An in silico scientific basis for LL-37 as a therapeutic for Covid-19.
Kiran Bharat LokhandeTanushree BanerjeeKakumani Venkateswara SwamyPayel GhoshManisha S DeshpandePublished in: Proteins (2021)
A multi-pronged approach with help in all forms possible is essential to completely overcome the Covid-19 pandemic. There is a requirement to research as many new and different types of approaches as possible to cater to the entire world population, complementing the vaccines with promising results. The need is also because SARS-CoV-2 has several unknown or variable facets which get revealed from time to time. In this work, in silico scientific findings are presented, which are indicative of the potential for the use of the LL-37 human anti-microbial peptide as a therapeutic against SARS-CoV-2. This indication is based on the high structural similarity of LL-37 to the N-terminal helix, with which the virus interacts, of the receptor for SARS-CoV-2, Angiotensin Converting Enzyme 2. Moreover, there is positive prediction of binding of LL-37 to the receptor-binding domain of SARS-CoV-2; this is the first study to have described this interaction. In silico data on the safety of LL-37 are also reported. As Vitamin D is known to upregulate the expression of LL-37, the vitamin is a candidate preventive molecule. This work provides the possible basis for an inverse correlation between Vitamin D levels in the body and the severity of or susceptibility to Covid-19, as widely reported in literature. With the scientific link put forth herein, Vitamin D could be used at an effective, medically prescribed, safe dose as a preventive. The information in this report would be valuable in bolstering the worldwide efforts to eliminate the pandemic as early as possible.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- binding protein
- angiotensin converting enzyme
- molecular docking
- angiotensin ii
- poor prognosis
- healthcare
- dna binding
- coronavirus disease
- systematic review
- microbial community
- risk assessment
- single cell
- big data
- electronic health record
- quality improvement
- social media
- health information