ArticleComputational and structural biotechnology journal2022
Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 21 citations in OpenAlex.
- Enhanced formulation of precision probiotics through active machine learning.Biology methods & protocols · 2026Article
- SAGE-prot: scoring-assisted generative exploration for multi-objective protein design.Briefings in bioinformatics · 2025Article
- Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants.Journal of chemical information and modeling · 2024Article
- Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing.Scientific reports · 2024Article
- An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling.Computational and structural biotechnology journal · 2023Review
- Reversal of the unique Q493R mutation increases the affinity of Omicron S1-RBD for ACE2.Computational and structural biotechnology journal · 2023Article
- Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors.BMC bioinformatics · 2022Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.4 Å, and 3D-SPIEs_8Å). While the PCgrades and PCspairs include general and statistical information in physicochemical properties in single and pairwise amino acids respectively, the 3D-SPIEs include specific and quantum-mechanical information with parameterized quantum mechanical calculations (FMO2-DFTB3/D/PCM). To evaluate the protein descriptors, we made prediction models with the new descriptors and previously developed descriptors for diverse protein datasets including protein expression and binding affinity change in SARS-CoV-2 spike glycoprotein. As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance (
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