ArticleScientific reports2017
Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- How does the same ligand activate signaling of different receptors in TNFR superfamily: a computational study.Journal of cell communication and signaling · 2023Article
- Error and optimism bias regularization.Journal of big data · 2023Article
- Computational Assessment of Protein-protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes.Genomics, proteomics & bioinformatics · 2021Article
- Classification of protein-protein association rates based on biophysical informatics.BMC bioinformatics · 2021Article
- Assessing the Role of Calmodulin's Linker Flexibility in Target Binding.International journal of molecular sciences · 2021Article
- A multiscale study on the mechanisms of spatial organization in ligand-receptor interactions on cell surfaces.Computational and structural biotechnology journal · 2021Article
- Understand the Functions of Scaffold Proteins in Cell Signaling by a Mesoscopic Simulation Method.Biophysical journal · 2020Article
- Understanding the Impacts of Conformational Dynamics on the Regulation of Protein-Protein Association by a Multiscale Simulation Method.Journal of chemical theory and computation · 2020Article
- Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association.Biomolecules · 2020Article
- Single-molecule dynamics of Dishevelled at the plasma membrane and Wnt pathway activation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches.The Journal of physiology · 2020Review
- Article
- A Systematic Test of Receptor Binding Kinetics for Ligands in Tumor Necrosis Factor Superfamily by Computational Simulations.International journal of molecular sciences · 2020Article
- Dynamical control by water at a molecular level in protein dimer association and dissociation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Multiscale simulation unravel the kinetic mechanisms of inflammasome assembly.Biochimica et biophysica acta. Molecular cell research · 2020Article
- A computational model for understanding the oligomerization mechanisms of TNF receptor superfamily.Computational and structural biotechnology journal · 2020Article
- A Multiscale Model for the Self-Assembly of Coat Proteins in Bacteriophage MS2.Journal of chemical information and modeling · 2019Article
- Prediction of Sub-Monomer A2 Domain Dynamics of the von Willebrand Factor by Machine Learning Algorithm and Coarse-Grained Molecular Dynamics Simulation.Scientific reports · 2019Article
- Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins.Physical chemistry chemical physics : PCCP · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.