Year: 2022
Type: editorial
Abstract: Editorial on the Research Topic Machine learning for peptide structure, function, and design Peptides with a length from 2 to 50 amino acids play important roles in the biological process and functions.Because of their wonderful variety of biological properties, peptide-based therapy has been a potential treatment for many diseases for decades.Meanwhile, peptide sequence, structure, and function are closely related, especially the relationship between structure and function.However, obtaining the structure or function of the peptides with wet experiments is costly, laborious, and time-consuming.In recent years, because of the obvious advantages of traditional machine learning and deep learning technology, these methods have been widely used in various protein or peptide structure and function predictions such as many kinds of site prediction, various interactions prediction, drug-targets prediction, and so on.This Research Topic explores the new technologies and applications of machine learning on peptide structure and function prediction.We are pleased to see that the authors of the 12 accepted papers introduce the research progress and application of the latest machine learning techniques in peptide-related problems.They are related to peptide treatment of disease, inter-residue distance or contact, binding site prediction, drug targets, community-specific function landscape for peptides, and related discussions.Peptide-based therapy has become a new potential method of disease treatment in recent decades years.Compared with traditional disease treatments, such as radiation therapy and chemotherapy, therapeutic peptides could avoid the obvious side effects of traditional disease treatments to guarantee precise treatment.Furthermore, most therapeutic peptides have the characteristics of high specificity, low production cost, low toxicity, easy synthesis, modification, etc.In this topic, three tools are
Source: Frontiers in Genetics
Authors: Ruiquan Ge, Chuan Dong, Juexin Wang, Yanjie Wei
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,950
Awards: G3876926024, G7582340057
OpenAlex ID: W4296401075