# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
###### Abstract  
Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel antimicrobial peptides, there is a growing interest in the development of computational prediction approaches, in parallel with the studies performing wet-lab experiments. The computational approaches aim to understand what controls antimicrobial activity from the perspective of machine learning, and to uncover the biological properties that define antimicrobial activity. Throughout this study, we aim to develop a novel prediction approach that can identify peptides with high antimicrobial activity against selected target bacteria. Along this line, we propose a novel method called AMP-GSM (antimicrobial peptide-grouping-scoring-modeling). AMP-GSM includes three main components: grouping, scoring, and modeling. The grouping component creates sub-datasets via placing the physicochemical, linguistic, sequence, and structure-based features into different groups. The scoring component gives a score for each group according to their ability to distinguish whether it is an antimicrobial peptide or not. As the final part of our method, the model built using the top-ranked groups is evaluated (modeling component). The method was tested for three AMP prediction datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. When we used 10 features (which are members of the physicochemical group), we obtained the highest area under curve (AUC) value for both the Gram-negative (99%) and Gram-positive (98%) datasets. AMP-GSM investigates the most significant feature groups that improve AMP prediction. A number of physico-chemical features from the AMP-GSM's final selection demonstrate how important these variables are in terms of defining peptide characteristics and how they should be taken into account when creating models to predict peptide activity. antimicrobial peptide (AMP) prediction; physico-chemical properties; grouping; scoring; modeling (GSM); antibiotic resistance; QSAR; Gram-negative bacteria; Gram-positive bacteria +
Footnote †: journal: Medical  
A number of databases have been recently introduced that provide details about AMPs and their functions. These databases include DBAASP [2], CAMP [3], YADAMP [4], LAMP [5], and DRAMP [6]. In recent years, different computational methods have been developed to predict antimicrobial peptides. As machine learning is utilized to solve numerous biological problems, it has also gained popularity for AMP prediction problems. In this problem setting, a query protein is predicted as AMP or non-AMP via analyzing its biological features. These features can be generated from the protein sequence or protein structure. The classification model can be used to score the peptides and hence it can help the selection of the peptide with the highest antimicrobial activity. By selecting the best candidate AMP before synthesis and then by testing it against pathogens in the wet lab, computational methods aid in developing effective antimicrobial drugs. Various machine learning algorithms have been applied to this problem, including support vector machines (SVM) [3; 7; 8; 9], random forests (RF) [3], artificial neural networks (ANN) [3; 8; 10], and logistic regression [11; 12]. Additionally, there are a few comprehensive studies that employ multiple ML models simultaneously to have a better understanding of how well ML methods work for AMP prediction [13; 14]. For the prediction of AMPs in different organisms, such as bacteria, fish, insects, plants and humans, [PERSON] et al. [15] built a model using amino acid compositions, amino acids pairs, and physico-chemical characteristics as features, with RF, SVM, and k-nearest neighbor (k-NN) as the classification algorithms.