Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.
The state-of-the-art protein sequence classification system is SSpro, which obtains 91.74 different unfiltered PDB dataset @cite_13 . However, this system exploits additional information via sequence similarity, and their reported accuracies were only 80 result in even better accuracies.
@cite_13
2131474431
Motivation: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and function and as a component of protein 3D structure prediction pipelines. Most predictors use a combination of machine learning and profiles, and thus must be retrained and assessed periodically as the number of available protein sequences and structures continues to grow. Results: We present newly trained modular versions of the SSpro and ACCpro predictors of secondary structure and relative solvent accessibility together with their multi-class variants SSpro8 and ACCpro20. We introduce a sharp distinction between the use of sequence similarity alone, typically in the form of sequence profiles at the input level, and the additional use of sequence-based structural similarity, which uses similarity to sequences in the Protein Data Bank to infer annotations at the output level, and study their relative contributions to modern predictors. Using sequence similarity alone, SSpros accuracy is between 79 and 80 (79 for ACCpro) and no other predictor seems to exceed 82 . However, when sequence-based structural similarity is added, the accuracy of SSpro rises to 92.9 (90 for ACCpro). Thus, by combining both approaches, these problems appear now to be essentially solved, as an accuracy of 100 cannot be expected for several well-known reasons. These results point also to several open technical challenges, including (i) achieving on the order of 80 accuracy, without using any similarity with known proteins and (ii) achieving on the order of 85 accuracy, using sequence similarity alone. Availability and implementation: SSpro, SSpro8, ACCpr