Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components

Abstract

Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.

Tick Paralysis: Solving an Enigma

“…An eight degree of separation is the use of bioinformatics to predict toxin properties in protein folds with known neurotoxic associations. The rationale would be that toxins exhibit motifs, patterns, or signatures that are associated with the molecular mechanisms of paralysis function [ 112 ]. This approach has a time honored history with known motifs that are associated with various functions [ 113 , 114 ].…”

“…This approach has a time honored history with known motifs that are associated with various functions [ 113 , 114 ]. In the case of toxins, numerous approaches have been implemented to derive generalized descriptions of toxin functionality [ 40 , 41 , 112 , 115 ]. These may not necessarily depend on homology, but rather aim to treat toxin folds as scaffolds where recurring themes are presented.…”

“…These may not necessarily depend on homology, but rather aim to treat toxin folds as scaffolds where recurring themes are presented. As such, conserved residues may provide clues to functionality [ 112 ]. However, in most cysteine-rich peptide families, the only conserved residues are the cysteines that are involved in disulphide bonds, as exhibited for the ICK fold [ 67 ].…”

A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features

“…Using feature selection techniques to optimize feature set can not only economize the time for computation, but also build robust prediction model. In fact, many techniques such as principal component analysis (PCA) [ 53 ], minimal-redundancy-maximal-relevance (mRMR) [ 54 ], analysis of variance (ANOVA) [ 55 ], F-score algorithm [ 40 ], binomial distribution [ 56 ] have been proposed and used in sequence analysis and prediction. Thus, feature selection in the future works hopefully can improve prediction results.…”

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

“…To deal with this problem, the PseAAC (Pseudo Amino Acid Composition) was introduced [53][54][55]. Ever since the concept of pseudo amino acid composition or Chou's PseAAC [55][56][57][58] was proposed, it has been widely used in many biomedicine and drug development areas [59,60] as well as nearly all the areas of computational proteomics(see, e.g., [39,43,45,[61][62][63][64][65][66][67][68][69][70][71][72][73] and a long list of references cited in two review papers [74,75]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach have been extended to deal with DNA/RNA sequences [76][77][78][79][80][81][82] in computational genomics via PseKNC (Pseudo K-tuple Nucleotide Composition) [83,84].…”