Bioinformatics toolbox for exploring target mutation-induced drug resistance

Abstract

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician’s perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Degradation strategies of pesticide residue: From chemicals to synthetic biology

“…Under this situation, degradation cannot be cast a veil, and synthetic biology methods are blooming to offer solutions. The application can be seen in biotic pesticides [ 42 , 43 ], and degradation in particular.…”

Systematic Investigation of Machine Learning on Limited Data: A Study on Predicting Protein-Protein Binding Strength

“…• 𝑁 /.5 12 is the number of interactive heavy atom pairs between two partners. Two heavy atoms are considered interactive if their distance is within 6 Å.…”

“…• 𝑆 .1 12 is sum of the amphiphilicity index of amino acids located at the binding interface. The amphiphilicity index of amino acids is obtained from Amino Acid Index Database 66 with identifier MITS020101 67 .…”

“…In previous work, we have developed a set of data-driven machine learning methods to evaluate the impact of missense mutations on protein stability 4 and their interactions with other molecules [5][6][7][8][9][10] . These methods employed traditional machine learning algorithms alongside handcrafted features, making them stand out as one of the widely recognized tools [11][12][13] . Additionally, traditional machine learning algorithms provide a direct measure of feature importance, enhancing the interpretability of the model outcomes.…”

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

“…In previous work, we developed a set of data-driven machine learning methods to evaluate the impact of missense mutations on protein stability [4] and their interactions with other molecules [5] , [6] , [7] , [8] , [9] , [10] . These methods employed traditional machine learning algorithms alongside handcrafted features, establishing them as widely recognized tools [11] , [12] , [13] . Additionally, traditional machine learning algorithms provide a direct measure of feature importance, enhancing the interpretability of the model outcomes.…”