Computational prediction of therapeutic peptides based on graph index

Abstract

As therapeutic peptides have been taken into consideration in disease therapy in recent years, many biologists spent time and labor to verify various functional peptides from a large number of peptide sequences. In order to reduce the workload and increase the efficiency of identification of functional proteins, we propose a sequence-based model, q-FP (functional peptide prediction based on the q-Wiener Index), capable of recognizing potentially functional proteins. We extract three types of features by mixing graphic representation and statistical indices based on the q-Wiener index and physicochemical properties of amino acids. Our support-vector-machine-based model achieves an accuracy of 96.71%, 93.34%, 98.40%, and 91.40% for anticancer, virulent, and allergenic proteins datasets, respectively, by using 5-fold cross validation.

PTPD: predicting therapeutic peptides by deep learning and word2vec

“…We also compared the performance of PTPD with that of q-FP [10], AS and 2Gram [41], VirulentPred [18], and NTX-pred [16] on a bacterial neurotoxins dataset (Table 4 and Fig. 4).…”

“…In addition, various types of amino acid compositions (AACs) of peptide sequences have been introduced to develop prediction models such as Chou’s pseudo amino acid composition (PseAAC) [8], combinations of AACs, average chemical shifts (acACS) and reduced AAC (RAAC) [6], pseudo g-Gap DPC, amphiphilic PseAAC, and reduced amino acid alphabet (RAAAC) [9]. Other methods include computational tools developed based on the q-Wiener graph indices for ACP predication [10]. In addition, machine learning methods were adopted to promote model efficiency [6, 9, 11].…”

“…Studies have also focused on conducting feature extraction of sequences such as protein presentations, by using amino acid sequence features and evolutionary information of a given protein [19]. Moreover, a computational tool based on the q-Wiener graph indices was also proposed to effectively predict virulent proteins [10]. Despite substantial progress, identifying specific peptides from massive protein databases remains challenging.…”

Molecular Design of Peptide-Fc Fusion Drugs

“…In this situation, structure-free peptide design is a good alternative solution. With the rapid increasing of available bioactive peptide database and the advance of artificial intelligence, predicting peptide leads based on sequence information has become popular [61][62][63][64][65][66][67][68][69][70][71][72][73][74]. However, there is a big problem for the current structure-free peptide design methods.…”

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

“…In recent years, protein sequencebased methods (Yu et al, 2017) are becoming the most widely applied technique for predicting PPIs due to the availability of protein sequence data. Liu et al (2012) designs a sequence analysis method to represent protein sequences based on hypergeometric series using the q-Wiener index (Xu et al, 2017). X. Li et al employs a global encoding approach (GE) to describe global information of amino sequence (Li et al, 2009).…”