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Cited by 133Computational Drug Discovery MethodsProtein Structure and DynamicsMachine Learning in Bioinformatics

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DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

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Pu, Limeng, Govindaraj, Rajiv Gandhi, Lemoine, Jeffrey Mitchell, Wu, Hsiao-Chun, Brylinski, Michal·2019·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1006718PMC PMC6375647
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Abstract

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.

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Figures

Fig 1 Voxelization of ligand-binding pockets. (A) Starting from a ligand-protein complex, a sphere centered on the geometric center of the ligand is created and filled with a 3D grid. Grid points (B) overlapping with the protein, (C) too far away from the protein, and (D) disconnected from the main grid structure are removed. Points to be removed are shown in red in B-D. Subsequently, (E) statistical potentials for ligand-protein interactions are calculated at each grid point and (F) the pocket principal axes are aligned to the Cartesian axes. (G) The voxel representation of a ligand-binding pocket is used as an input in deep learning.
Fig 1 Voxelization of ligand-binding pockets. (A) Starting from a ligand-protein complex, a sphere centered on the geometric center of the ligand is created and filled with a 3D grid. Grid points (B) overlapping with the protein, (C) too far away from the protein, and (D) disconnected from the main grid structure are removed. Points to be removed are shown in red in B-D. Subsequently, (E) statistical potentials for ligand-protein interactions are calculated at each grid point and (F) the pocket principal axes are aligned to the Cartesian axes. (G) The voxel representation of a ligand-binding pocket is used as an input in deep learning.CC BY · PubMed Central
Fig 2 Structure of a convolutional neural network in DeepDrug3D. The network consists of (A) an input voxel followed by (B) two convolutional layers with leaky ReLu activation functions, and (C) a series of dropout, pooling, fully connected and softmax layers.
Fig 2 Structure of a convolutional neural network in DeepDrug3D. The network consists of (A) an input voxel followed by (B) two convolutional layers with leaky ReLu activation functions, and (C) a series of dropout, pooling, fully connected and softmax layers.CC BY · PubMed Central
Fig 3 An example of class-activation map (CAM) grids within ligand-binding sites. ATP bound to the human TRPV4 ankyrin repeat domain is shown as sticks colored by atom type (C–green, O–red, N–blue, and P–orange). Selected grid points are represented by spheres whose size and color depend on the assigned CAM value according to the scale in the top left corner. Two residues are shown, R248 (orange) forming a hydrogen bond with the ribose moiety of ATP and a more distant residue F272 (purple). A dotted black line represents a hydrogen bond, whereas dotted red lines mark the distance between protein residues and the highest-scoring grid point.
Fig 3 An example of class-activation map (CAM) grids within ligand-binding sites. ATP bound to the human TRPV4 ankyrin repeat domain is shown as sticks colored by atom type (C–green, O–red, N–blue, and P–orange). Selected grid points are represented by spheres whose size and color depend on the assigned CAM value according to the scale in the top left corner. Two residues are shown, R248 (orange) forming a hydrogen bond with the ribose moiety of ATP and a more distant residue F272 (purple). A dotted black line represents a hydrogen bond, whereas dotted red lines mark the distance between protein residues and the highest-scoring grid point.CC BY · PubMed Central
Fig 4 ROC plots evaluating the performance of various algorithms to classify ligand-binding sites. DeepDrug3D is compared to volume- and shape-based approaches, as well as a classifier employing the histogram of gradients with principal component analysis (HOG/PCA) for (A) nucleotide- and (B) heme-binding pockets. The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.
Fig 4 ROC plots evaluating the performance of various algorithms to classify ligand-binding sites. DeepDrug3D is compared to volume- and shape-based approaches, as well as a classifier employing the histogram of gradients with principal component analysis (HOG/PCA) for (A) nucleotide- and (B) heme-binding pockets. The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.CC BY · PubMed Central
Fig 5 ROC plots evaluating the performance of DeepDrug3D and other methods to classify ligand-binding sites. DeepDrug3D is compared to pocket matching with G-LoSA, molecular docking with Vina, and sequence signature detection with ScanProsite for (A) nucleotide- and (B) heme-binding pockets. The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.
Fig 5 ROC plots evaluating the performance of DeepDrug3D and other methods to classify ligand-binding sites. DeepDrug3D is compared to pocket matching with G-LoSA, molecular docking with Vina, and sequence signature detection with ScanProsite for (A) nucleotide- and (B) heme-binding pockets. The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.CC BY · PubMed Central
Fig 6 ROC plots evaluating the performance of DeepDrug3D against the peptidase dataset. The performance is assessed individually for five groups of enzymes, serine endopeptidases (EC 3.4.21), cysteine endopeptidases (EC 3.4.22), aspartic endopeptidases (EC 3.4.23), metalloendopeptidases (EC 3.4.24), and threonine endopeptidases (EC 3.4.25). The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.
Fig 6 ROC plots evaluating the performance of DeepDrug3D against the peptidase dataset. The performance is assessed individually for five groups of enzymes, serine endopeptidases (EC 3.4.21), cysteine endopeptidases (EC 3.4.22), aspartic endopeptidases (EC 3.4.23), metalloendopeptidases (EC 3.4.24), and threonine endopeptidases (EC 3.4.25). The x-axis shows the false positive rate (FPR) and the y-axis shows the true positive rate (TPR). The gray area represents a random prediction.CC BY · PubMed Central
Fig 7 Distribution of class-activation map (CAM) scores for specific ligand-protein interactions. Three interaction types, hydrogen bonds, aromatic and hydrophobic contacts are reported by the LPC program for (A) nucleotide- and (B) heme-binding pockets. For each interaction type, grid points are divided into two groups, those points in close proximity to residues forming a particular contact and the remaining points that are closer to residues not forming these interactions. The last pair of violins are plotted for grid points randomly assigned into two groups, irrespectively of any ligand-protein interactions. Horizontal blue lines represent median values, and whiskers extend to the most extreme non-outlier data points.
Fig 7 Distribution of class-activation map (CAM) scores for specific ligand-protein interactions. Three interaction types, hydrogen bonds, aromatic and hydrophobic contacts are reported by the LPC program for (A) nucleotide- and (B) heme-binding pockets. For each interaction type, grid points are divided into two groups, those points in close proximity to residues forming a particular contact and the remaining points that are closer to residues not forming these interactions. The last pair of violins are plotted for grid points randomly assigned into two groups, irrespectively of any ligand-protein interactions. Horizontal blue lines represent median values, and whiskers extend to the most extreme non-outlier data points.CC BY · PubMed Central
Fig 8 Two examples of accurately classified ligand-binding pockets. (A and B) A GDP-binding protein, the signal recognition particle receptor ftsY from E. coli, and (C and D) a heme-binding protein, the C-terminal domain of the S. enterica PduO protein. (A and C) Experimental complex structures and (B and D) close-ups of binding sites with high-scoring class-activation map (CAM) grid points. GDP and heme are shown as green sticks colored by atom type (C–green, O–red, N–blue, P and Fe–orange), whereas grid points are represented by spheres whose size and color depend on CAM values according to the scale shown in Fig 3. Hydrogen bonds are indicated by dashed black lines. Selected binding residues are labeled and colored by the interaction type (hydrogen bond–orange, aromatic–gray, hydrophobic–blue, aromatic and hydrophobic–cyan, hydrogen bond, aromatic and hydrophobic–magenta).
Fig 8 Two examples of accurately classified ligand-binding pockets. (A and B) A GDP-binding protein, the signal recognition particle receptor ftsY from E. coli, and (C and D) a heme-binding protein, the C-terminal domain of the S. enterica PduO protein. (A and C) Experimental complex structures and (B and D) close-ups of binding sites with high-scoring class-activation map (CAM) grid points. GDP and heme are shown as green sticks colored by atom type (C–green, O–red, N–blue, P and Fe–orange), whereas grid points are represented by spheres whose size and color depend on CAM values according to the scale shown in Fig 3. Hydrogen bonds are indicated by dashed black lines. Selected binding residues are labeled and colored by the interaction type (hydrogen bond–orange, aromatic–gray, hydrophobic–blue, aromatic and hydrophobic–cyan, hydrogen bond, aromatic and hydrophobic–magenta).CC BY · PubMed Central

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Metadata

Authors
Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M.
Affiliations
Louisiana State University (US)
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2019-02-04
Volume
15
Issue
2
Pages
e1006718
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1006718
PMC
PMC6375647
OpenAlex
W2914877211
Cited by
133
References
96
Citations by year
2026: 7 · 2025: 11 · 2024: 23 · 2023: 14 · 2022: 28 · 2021: 21 · 2020: 21 · 2019: 8
FWCI
13.24
Type
journal article
Language
en
Update notices
new_version (2019-02-14)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Civil War Book Review (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institute of General Medical Sciences (R35GM119524)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
convolutional neural networks, nucleotide binding site, heme binding site, steroid binding proteins, protein structure
MeSH terms
Deep Learning, Algorithms, Binding Sites, Ligands, Models, Molecular, Protein Binding, Proteins, Neural Networks, Computer, Computational Biology, Databases, Protein

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