Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Abstract

Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure–activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without a priori knowledge. In this study, we have established an effective QSAR protocol for screening antifungal peptides. The screening protocol integrates an accurate antifungal peptide classification model and four activity prediction models against specified target fungi. A demonstrative application was performed on more than three million candidate peptides, and three outstanding peptides were identified. The whole screening took only a few days, which was much faster than our previous experimental screening works. In conclusion, the protocol is useful and effective for reducing repetitive laboratory efforts in antifungal peptide discovery. The prediction server (antifungal Web server) is freely available at .

Designed Multifunctional Peptides for Intracellular Targets

“…The iAMPpred web server (accessed on 7 August 2022) of Meher et al [ 31 ] gives predictions for antibacterial, antiviral, and antifungal activity, but we reported only the last one. We also used the AntiFungal server of Zhang et al [ 32 ] ( , (accessed on 7 August 2022)) to predict the antifungal activity.…”

“…The probability for antifungal activity increased from 0.22 for the TriPaxB penetratin sequence RVVQVWFQNQRAKLKK (see Table 2 , peptide 3) to 0.54 or higher for the constructs RVVQVWFQNQRAKLKK-G-LKLFKKILKVL or RVVQVWFQNQRAKLKK-G-KKLFKKILKKL (see Table 2 , peptide 10 for the second construct predictions). The sequence should be submitted to other predictive algorithms (besides iAMPpred [ 31 ] and AntiFungal [ 32 ]) for serious consideration of experimental confirmations.…”

“…& Results of peptide antiviral prediction with servers ENNAVIA ( [ 28 ], sequence length restricted between 7 and 40 residues)/FIRM-AVP ( (accessed on 7 August 2022) [ 29 ]/Meta-iAVP ( (accessed on 7 August 2022) [ 30 ]. ⁋ Results of iAMPpred peptide antifungal prediction by Meher et al [ 31 ] ( , (accessed on 7 August 2022)) and Zhang et al [ 32 ] ( , (accessed on 7 August 2022)). § Results for the prediction of anti-inflammatory activity (Anti-inf.)…”

“…However, they are still helpful indicators of which natural peptides or in silico constructs are promising for much more expensive verifications in vitro and in vivo. A plethora of user-friendly servers has appeared during recent years for sequence-based prediction of cell-penetrating (CPP), antimicrobial (AMP), anticancer (ACP), antiviral (AVP), antifungal (AFP), and anti-inflammatory (AIP) peptides [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. An older server by Hwang et al [ 36 ] can be used to predict DNA binding.…”

New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning

“…ML models were established to predict the composition of complex systems by using molecular spectroscopy, [15][16][17][18] or to explore any quantitative structureactivity relationship through designing a large number of active molecules for disease treatment. 19 Recently, ML models were yielded to address the problems in the subtypes of AIS, 20 salvageable tissue lesion 21,22 and outcomes, 22 etc. The most model outputs were likely desirable, however, failed to substantially shorten the time from symptom onset to treatment, since the models were commonly built upon neuroimaging data, which were relatively time-consuming to acquire.…”