scholarly journals In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
William F. Porto ◽  
Luz Irazazabal ◽  
Eliane S. F. Alves ◽  
Suzana M. Ribeiro ◽  
Carolina O. Matos ◽  
...  
2021 ◽  
Vol 596 ◽  
pp. 352-363
Author(s):  
Rafael V.M. Freire ◽  
Yeny Pillco-Valencia ◽  
Gabriel C.A. da Hora ◽  
Madeleine Ramstedt ◽  
Linda Sandblad ◽  
...  

2015 ◽  
Vol 14 (6) ◽  
pp. 2649-2658 ◽  
Author(s):  
Mikel Azkargorta ◽  
Javier Soria ◽  
Claudia Ojeda ◽  
Fanny Guzmán ◽  
Arantxa Acera ◽  
...  

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Haohan Liu ◽  
Miaomiao Lei ◽  
Xiaoyuan Du ◽  
Pengfei Cui ◽  
Shicui Zhang

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Deepesh Nagarajan ◽  
Tushar Nagarajan ◽  
Neha Nanajkar ◽  
Nagasuma Chandra

Antimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data are now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms of Gram-negative, Gram-positive, mycobacterial, and fungal origin. We also present circular dichroism spectra for all antimicrobial peptides. We draw simple inferences from this data, and we discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.


2012 ◽  
Vol 13 (9) ◽  
pp. 1148-1157 ◽  
Author(s):  
Marc Torrent ◽  
M. Victoria Nogues ◽  
Ester Boix

Author(s):  
Nireeksha Nireeksha ◽  
Pavan Gollapalli ◽  
Sudhir Rama Varma ◽  
Mithra N. Hegde ◽  
N. Suchetha Kumari

AbstractLimiting the spread of virus during the recent pandemic outbreak was a major challenge. Viral loads in saliva, nasopharyngeal and oropharyngeal swabs were the major cause for droplet transmission and aerosols. Saliva being the major contributor for the presence of viral load is the major key factor; various mouthwashes and their combination were analyzed and utilized in health care centers to hamper the spread of virus and decrease viral load. The compositions of these mouthwashes to an extent affected the viral load and thereby transmission, but there is always a scope for other protocols which may provide better results. Here we evaluated the potential of antimicrobial peptide LL-37 in decreasing the viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through an in silico work and evidence from other studies. This narrative review highlighted a brief nonsystematic methodology to include the selected articles for discussion. Accessible electronic databases (Medline, Scopus, Web of Science, SciELO, and PubMed) were used to find studies that reported the salivary viral load of SARS-CoV-2 published between December 2019 and June 2021. The following keywords were utilized for brief searching of the databases: “saliva,” “viral load,” and “SARS-CoV-2.” Articles in English language, in vitro cell-line studies, ex vivo studies, and clinical trials explaining the viral load of SARS-CoV-2 in saliva and strategies to decrease viral load were included in this review. The search was complemented by manual searching of the reference lists of included articles and performing a citation search for any additional reviews. The antiviral potential of cationic host defense peptide LL-37 was evaluated using computational approaches providing in silico evidence. The analysis of docking studies and the display of positive interfacial hydrophobicity of LL-37 resulting in disruption of COVID-19 viral membrane elucidate the fact that LL-37 could be effective against all variants of SARS-CoV-2. Further experimental studies would be needed to confirm the binding of the receptor-binding domain with LL-37. The possibility of using it in many forms further to decrease the viral load by disrupting the viral membrane is seen.


2018 ◽  
Author(s):  
Deepesh Nagarajan ◽  
Tushar Nagarajan ◽  
Neha Nanajkar ◽  
Nagasuma Chandra

ABSTRACTAntimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data is now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms spanning gram positive, gram negative, fungal, and mycobacterial origin. We draw inferences from the results of 600 individual MIC assays, and discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.


2015 ◽  
Vol 3 (6) ◽  
Author(s):  
Caleb M. Agbale ◽  
Osmel Fleitas ◽  
Isaac K. Galyuon ◽  
Octavio L. Franco

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