ArticleScientific reports2018
Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Untangling the Complexity of Binding Across Biomolecular Interfaces with Interaction Network Models.Methods in molecular biology (Clifton, N.J.) · 2026Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Detection of protein structural hotspots using AI distillation and explainability: application to the DAX-1 protein.NAR genomics and bioinformatics · 2025Article
- cPEPmatch Webserver: A comprehensive tool and database to aid rational design of cyclic peptides for drug discovery.Computational and structural biotechnology journal · 2024Article
- AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank.International journal of molecular sciences · 2024Article
- PPI-hotspoteLife · 2024Article
- PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.Scientific reports · 2023Article
- Molecular Simulation Study on the Interaction between Porcine CR1-like and C3b.Molecules (Basel, Switzerland) · 2023Article
- Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.PloS one · 2023Article
- Densest subgraph-based methods for protein-protein interaction hot spot prediction.BMC bioinformatics · 2022Article
- Article
- Rapidly predicting Kohn-Sham total energy using data-centric AI.Scientific reports · 2022Article
- Identification of Small-Molecule Inhibitors of Fibroblast Growth Factor 23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho.Journal of chemical information and modeling · 2022Article
- Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules' Growth, Catastrophe, Shortening, and Rescue.Computational and structural biotechnology journal · 2022Article
- Body fat prediction through feature extraction based on anthropometric and laboratory measurements.PloS one · 2022Article
- Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data.Frontiers in cell and developmental biology · 2021Article
- Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania.Risk management and healthcare policy · 2021Article
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3 authors.
Funding
Abstract
Identification of hot spots, a small portion of protein-protein interface residues that contribute the majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more effective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classification algorithms play a vital role in improving the prediction quality.
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