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Cited by 172Topological and Geometric Data AnalysisComputational Drug Discovery MethodsMachine Learning in Bioinformatics

Integration of element specific persistent homology and machine learning for protein‐ligand binding affinity prediction

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Cang, Zixuan, Wei, Guo‐Wei·2018·International Journal for Numerical Methods in Biomedical Engineering
DOI 10.1002/cnm.2914
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Abstract

Protein‐ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein‐ligand binding affinities is vital to rational drug design and the understanding of protein‐ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology‐based machine‐learning paradigm outperforms other existing methods in protein‐ligand binding affinity predictions. ESPH reveals protein‐ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein‐ligand hydrophobic interactions are extended to 40Å away from the binding site, which has a significant ramification to drug and protein design.

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  • The integration of ESPH and machine learning gives rise to some of the most accurate predictions of protein-ligand binding affinities and mutation induced protein stability changes.

    “The integration of ESPH and machine learning gives rise to some of the most accurate predictions of protein-ligand binding affinities 27,28 and mutation induced protein stability changes. 26,28”

    From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [27]

  • Characterizing non-additive contributions of functional groups helps machine learning-based scoring functions consistently maintain accuracy in binding affinity predictions across a variety of protein-ligand complexes.

    “Such a characterization can help machine learning-based SFs consistently maintain their accuracy in binding affinity predictions for a variety of protein–ligand complexes [19–23].”

    From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [21]

  • Both the paper's graph-theory-based models and algebraic-topology-based models achieve top-class performance with ensemble-of-trees methods.

    “Both our graph theory based models [20, 62] and algebraic topology based models [21, 23] achieve top-class performance with the ensemble of trees methods.”

    From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [21]

  • Fixed-length features derived from element specific persistent homology barcodes can be used with ensemble of trees and deep learning neural networks.

    “These fixed length features can be used with powerful machine learning methods such as the ensemble of trees and deep learning neural networks [21, 22].”

    From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [21]

  • Element specific persistent homology computation on all carbon atoms describes the hydrophobic network or structural basis of a molecule, while computation on nitrogen and oxygen atoms characterizes hydrophilic networks.

    “specific persistent homology computation on the collection of all carbon atoms describes the hydrophobic network or the structural basis of the molecule while computation on the nitrogen and oxygen atoms characterizes the hydrophilic networks [21].”

    From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [21]

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Authors
Cang, Z., & Wei, G.
Affiliations
Michigan State University (US)
Corresponding author
Wei, Guo‐Wei
ORCID
Venue
International Journal for Numerical Methods in Biomedical Engineering
Publisher
Wiley
Published
2017-08-16
Volume
34
Issue
2
ISSN
  • 2040-7939 (ISSN-L)
  • 2040-7947
DOI
10.1002/cnm.2914
OpenAlex
W2733638363
Cited by
172
References
52
Citations by year
2026: 9 · 2025: 22 · 2024: 16 · 2023: 25 · 2022: 22 · 2021: 25 · 2020: 23 · 2019: 18 · 2018: 10 · 2017: 2
FWCI
10.88
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal for Numerical Methods in Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Science Foundation (Division of Information and Intelligent Systems, 1302285 and Directorate for Mathematical Sciences, 1721024)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein-ligand binding affinity prediction, topological data analysis, hydrophobic interactions, binding mechanism, drug design
MeSH terms
Machine Learning, Hydrogen Bonding, Ligands, Protein Binding, Proteins, Databases, Protein

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