Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

Abstract

Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu.cn/iampraac.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization

“…As depicted in Figure 7 , numerous models currently specialize in AMP attribute prediction, ranging from initial single activity prediction to multi-attribute prediction. In our Diff-AMP framework, we have integrated leading AMP attribute prediction models, including AMAP [ 62 ], AMPfun [ 63 ], CAMP [ 64 ], DbAMP2.0 [ 65 ], DRamp [ 66 ], iamp-2l [ 67 ], iamp-CA2L [ 44 ], iamp-RACC [ 68 ], MLAMP [ 69 ], multi-AMP [ 47 ] and IAMPCN [ 53 ]. This capability allows the Diff-AMP framework to extensively predict AMP properties, encompassing over 20 attributes such as antibacterial, anti-Gram-positive, anti-Gram-negative, antifungal, antiviral and more.…”

Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition

“…With each evaluation method, there were up to 18 different grouping methods, from which up to 673 schemes were created. This method was applied in classification of human enzymes and in classification of antimicrobial peptides . Another way of grouping amino acids was proposed by Takabatake et al in which the BLOSUM62 matrix was used to calculate the number of similarity scores between amino acids prior to calculating the correlation coefficients of each pair of amino acids and grouping the amino acids using hierarchical clustering.…”

Novel and Alternative Therapeutic Strategies for Controlling Avian Viral Infectious Diseases: Focus on Infectious Bronchitis and Avian Influenza

“…Pre-treatment of embryos with SIAMP and infected with IBV showed a remarkable reduction in mortality. Though the authors of this study did not present the characterization of the antiviral mechanism, they suggested that SIAMP might play a role during virus attachment to the cell surface, thus limiting IBV infection (93). Mannose binding lectin (MBL) is well known as an antiviral agent (94).…”