Computational Approaches to Natural Products Discovery, Volume 720 provides descriptions of the latest techniques in computational methods for the study of natural products. Techniques covered are related to genome mining, natural product biosynthesis, natural product evolution, and natural product structure. Chapters in this new release cover Prediction of natural product bioactivity from BGC sequence to guide genome mining efforts, Genome mining for resistance genes to identify bioactive natural products with ARTS, Predicting lasso peptide structure with LassoPred, Predicting lanthipeptide structure with Rosetta, Using lsaBGC to investigate BGC Evolution, and an Analysis of BGC evolution with CORASON.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Series Page - p. iii 6. Copyright - p. iv 7. Contributors - p. xi 8. Chapter One: Prediction of natural product bioactivity from BGC sequence to guide genome mining efforts - p. 1 9. Chapter Two: The ARTS toolset: Resistance-based genome mining for systematic prioritization of bioactive gene clusters - p. 35 10. Chapter Three: Bringing CORASON to Windows: Exploring fungal natural products through biosynthetic gene clusters - p. 61 11. Chapter Four: Predicting substrate specificity in fungal type III polyketide synthases - p. 75 12. Chapter Five: Predicting lasso peptide structure with LassoPred - p. 91 13. Chapter Six: Lanthipeptide structure prediction and design with Rosetta - p. 107 14. Chapter Seven: SMART deep learning tools to accelerate the characterization of natural product structures from their NMR data - p. 135 15. Chapter Eight: MS2DECIDE: Harnessing the complementarity of mass spectrometry annotation tools for prioritizing novel natural products - p. 169
Computational Approaches to Natural Products Discovery, Volume 720 provides descriptions of the latest techniques in computational methods for the study of natural products. Techniques covered are related to genome mining, natural product biosynthesis, natural product evolution, and natural product structure. Chapters in this new release cover Prediction of natural product bioactivity from BGC sequence to guide genome mining efforts, Genome mining for resistance genes to identify bioactive natural products with ARTS, Predicting lasso peptide structure with LassoPred, Predicting lanthipeptide structure with Rosetta, Using lsaBGC to investigate BGC Evolution, and an Analysis of BGC evolution with CORASON.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Series Page - p. iii 6. Copyright - p. iv 7. Contributors - p. xi 8. Chapter One: Prediction of natural product bioactivity from BGC sequence to guide genome mining efforts - p. 1 9. Chapter Two: The ARTS toolset: Resistance-based genome mining for systematic prioritization of bioactive gene clusters - p. 35 10. Chapter Three: Bringing CORASON to Windows: Exploring fungal natural products through biosynthetic gene clusters - p. 61 11. Chapter Four: Predicting substrate specificity in fungal type III polyketide synthases - p. 75 12. Chapter Five: Predicting lasso peptide structure with LassoPred - p. 91 13. Chapter Six: Lanthipeptide structure prediction and design with Rosetta - p. 107 14. Chapter Seven: SMART deep learning tools to accelerate the characterization of natural product structures from their NMR data - p. 135 15. Chapter Eight: MS2DECIDE: Harnessing the complementarity of mass spectrometry annotation tools for prioritizing novel natural products - p. 169
