A stacked meta-ensemble for protein inter-residue distance prediction

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Rahman, J
Newton, MAH
Hasan, MAM
Sattar, A
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2022
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Abstract

Predicted inter-residue distances are a key behind recent success in high quality protein structure prediction (PSP). However, prediction of both short and long distance values together is challenging. Consequently, predicted short distances are mostly used by existing PSP methods. In this paper, we use a stacked meta-ensemble method to combine deep learning models trained for different ranges of real-valued distances. On five benchmark sets of proteins, our proposed inter-residue distance prediction method improves mean Local Distance Different Test (LDDT) scores at least by 5% over existing such methods. Moreover, using a real-valued distance based conformational search algorithm, we also show that predicted long distances help obtain significantly better protein conformations than when only predicted short distances are used. Our method is named meta-ensemble for distance prediction (MDP) and its program is available from https://gitlab.com/mahnewton/mdp.

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Computers in Biology and Medicine

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148

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Information and computing sciences

Engineering

Clinical sciences

Deep learning

Ensemble learning

Inter-residue distance

Protein structure prediction

Real-valued distance

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Rahman, J; Newton, MAH; Hasan, MAM; Sattar, A, A stacked meta-ensemble for protein inter-residue distance prediction, Computers in Biology and Medicine, 2022, 148, pp. 105824

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