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

By Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, Yaoqi Zhou. 2014

84 citations recorded by the source corpus.

Locating sequences compatible with a protein structural fold is the well‐known inverse protein‐folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy‐optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment‐derived sequence profiles and structure‐derived energy profiles. SPIN improves over the fragment‐derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild‐type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single‐body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks‐lab.org. Proteins 2014; 82:2565–2573. © 2014 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology, Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

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Citations in the corpus

Learning inverse folding from millions of predicted structures (2022, methodology): “Early machine learning approaches in structure-based protein sequence design used fragment-based and energy-based global features derived from structures (Li et al., 2014; O’Connell et al., 2018).”
Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “Therefore, the proxy metric of native sequence recovery — inferring native sequences given their experimentally determined structures—is often used (Li et al., 2014; O’Connell et al., 2018; Wang et al., 2018).”
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB (2022, methodology): “Since SPIN (Li et al., 2014) introduced the TS50 consisting of 50 native structures, it has beening served as a common test set for evaluating different methods (O’Connell et al., 2018; Wang et al., 2018; Chen et al., 2019; Jing et al., 2020; Zhang et al., 2020a; Qi & Zhang, 2020; Strokach et al.,…”
SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018, methodology): “16 The sequence recovery increased from 24% for the fragment-based technique to 30% for SPIN.”