Improving protein fold recognition by random forest

By Taeho C. Jo, Jianlin Cheng. 2014

54 citations recorded by the source corpus.

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. 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. RF-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. The good performance achieved by the RF-Fold demonstrates the random forest's effectiveness for protein fold recognition.

Subfields: Molecular Biology

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In Molecular Biology

1. Jie Hou
2. Badri Adhikari
3. Jianlin Cheng
4. Jinbo Xu
5. Björn Wallner
6. Yang Zhang
7. Sheng Wang
8. Renzhi Cao

Citations in the corpus

• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, positive): “ally applied to predict the fold of an arbitrary protein and therefore is not practically useful for protein structure prediction. To work around the problem, another kind of machine learning methods [8, 18, 19] converts a multi-fold classification problem into a binary classification problem to pre”