Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Ahmad, Sarfraz; Mirza, Muhammad Usman; Trant, John F.

doi:10.1016/j.jpha.2023.04.008

Cited by 2 publications

(2 citation statements)

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“…The in silico design of AFPs is a burgeoning field with the potential to expedite the discovery process. 4,5 Machine learning-based bioactivate peptide prediction, particularly quantitative structure−activity relationship (QSAR) modeling, has shown considerable promise in identifying AFPs. 6−8 For example, Jan et al 9 developed a target-AMP model for antimicrobial peptide (AMP) identification using K-nearest neighbor, random forest, and support vector machine algorithms, achieving theoretical accuracies up to 97.07%.…”

Section: Introductionmentioning

confidence: 99%

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In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity

Zhang,

Sun,

Zhao

et al. 2024

J. Chem. Inf. Model.

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Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97–20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10–60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at .

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Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the development of new AFPs faces the contradiction between massive sequence space and limited experimental validation efficiency. The in silico design of AFPs is a burgeoning field with the potential to expedite the discovery process. , …”

Section: Introductionmentioning

confidence: 99%