Protein structure reconstruction from nuclear magnetic resonance (NMR) experiments largely relies on computational algorithms, such as matrix completion (MC). As an efficient low-rank MC method, scaled alternating steepest descent (ScaledASD) algorithm has been used in image processing successfully, which inspired us to apply it into protein structure calculation. To this end, we established the corresponding initial distance matrix according to the characteristics of proteins, experimental NMR data, and the triangle inequality estimation. After obtaining a raw structure by ScaledASD, we added several postprocedures, including chirality refinement, distance lower (upper) bound refinement, and water refinement to improve the accuracy of the structures. To evaluate our results, we compared the root-mean-square deviation (RMSD), template modeling score (TM-score), Ramachandran plot, and secondary structure with reference structures deposited from Protein Data Bank. We conclude that our method is consistent with the popularly used methods, which illustrates ScaledASD is a promising algorithm for protein structure calculation.
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