Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Database
Language
Document Type
Year range
1.
Int J Biol Macromol ; 209(Pt A): 984-990, 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1796725

ABSTRACT

MERS-CoV main protease (Mpro) is essential for the maturation of the coronavirus; therefore, considered a potential drug target. Detailed conformational information is essential to developing antiviral therapeutics. However, the conformation of MERS-CoV Mpro under different conditions is poorly characterized. In this study, MERS-CoV Mpro was recombinantly produced in E.coli and characterized its structural stability with respect to changes in pH and temperatures. The intrinsic and extrinsic fluorescence measurements revealed that MERS-CoV Mpro tertiary structure was exposed to the polar environment due to the unfolding of the tertiary structure. However, the secondary structure of MERS-CoV Mpro was gained at low pH because of charge-charge repulsion. Furthermore, differential scanning fluorometry studies of Mpro showed a single thermal transition at all pHs except at pH 2.0; no transitions were observed. The data from the spectroscopic studies suggest that the MERS-CoV Mpro forms a molten globule-like state at pH 2.0. Insilico studies showed that the covid-19 Mpro shows 96.08% and 50.65% similarity to that of SARS-CoV Mpro and MERS-CoV Mpro, respectively. This study provides a basic understanding of the thermodynamic and structural properties of MERS-CoV Mpro.


Subject(s)
Coronavirus 3C Proteases , Middle East Respiratory Syndrome Coronavirus , Coronavirus 3C Proteases/genetics , Coronavirus 3C Proteases/metabolism , Middle East Respiratory Syndrome Coronavirus/enzymology , Middle East Respiratory Syndrome Coronavirus/genetics , Protein Conformation , Recombinant Proteins
2.
Comput Biol Med ; 143: 105292, 2022 Feb 08.
Article in English | MEDLINE | ID: covidwho-1670370

ABSTRACT

There has been recent success in prediction of the three-dimensional folded native structures of proteins, most famously by the AlphaFold Algorithm running on Google's/Alphabet's DeepMind computer. However, this largely involves machine learning of protein structures and is not a de novo protein structure prediction method for predicting three-dimensional structures from amino acid residue sequences. A de novo approach would be based almost entirely on general principles of energy and entropy that govern protein folding energetics, and importantly do so without the use of the amino acid sequences and structural features of other proteins. Most consider that problem as still unsolved even though it has occupied leading scientists for decades. Many consider that it remains one of the major outstanding issues in modern science. There is crucial continuing help from experimental findings on protein unfolding and refolding in the laboratory, but only to a limited extent because many researchers consider that the speed by which real proteins folds themselves, often from milliseconds to minutes, is itself still not fully understood. This is unfortunate, because a practical solution to the problem would probably have a major effect on personalized medicine, the pharmaceutical industry, biotechnology, and nanotechnology, including for example "smaller" tasks such as better modeling of flexible "unfolded" regions of the SARS-COV-2 spike glycoprotein when interacting with its cell receptor, antibodies, and therapeutic agents. Some important ideas from earlier studies are given before moving on to lessons from periodic and aperiodic crystals, and a possible role for quantum phenomena. The conclusion is that better computation of entropy should be the priority, though that is presented guardedly.

SELECTION OF CITATIONS
SEARCH DETAIL