Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set

Abstract

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence–activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at .

Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

“… Target or calibration databases: We considered five databases of APPs and non-APPs reported in ref. 30 There were different balanced and unbalanced datasets stored in five FASTA files with thousands of labeled APPs and non-APPs (SI1C-G).…”

“…To assess the relative performance of the mQSSMs, we used the five data sets of APPs and non-APPs recently provided in ref. 30 These datasets were obtained from starPepDB, whose description can be found in https://biocom-ampdiscover.cicese.mx/dataset. Each set of queries and similarity thresholds were wrapped into a calibration algorithm, comprising a modified virtual screening simulation technique.…”

“…Each set of queries and similarity thresholds were wrapped into a calibration algorithm, comprising a modified virtual screening simulation technique. 30 In these models, we used just the queries' subset of APPs as the multi-query calibration group, while the active and inactive subsets were the target datasets. The prediction ensemble, composed of similarity scores of each peptide D in the target dataset with each query Q, was ordered with the MAX-SIM multi-classifier.…”

“…62 After applying these statistical tests, we obtained the best mQSSM. A second comparison was carried out to compare our best similarity searching models with ML-based models reported in the literature for APP prediction 30,31 by using the same five calibration datasets.…”

“…29 It is worth mentioning this is the first time we are exploring the chemical space from starPepDB to retrieve valuable information derived from AMPs. In addition, we evaluated the multi-query similarity searching models (mQSSMs) performance with five benchmarking data sets of APP/non-APPs, and we compared these results with performance metrics of ML APPs prediction servers AMPDiscover (https://biocom-ampdiscover.cicese.mx) 30 and AMPFun (http://fdblab.csie.ncu.edu.tw/AMPfun/index.html). 31…”