Abstract
BackgroundRecognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem. In our work, we developed RF-Fold that uses random forest - one of the most powerful and scalable machine learning classification methods - to recognize protein folds.ResultsRF-Fold consists of hundreds of decision trees that can be trained efficiently on very large datasets to make accurate predictions on a highly imbalanced dataset. We evaluated RF-Fold on the standard Lindahl's benchmark dataset comprised of 976 × 975 target-template protein pairs through cross-validation. Compared with 17 different fold recognition methods, the performance of RF-Fold is generally comparable to the best performance in fold recognition of different difficulty ranging from the easiest family level, the medium-hard superfamily level, and to the hardest fold level. Based on the top-one template protein ranked by RF-Fold, the correct recognition rate is 84.5%, 63.4%, and 40.8% at family, superfamily, and fold levels, respectively. Based on the top-five template protein folds ranked by RF-Fold, the correct recognition rate increases to 91.5%, 79.3% and 58.3% at family, superfamily, and fold levels.ConclusionsThe good performance achieved by the RF-Fold demonstrates the random forest's effectiveness for protein fold recognition.
Highlights
Recognizing the correct structural fold among known template protein structures for a target protein is essential for template-based protein structure modeling
Because random forest selects a random subset of input features to construct each decision tree, the average prediction of a sufficient number of decision trees is robust against the existence of irrelevant features, which partially contributes to its good accuracy
Comparison of random forest with a single decision tree We compared the random forest consisting of 500 decision trees to a single decision tree in terms of the error rate
Summary
Recognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem. Since the number of unique protein structures appears to be limited (e.g., several thousand) according to the structural analysis on all the tertiary protein structures in the Protein Data Bank (PDB) [6], it is possible to identify one correct template structure (fold) for a large portion of target proteins This is the case if a target protein has a significant sequence identity with one of template proteins with a known tertiary structure. Machine learning methods have been used to tackle the problem effectively by casting the fold recognition as a binary classification problem to decide whether or not a target protein shares the same structural fold with a template protein in a protein structure library [6,7,8]
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