In Silico Exploration of Microtubule Agent Griseofulvin and Its Derivatives Interactions with Different Human ß-Tubulin Isotypes.

Tubulin isotypes are known to regulate microtubule stability and dynamics, as well as to play a role in the development of resistance to microtubule-targeted cancer drugs. Griseofulvin is known to disrupt cell microtubule dynamics and cause cell death in cancer cells through binding to tubulin protein at the taxol site. However, the detailed binding mode involved molecular interactions, and binding affinities with different human ß-tubulin isotypes are not well understood. Here, the binding affinities of human ß-tubulin isotypes with griseofulvin and its derivatives were investigated using molecular docking, molecular dynamics simulation, and binding energy calculations. Multiple sequence analysis shows that the amino acid sequences are different in the griseofulvin binding pocket of ßI isotypes. However, no differences were observed at the griseofulvin binding pocket of other ß-tubulin isotypes. Our molecular docking results show the favorable interaction and significant affinity of griseofulvin and its derivatives toward human ß-tubulin isotypes. Further, molecular dynamics simulation results show the structural stability of most ß-tubulin isotypes upon binding to the G1 derivative. Taxol is an effective drug in breast cancer, but resistance to it is known. Modern anticancer treatments use a combination of multiple drugs to alleviate the problem of cancer cells resistance to chemotherapy. Our study provides a significant understanding of the involved molecular interactions of griseofulvin and its derivatives with ß-tubulin isotypes, which may help to design potent griseofulvin analogues for specific tubulin isotypes in multidrug-resistance cancer cells in future.

Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research.

Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450 000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics.