@article{ding2021samf,
author = {Ding, Wenze and Xu, Qijiang and Liu, Siyuan and Wang, Tong and Shao, Bin and Gong, Haipeng and Liu, Tie-Yan},
title = {SAMF: a Self-adaptive Protein Modeling Framework},
year = {2021},
month = {May},
abstract = {MOTIVATION Gradient descent-based protein modeling is a popular protein structure prediction approach that takes as input the predicted inter-residue distances and other necessary constraints and folds protein structures by minimizing protein-specific energy potentials. The constraints from multiple predicted protein properties provide redundant and sometime conflicting information that can trap the optimization process into local minima and impairs the modeling efficiency. RESULTS To address these issues, we developed a self-adaptive protein modeling framework, SAMF. It eliminates redundancy of constraints and resolves conflicts, folds protein structures in an iterative way, and picks up the best structures by a deep quality analysis system. Without a large amount of complicated domain knowledge and numerous patches as barriers, SAMF achieves the state-of-the-art performance by exploiting the power of cutting-edge techniques of deep learning. SAMF has a modular design and can be easily customized and extended. As the quality of input constraints is ever growing, the superiority of SAMF will be amplified over time. AVAILABILITY The source code and data for reproducing the results is available at https://msracb.blob.core.windows.net/pub/psp/SAMF.zip. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.},
url = {http://approjects.co.za/?big=en-us/research/publication/samf-a-self-adaptive-protein-modeling-framework/},
journal = {Bioinformatics},
}