertiary structure of the protein which not only helps in prediction of protein function but also in prediction of sub-cellular localization. This chapter aims to explain the different algorithms and methodologies, which are used in secondary structure prediction. Similarly, tertiary structure prediction has also emerged as one of developing areas of bioinformatics/computational biological research owing to the large gap between the available number of protein sequences and the known experimentally solved structures. Because of time and cost intensive experimental methods, experimentally determined structures are not available for vast majority of the available protein sequences present in public domain databases. The primary aim of this chapter is to offer a detailed conceptual insight to the algorithms used for protein secondary and tertiary structure prediction. This chapter systematically illustrates flowchart for selecting the most accurate prediction algorithm among different categories for the target sequence against three categories of tertiary structure prediction methods. Out of the three methods, homology modeling which is considered as most reliable method is discussed in detail followed by strengths and limitations for each of these categories. This chapter also explains different practical and conceptual problems, obstructing the high accuracy of the protein structure in each of the steps for all the three methods of tertiary structure prediction. The popular hybrid methodologies which further club together a number of features such as structural alignments, solvent accessibility and secondary structure information are also discussed. Moreover, this chapter elucidates about the Meta-servers that generate consensus result from many servers to build a protein model of high accuracy. Lastly, scope for further research in order to bridge existing gaps and for developing better secondary and tertiary structure prediction algorithms is also highlighted.

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Computational screening for new inhibitors ofM. tuberculosismycolyltransferases antigen 85 group of proteins as potential drug targets

Cited by 13 publications

References 25 publications

Mechanical Forces between Mycobacterial Antigen 85 Complex and Fibronectin

Mechanical Forces between Mycobacterial Antigen 85 Complex and Fibronectin

Ribavirin suppresses bacterial virulence by targeting LysR-type transcriptional regulators

Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach

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