Computational prediction of antifungal peptides via Chou’s PseAAC and SVM

Abstract

With the increase in immunocompromised patients in the recent years, fungal infections have emerged as new and serious threat in hospitals. This, and the insufficiency of current antifungal therapies alongside their toxic effects on patients, has led to the increased interest in seeking new antifungal peptides. In the present study, we have developed a prediction method for screening of antifungal peptides. For this, we have chosen Chou's pseudo amino acid composition (PseAAC) to translate peptide sequences into numeric values. Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC [Formula: see text] SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides.

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

“…Attempts of using more complex solutions, as Long Short Term Memory (LSTM) 40 artificial neural networks, have also been performed 41 , supported by the rationale that sequences of amino acids conceptually resemble discrete time series. In both cases, results were remarkable, reaching an accuracy in the discrimination of antimicrobial vs. non-antimicrobial peptides of 91.9% 36 , 94.76% 37 , and 95.79% 41 . It has nevertheless been highlighted that special care should be used when interpreting these results, and especially avoid taking them at face value.…”

“…From a technical point of view, these papers can be organized in two groups. Several authors have applied standard data mining models, including Support Vector Machines (SVM) 36 37 38 and semi-supervised clustering 39 . Attempts of using more complex solutions, as Long Short Term Memory (LSTM) 40 artificial neural networks, have also been performed 41 , supported by the rationale that sequences of amino acids conceptually resemble discrete time series.…”

“…Secondly, both problems can also benefit from an increasing availability of real data, not necessarily just coming from traditional sources, but even from social networks and media 101 , 102 . Yet, analysing those data is not a simple endeavour: it requires combining knowledge and techniques coming from fields far apart, and overcoming biases and bad practices 36 , 37 , 39 , 41 , 42 . Finally, and possibly because of this complexity, a surprising low number of research studies have been published in the last two years on AI applied to antimicrobial resistance and climate change in epidemiology; and this in spite of many opinions in favour of such integration 19 , 94 , 103 , and even with the availability of specialised research funds.…”

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

“…The peptide sequence descriptors include amino acid composition as well as Chou’s pseudoamino acid composition for incorporation of the sequence order information [ 35 ]. With success of PseACC in the sequence-based prediction [ 36 , 37 , 38 ], it is an imperative addition to the standard composition feature vectors. The peptide structure descriptors have been formulated with molecular weight, peptide shape ( ), positive charge ( ), negative charge ( ) and volume.…”

A review on antimicrobial peptides databases and the computational tools

“…Furthermore, AMPs are mainly produced by some immune cells such as neutrophils and macrophages and exert immunomodulatory activities such as the recruitment and activation of immune cells, initiation of adaptive immunity, reduction of inflammation ( 27 ), chemo attraction of immune cells, induction of chemokine, cytokine, and histamine production and secretion, wound healing stimulation, angiogenesis and adjuvant city ( 76 ).…”