Biological Language Model. Qiwen Dong

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       3.4.4 Performance comparison by using the BRNN method

      In recent years, deep learning-based methods for protein secondary structure prediction have achieved significant improvements.⁴⁸ One of the most important advantages of deep learning methods is that they can capture both neighboring and long-range interactions, which could avoid the shortcomings of sliding window methods with handcrafted window size. For example, Heffernan et al.⁴² have achieved state-of-the-art performances by using the long short-term memory (LSTM) bidirectional recurrent neural networks. Therefore, to exclude the potential influence of the handcrafted window size, we also perform an assessment by using the bidirectional recurrent neural networks (BRNN) with long short-term memory cells. The model used here is similar to the model used in Heffernan’s work,⁴² as shown in Fig. 3-4, which contains two BRNN layers with 256 LSTM cells and two fully connected (dense) layers with 1024 and 512 nodes, and it is implemented based on the open-sourced deep learning library TensorFlow.⁵⁹

      The corresponding comparison results of the 16 selected encoding methods are shown in Table 3-4. From the overall view, the BRNN-based method was found to have better performance compared with the Random Forests-based method, but there are also some specific similarities and differences between them. For binary encoding methods, one-hot encoding still shows the best performance, which once again confirms the information loss of the one-hot (6-bit) and the binary 5-bit encoding methods. For the physicochemical property encodings, the Meiler parameters do not perform as well as the Acthely factors, suggesting that the Acthely factors are more efficient for deep learning methods. For the evolution-based encodings, the PSSM encoding achieves the best accuracy, while the HMM encoding just achieves as much accuracy as those position-independent encodings (PAM250 and BLOSUM62). The difference could be due to the different levels of homologous sequence identity. The HMM encoding is extracted from the UniProt20 database with 20% sequence identity, while the PSSM encoding is extracted from the UniRef90 database with 90% sequence identity. Therefore, for a certain protein sequence, its MSA from the UniProt20 database mainly contains remote homologous sequences, while its MSA from the UniRef90 database usually contains more homologous sequences. From the results in Table 3-4, the evaluation information of homologous sequences is more powerful for distinguishing different protein secondary structures than that of remote homologous sequences. For the structure-based encodings, the Micheletti potentials have much better performance when the BRNN method is used than when the Random Forests method is used. For machine-learning encodings, the ProtVec and ProtVec-3mer achieve significantly better performance compared with the values given in Table 3-4, which demonstrates the potential of machine-learning encoding. It is worth noting that ProtVec-3mer has better performance than ProtVec on the BRNN algorithm, corresponding to the authors’ recent work.⁴⁹ Overall, for the deep learning algorithm BRNN, the position-dependent PSSM encoding still performs best among all encoding methods. For the position-independent encoding methods, the Micheletti potentials achieve the best performance, which demonstrates that the structure-related information has application potential in protein structure and function studies.

      Figure 3-4 The architecture of the long short-term memory (LSTM) bidirectional recurrent neural networks for protein secondary structure prediction.

Table 3-4 Protein secondary structure prediction accuracy of 16 amino acid encoding methods by using the BRNN method.