ArticleJournal of cheminformatics2013
Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets.
Article in Journal of cheminformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- Structure-independent machine-learning predictions of the CDK12 interactome.Biophysical journal · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction.RSC advances · 2024Article
- Classifying protein kinase conformations with machine learning.Protein science : a publication of the Protein Society · 2024Article
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
- 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors.Journal of cheminformatics · 2023Article
- CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides.Antibiotics (Basel, Switzerland) · 2023Article
- PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships.Amino acids · 2023Article
- A deep learning based ensemble approach for protein allergen classification.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
backgroundWhile a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).
resultsIn describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data.
conclusionIn this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.
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