SPIN2: Predicting sequence profiles from protein structures using deep neural networks

Global analysis of protein folding using massively parallel design, synthesis, and testing

(2017) Gabriel J. Rocklin et al.   SCIENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles

(2014) Zhixiu Li et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

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

(2014) Peng Xiong et al.   Nature Communications

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

(2012) Grant S. Murphy et al.   STRUCTURE

Improving computational protein design by using structure-derived sequence profile

(2010) Liang Dai et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Backbone flexibility in computational protein design

(2009) Daniel J Mandell et al.   CURRENT OPINION IN BIOTECHNOLOGY

Computational protein design as a tool for fold recognition

(2009) Marcel Schmidt am Busch et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Function and structure of inherently disordered proteins

(2008) A Keith Dunker et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Specific interactions for ab initio folding of protein terminal regions with secondary structures

(2008) Yuedong Yang et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction

(2002) Hongyi Zhou et al.   PROTEIN SCIENCE