Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability

By Peng Xiong, Meng Wang, Xiaoqun Zhou, Tongchuan Zhang, Jiahai Zhang, Quan Chen, Haiyan Liu. 2014

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How researchers see this paper

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Computational Protein Design with Deep Learning Neural Networks (2018, methodology): “30,31 Recently, two statistical potentials for protein design have been developed, 32,33 and the ABACUS potential 34 has been successfully used in designing proteins.”
SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018, background): “13,14 The effect of an insufficient energy function can be seen in the accuracies of current systems.”
Achievements and Challenges in Computational Protein Design. (2017, background): “In 2014Liu,Chen and coworkers presented a newCPDmethod with a comprehensive statistical energy function (SEF) and systematic integration of experimental selection for foldability which was proven experimentally on two de novo structurally resolved designs [112].”
Protein sequence design with a learned potential (2020, background): “Design methods that account for flexibility of the protein backbone often use “softer” potentials [49, 54], including statistical potentials derived from data [55, 56, 55, 57, 58].”