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Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN)

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Abstract

Three-dimensional protein structure prediction is one of the major challenges in bioinformatics. According to recent research findings, real-valued distance prediction plays a vital role in determining the unique three-dimensional protein structure. This paper proposes a novel methodology involving a deep residual dense network (DRDN) for predicting protein real-valued distance. The features extracted from the given query protein sequence and its corresponding homologous sequences are used for training the model. Multi-aligned homologous sequences for each query protein sequence are retrieved from five different databases using DeepMSA, HHblits, and HITS_PR_HHblits methods. The proposed method yielded outcomes of 3.89, 0.23, 0.45, and 0.63, respectively, corresponding to the evaluation metrics such as Absolute Error, Relative Error, High-accuracy Pairwise Distance Test (PDA), and Pairwise Distance Test (PDT). Further, the contact map is computed based on CASP criteria by converting the predicted real-valued distance, and it is evaluated using the precision metric. It is observed that precision of long-range top L/5 contact prediction on the CASP13 dataset by the proposed method, RaptorX, Zhang, trRosetta, **boXu & **Lu, and Deepdist are 0.834, 0.657, 0.70, 0.785, 0.786, and 0.812, respectively. Also, Top-L/5 contact prediction on the CASP14 dataset evaluated using average precision resulted in 0.847, 0.707, 0.752, 0.783, 0.792, 0.817, and 0.825 respectively, corresponding to the proposed method, Zhang, RaptorX, trRosetta, Deepdist, **boXu & **Lu, and Alphafold2.

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  1. Department of Computer Science and IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, Ernakulam, Kerala, India

    S. Geethu & E. R. Vimina

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  1. S. Geethu
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  2. E. R. Vimina
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Geethu, S., Vimina, E.R. Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN). Protein J 41, 468–476 (2022). https://doi.org/10.1007/s10930-022-10067-4

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