DLPAlign: A Deep Learning based Progressive Alignment for Multiple Protein Sequences

Kuang, Mengmeng

This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment. We trained a decision-making model based on the convolutional neural networks and bi-directional long short term memory networks, and based on this model, we progressively aligned the input sequences by calculating different posterior probability matrixes. To test the accuracy of this approach, we have implemented a multiple sequence alignment tool called DLPAlign and compared its performance with eleven leading alignment methods on three empirical alignment benchmarks (BAliBASE, OXBench and SABMark). Our results show that DLPAlign can get the best total-column scores on the three benchmarks. When evaluated against the 711 low similarity families with average PID ≤ 30%, DLPAlign improved about 2.8% over the second-best MSA software. Besides, we also compared the performance of DLPAlign and other alignment tools on a real-life application, namely protein secondary structure prediction on four protein sequences related to SARS-COV-2, and DLPAlign provides the best result in all cases.