Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides

Abstract

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

“…Despite a great diversity of peptide features (classical and non-classical) that has been used in AMPs prediction/design, most of those features are sequences- or property- based; however, the 3D structural information of AMPs has not been deeply exploited for such aims [ 173 , 174 , 175 ]. Although experimental determinate 3D structures of AMPs are used in minor proportion than their sequences, the 3D structure prediction tools are becoming more accessible and less computational demanding when considering new advances in both software and computer architectures [ 176 , 177 ].…”

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

“…Despite the large amount of effort put into the prediction of ACPs using AI, ACPs for the treatment of human cancer have not yet been explored well. However, some research reports that peptides developed by AI have anticancer activity on cancerous cell lines and negligible toxicity on normal cell lines ( Table S1 ) [ 147 , 148 , 149 , 150 ]. Hence, it is true that the efficacy and safety of ACPs developed by AI are not well explored yet, but they have high potential to be a promising candidate for cancer therapy and can be applied for preclinical and clinical trials later.…”

In silico identification and in vitro assessment of a potential anti-breast cancer activity of antimicrobial peptide retrieved from the ATMP1 Anabas testudineus fish peptide

“…The three-dimensional (3D) structure of peptides may be altered and improved to produce antimicrobial peptides (AMPs) with increased efficacy, selectivity, and reduced toxicity. The 3D structure of the peptides was used to create a distinct model for predicting anti-cancer peptides (ACPs), hemolytic peptides, and hazardous peptides ( Zhao et al, 2021 ; Sugrani et al, 2020 ; E-Kobon et al, 2016 ; Lamiable et al, 2016 ). Additionally, the previous study stated that there is a need to develop more peptides and proteins to increase their efficacy and selectivity against cancer cells ( Rozek et al, 2000 ; Chen, Lin & Lin, 2009 ).…”

“…Additionally, the previous study stated that there is a need to develop more peptides and proteins to increase their efficacy and selectivity against cancer cells ( Rozek et al, 2000 ; Chen, Lin & Lin, 2009 ). Enhancing AMP’s selectivity and cationic properties will minimize its toxicity to eukaryotic cells and increase its potential therapeutic index ( Zhao et al, 2021 ; Thomsen et al, 2020 ). Therefore, new design approaches are needed to identify more potent sequences ( i.e ., more unmodified sequences without post-translational modifications) that are more effective, have multiple activities without toxicity on normal cells, and have a good selectivity profile.…”