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Advancement in Gene Delivery
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Shilpa Rawat, Akash Chauhan, Rishabha Malviya, Md. Aftab Alam, Swati Verma, Shivkanya Fuloria
Only the “homology modeling” technique, which normally consists of four phases, produces solid findings in the prediction of 3D protein complexes (modeling):Data banks are used to detect structural homologs;Alignment of the target and the template;Model development and optimization;Model evaluation.SWISS-MODEL is a 3D protein structure comparative homology modeling tool. In 1993, it pioneered the field of automated modeling, and it is today the most widely used free web-based automated modeling tool. In 2002, the service processed 120,000 user queries for 3D protein models. The SWISS-Internet MODEL interface allows for varying levels of user involvement. In “first approach mode,” only an amino acid sequence of a protein is supplied to build a 3D model. The server handles all template selection, alignment, and model creation. In “alignment mode,” the modeling approach is based on a user-defined target–template alignment. In “project mode,” Deep View, an integrated sequence-to-structure workbench (available for PC, Macintosh, Linux, and SGI at https://www.expasy.org/spdbv), can perform sophisticated modeling jobs. All models are returned via email, along with a detailed modeling report. The What Check and ANOLEA assessments are entirely optional. CPHmodels, Geno3D, and ESyPred3D are examples of homonymy modelers [54].
Application of Computational and Bioinformatics Techniques in Drug Repurposing for Effective Development of Potential Drug Candidate for the Management of COVID-19
Published in Hajiya Mairo Inuwa, Ifeoma Maureen Ezeonu, Charles Oluwaseun Adetunji, Emmanuel Olufemi Ekundayo, Abubakar Gidado, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Medical Biotechnology, Biopharmaceutics, Forensic Science and Bioinformatics, 2022
Charles Oluwaseun Adetunji, Olaniyan Tope Olugbemi, Muhammad Akram, Umme Laila, Michael Olugbenga Samuel, Ayomide Michael Oshinjo, Juliana Bunmi Adetunji, Gloria E. Okotie, Nwadiuto (Diuto) Esiobu, Omotayo Opemipo Oyedara, Folasade Muibat Adeyemi
The lack of information about the crystal structure of proteins hinders drug development by pharmaceutical companies or scientists. Hence, homology modeling is a widely used application to build reliable sequence and structure of amino acids and proteins (Vyas et al., 2012). This technique is also known as comparative modeling of proteins. Homology modeling is considered as a reliable and successful technique for comparing sequences of amino acid and identifying the three-dimensional structure of proteins. Different tools that can be used for homology modeling include MODELLER, Phyre2, and web-based programs like SWISS-MODEL (Xu et al., 2000).
Protein structure prediction
Published in A. K. Haghi, Lionello Pogliani, Devrim Balköse, Omari V. Mukbaniani, Andrew G. Mercader, Applied Chemistry and Chemical Engineering, 2017
Homology modeling is based on the assumptions that two homologous proteins will share very similar structure, as in the due course of the evolution, structures are more conserved than amino acid sequences. So a good model generation depends upon good alignment between the target and the template. In general, we used to predict a model when sequence identity was more than 30%. Highly homologous sequences will generate a more accurate model (Fig. 12.2). The tools for homology modeling are listed in Table 12.1.
Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology
Published in Journal of Environmental Science and Health, Part C, 2018
Yan Li, Gabriel Idakwo, Sundar Thangapandian, Minjun Chen, Huixiao Hong, Chaoyang Zhang, Ping Gong
We are currently developing and optimizing libraries of target-specific models for qualitative toxicity classification and quantitative toxicity prediction using curated datasets in the ChemMoA database as the training data. The 3D structural model of a target protein is either directly retrieved from PDB or built via homology modeling using Modeller51 or Swiss-Model.52 The prediction model libraries are deployed to the TsTKb and made available to the public via a Representational State Transfer (REST) application programing interface (API). This provides a convenient platform for speedy and accurate profiling of toxicological properties for uncharacterized chemicals in the user’s query. A workflow is depicted in Figure 3 and explained in details as follows:
Evaluation of a single amino acid substitution at position 79 of human IFN-α2b in interferon-receptor assembly and activity
Published in Preparative Biochemistry and Biotechnology, 2019
Samira Talebi, Alireza Saeedinia, Mehdi Zeinoddini, Fathollah Ahmadpour, Majid Sadeghizadeh
Protein sequences and 3D structures of IFN-α2b and IFNAR1,2 were attained from PDB (Protein Data Bank). The 3D structure of IFN-α and IFNAR1,2 was accessible in PDB, nonetheless, there were lots of errors in their structure and its dues to the error in their protein–protein docking. In consequence, the 3D structure was predicted by homology modeling. Both native and our designed Mutants (such as Asn93 to Glu, Gln62 to Lys, Glu96 to Trp, Thr79 to Gln, Leu128 to Tyr, Phe27 to Thr, Phe27 to Asn, Thr79 to Gln) in association to IFNAR1 were modeled through Easy Modeler 2.1 using the available templates in Protein Data Bank.