The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction
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AbstractIt has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.
All Author(s) ListHongjian Li, Jiangjun Peng, Yee Leung, Kwong-Sak Leung, Man-Hon Wong, Gang Lu, Pedro J Ballester
Journal nameBiomolecules
Year2018
Month3
Day14
Volume Number8
Issue Number1
PublisherMDPI
Place of PublicationSwitzerland
Article number12
ISSN2218-273X
LanguagesEnglish-United States
Keywordsmachine learning, scoring function, molecular docking, binding affinity prediction

Last updated on 2020-30-07 at 01:24