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Computational Drug Discovery and Development Along With Their Applications in the Treatment of Women-Associated Cancers
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Rahul Kumar, Rakesh Kumar, Harsh Goel, Somorjit Singh Ningombam, Pranay Tanwar
In SBDD, the target acts as a prerequisite material and relies on the 3D structure as the drug is binding to 3D surface of macromolecules. Usually, 3D structures of macromolecules are elucidated by various experimental approaches such as NMR or x-ray crystallography and resolved structures are deposited in PDB database [17–18]. If the 3D structure of target protein is not available, then it can be determined by using computational methods such as homology (or comparative), threading (or fold recognition) and ab initio (de novo) modelling. Several computational tools are available for 3D structure prediction (Table 5.1). Homology modelling depends upon the sequence homologs with known structure of protein which is used as a template for generating 3D structure of target protein [19–20]. If the homologs have a low sequence identity (<25–30), then the model is constructed by using a threading method which relies on the secondary structures of proteins [21]. Another method is ab initio, used to predict the structure of target protein if no template is available [22]. Once the model is anticipated, stereochemical and geometrical properties are assessed to optimize the quality of the 3D structure.
Study of Nutraceuticals in Cancer Treatment: An In Silico Approach
Published in Raj K. Keservani, Anil K. Sharma, Rajesh K. Kesharwani, Nutraceuticals and Dietary Supplements, 2020
Structure-based, ligand-based, hybrid protein–ligand pharmacophore methods, homology modeling, molecular docking, and molecular simulation studies help in different steps of drug discovery pipeline that can save both the time and expenditure. In silico tools also find applications in the domain of cancer drug development in natural products. Phytochemicals are potential sources of medicines in different diseases. In cancer therapeutics, along with chemotherapy, radiotherapy, and immunotherapy, nutrients can take part in the prevention and healing of certain types of cancer. Different stages of cancers can be checked by nutraceuticals, and their mechanism of action at the molecular level can be explained and visualized by using computational studies.
Preliminary Phytochemical Screening and Identification of Bioactive Compounds from Banana Inflorescence and to Find the Interactions on Molecular Docking for PCOS
Published in Parimelazhagan Thangaraj, Phytomedicine, 2020
M. C. Kamaraj, Suman Thamburaj, R. Akshaya, V. Bhanu Deepthi
The protein sequence was subjected for comparative homology modeling via Swiss-model (Kiefer et al. 2008) and evaluated by the RAMPAGE online server (Lovell et al. 2003). The protein was validated by using the online server PROCHECK (Laskowski et al. 1996). The Swiss-model performs the sequence alignments and searches for the putative template protein for generating the 3D model.
Identification of a novel SPT inhibitor WXP-003 by docking-based virtual screening and investigation of its anti-fungi effect
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2021
Xin Wang, Xin Yang, Xin Sun, Yi Qian, Mengyao Fan, Zhehao Zhang, Kaiyuan Deng, Zaixiang Lou, Zejun Pei, Jingyu Zhu
Initially, the input human SPT sequences were searched to find homologous protein structures using BLAST, and this resulted four proteins as matching proteins in the PDB, structure ID 2X8U (a crystal structure of Sphingomonas wittichii), 2JG2 (a crystal structure of Sphingomonas paucimobilis), 3A2B (a crystal structure of Sphingobacterium multivorum) and 4BMK (a crystal structure of Sphingomonas paucimobilis). As is well known, the sequence alignment plays a key role in homology modelling method. From the BLASTP results, the human SPT exhibited a good alignment with the selected template proteins. The sequence identities were 29.41% for 2X8U, 28.44% for 2JG2, 32.33% for 3A2B, and 28.44% for 4BMK, and the alignments of structures and sequences showed in Figure 3. The quality of the homology SPT model was assessed through the Ramachandran plot generated by SAVES server. As shown in Figure 4, 90.2% amino acids were in most favoured regions, 7.4% amino acids were in additional allowed regions and only 0.8% were in disallowed regions. These results indicated that the building human SPT model was satisfactory and reliable for the following VS.
The compromise of virtual screening and its impact on drug discovery
Published in Expert Opinion on Drug Discovery, 2019
Olivia Slater, Maria Kontoyianni
How the availability of protein-ligand complexes and sequencing of the human genome project made docking and VS a staple in drug discovery may be obvious. Molecular recognition is the cornerstone of ligand binding, a key step in enzymatic reactions and protein function. Small molecules are recognized by macromolecules or interfaces eliciting a biological effect. Drug discovery is based on that binding and its disruption or enhancement, depending on the desired physiological profile. Having structural descriptions of receptor-ligand complexes through crystallography or NMR provides information on intermolecular interactions, binding conformations, potential binding pockets, and mechanistic details if crystal structures of the same receptor with different bound ligands are available. Further, with the human genome project, the number of potential therapeutic targets increased, expanding the focus of pharmaceutical discovery and escalating the need for high-throughput processes. There are approximately 20,000 protein-encoding genes [1–4], while a core proteome is suggested to include 10,000–12,000 ubiquitously expressed proteins [2,3,5–8]. Given that the currently approved drugs are targeting only 618 proteins [9], a high number of proteins remains available to potentially be exploited by drugs. Even in cases that macromolecular structures are lacking, homology modeling efforts can still be amenable to docking and VS to varying degrees.
Design of novel PhMTNA inhibitors, targeting neurological disorder through homology modeling, molecular docking, and dynamics approaches
Published in Journal of Receptors and Signal Transduction, 2019
Prajisha Jayaprakash, Jayashree Biswal, Sureka Kanagarajan, Dhamodharan Prabhu, Prerana Gogoi, Shankar Prasad Kanaujia, Jeyaraman Jeyakanthan
The amino acid sequence of PhMTNA protein was retrieved from UniProt database (UniProtKB accession number: O58433) which comprises of 364 amino acids [12]. A similarity search for PhMTNA was performed using the NCBI BLASTP to find a suitable template. Crystal structure of MTNA from Bacillus subtilis complexed with sulfate ion (PDB ID: 2YRF) of 2.60 Å resolution was chosen as the best template having 50% identity and a query coverage of 94% with the target protein. Using ClustalW, sequence alignment was performed between target and template sequence to find the conserved residues among the sequences [13]. Homology modeling was performed using Modeler 9v11, generated five models, and the best model was selected based upon the least DOPE (Discrete Optimized Protein Energy) score gives a clear relationship between the template and the modeled structure based on a statistical program called atomic distance-dependent. The lower dope score, higher is the reliability of the model [14]. After predicting the three-dimensional structure of PhMTNA protein, validation of the modeled structure was done using Structure Analysis and Verification Server (SAVS) in order to assess the overall stereo chemical quality of the modeled protein (www.mbi.ucla.edu/SAVS). Ramachandran plot analysis was performed using the program PROCHECK [15]. The modeled structure of PhMTNA was further evaluated using VERIFY3D, PROSA, and ERRAT, a verification algorithm well-suited for evaluating the progress of crystallographic model building, validation and refinement [16].