Molecular modeling, simulation and virtual screening of MurD ligase protein from Salmonella typhimurium LT2

Author(s):  
Himanshu Bhusan Samal ◽  
Jugal Kishore Das ◽  
Rajani Kanta Mahapatra ◽  
Mrutyunjay Suar
2014 ◽  
Vol 343 ◽  
pp. 113-119 ◽  
Author(s):  
Sweta Kumari ◽  
Arumugam Mohana Priya ◽  
Sajitha Lulu ◽  
Mohammad Tauqueer

Author(s):  
Suraj N. Mali ◽  
Anima Pandey

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.


2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.


RSC Advances ◽  
2016 ◽  
Vol 6 (2) ◽  
pp. 1466-1483 ◽  
Author(s):  
Mayank Kumar Sharma ◽  
Prashant R. Murumkar ◽  
Guanglin Kuang ◽  
Yun Tang ◽  
Mange Ram Yadav

A four featured pharmacophore and predictive 3D-QSAR models were developed which were used for virtual screening of the Asinex database to get chemically diverse hits of peripherally active CB1 receptor antagonists.


ChemBioChem ◽  
2005 ◽  
Vol 7 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Fei Ye ◽  
Zhen-Shan Zhang ◽  
Hai-Bin Luo ◽  
Jian-Hua Shen ◽  
Kai-Xian Chen ◽  
...  

Biochimie ◽  
2011 ◽  
Vol 93 (8) ◽  
pp. 1252-1266 ◽  
Author(s):  
Dik-Lung Ma ◽  
Daniel Shiu-Hin Chan ◽  
Paul Lee ◽  
Maria Hiu-Tung Kwan ◽  
Chung-Hang Leung

2012 ◽  
Vol 95 ◽  
pp. 101-111 ◽  
Author(s):  
Josep O. Pou ◽  
Yesica E. Alvarez ◽  
Justin K. Watson ◽  
Jonathan P. Mathews ◽  
Sarma Pisupati

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