Low-molecular-weight anti-HIV-1 agents targeting HIV-1 capsid proteins

Abstract

Design and synthesis of MKN-3 derivatives: a new class of small molecules, MKN-3 derivatives, were developed based on in silico screening, as dipeptide mimics of Trp184 and Met185 at the hydrophobic interaction site between two capsid (CA) proteins.

Development of Small-Molecule Anti-HIV-1 Agents Targeting HIV-1 Capsid Proteins

“…In our previous study, although , with a London dG value of −9.555, has high binding affinity for a CA molecule, it failed to show significant anti-HIV activity, possibly due to the poor cell membrane permeability of MKN-3 (2), which therefore could not penetrate the cells. 69) In general, the hydrophobicity of compounds is correlated to their cell membrane permeability. 73) The Log P value of MKN-3 (2) is 0.27, which is markedly lower than that of MKN-1 (1) (LogP: 2.97), which show significant anti-HIV activity.…”

“…67) As a result, hydrophobic derivatives of MKN-3 (2), TKB063 (3) and TON03 (4), showed significant anti-HIV activity. 69) Therefore, Log P values of compounds are considered and shown in Table 1.…”

“…This screening strategy provided some candidates with high binding affinity, including MKN-1 (1) and MKN-3 (2) with London dG values of −9.134 and −9.555, respectively. 67,69) MKN-1 (1) and MKN-3 (2) derivatives, 4), showed significant anti-HIV activity at the level of micromolar, 67,69) although MKN-3 (2) itself failed to show anti-HIV activity below 50 µM because of relatively high hydrophilicity of the compound. 69) In the present study, several derivatives of MKN-1 (1) were synthesized, and their anti-HIV activity and cytotoxicity were evaluated.…”

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

“…This enables critical molecular reactions and the development of drugs and therapeutics for various diseases [ 15 ]. Different protein mimicry approaches including DeNovo and structure-guided design can be used to stabilize protein structure, design drugs, and produce vaccines [ 15 , 16 , 17 ]. Furthermore, combining SMs with various specificities can lead to the creation of multi-functional peptides that can target multiple pathways or bind to multiple protein targets [ 18 ].…”

“…Peptide therapeutics generated in-silico using advanced algorithms and AI offer new possibilities for generating peptide therapeutics with high specificity [ 1 ]. Computation can aid in designing peptide structures to mimic protein activity, providing insight into important disease pathways, cellular localization, and potential therapeutic applications [ 15 , 16 , 17 ]. Peptide therapeutics developed through computation can therefore provide potential avenues for generating therapeutics against a broader range of drug targets [ 10 ].…”