ArticleJournal of computer-aided molecular design2013
Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.
Article in Journal of computer-aided molecular design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.Interdisciplinary sciences, computational life sciences · 2026Article
- DeepPepQSAR: all-in-one for comprehensively exploiting the vast molecular diversity space of bioactive peptide universe.Molecular diversity · 2026Article
- Quantitative structure-activity relationship characterization and modeling of length-varying bioactive peptides.Journal of computer-aided molecular design · 2026Article
- Quantitative physics-physiology relationship modeling of human emotional response to Shu music.Frontiers in psychology · 2024Article
- Cold spots are universal in protein-protein interactions.Protein science : a publication of the Protein Society · 2022Article
- Integrated In Silico-In Vitro Identification and Optimization of Bone Morphogenic Protein-2 Armpit Epitope as Its Antagonist Binding Site.The protein journal · 2020Article
- Inverse in silico-in vitro fishing of unexpected paroxetine kinase targets from tumor druggable kinome.Journal of molecular modeling · 2020Article
- PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.Scientific reports · 2020Article
- Rational design of type-IA receptor-derived cyclic peptides to target human bone morphogenic protein 2.Journal of biosciences · 2019Article
- Rational creation and systematic analysis of cervical cancer kinase-inhibitor binding profile.Journal of computer-aided molecular design · 2019Article
- Grafting, Stripping and Stapling of Helical Peptides from the Dimerization Interface of ONFH-Related Bone Morphogenetic Protein-2.The protein journal · 2019Article
- Electrostatic explanation of D1228V/H/N-induced c-Met resistance and sensitivity to type I and type II kinase inhibitors in targeted gastric cancer therapy.Journal of molecular modeling · 2019Article
- Electrostatic mechanism of V600E mutation-induced B-Raf constitutive activation in colorectal cancer: molecular implications for the selectivity difference between type-I and type-II inhibitors.European biophysics journal : EBJ · 2019Article
- Structural Identification and Systematic Comparison of Phorbol Ester, Dioleoylglycerol, Alcohol and Sevoflurane Binding Sites in PKCδ C1A Domain.The protein journal · 2018Article
- Minimalistic predictor of protein binding energy: contribution of solvation factor to protein binding.Biophysical journal · 2015Article
- A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity.Journal of computer-aided molecular design · 2014Article
- Modeling protein-peptide recognition based on classical quantitative structure-affinity relationship approach: implication for proteome-wide inference of peptide-mediated interactions.The protein journal · 2013Article
- The scoring of poses in protein-protein docking: current capabilities and future directions.BMC bioinformatics · 2013Article
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6 authors.
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Abstract
Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.
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