タンパク質のポケット・キャビティサーチを探索して結合部位を予測する

Prediction of ligand-protein binding site
Jian Yu
Institute for Protein Research
Osaka University

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

2022.11.24

Purpose of predicting protein binding sites
What is binding site?
Binding site is a concave(凹面) region(usually called pocket or cavity) on amacromolecule, such as protein, DNAor RNA surface to which ligands canbind. The binding partner of themacromolecule is often referred to asa ligand.

Why should we search forbinding site?

PDB ID：1ION

1. A measure of different types of ligands that can bind with a molecule.
2. Meaningful to characterize the function of biomolecules.
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Prediction of ligand-protein binding sites
 How to define the region of binding site？
If the distance does not exceed the sum of the van der Waals radii of two atoms plus
0.5 Å, the amino acid residue is regarded as a ligand binding residue.

Distance ≤ 3.75 ÅNCα1.55 Å

1.70 Å0.5 Å

Van der Waals radii table

S. S. Batsanov. Inorganic Materials, Vol. 37, No. 9, 2001, pp. 871–885
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Prediction of ligand-protein binding sites
 Ligand could be biomolecules or ions such as metal ions, nucleic acids,
inorganic or organic small molecules.
MW distribution of bound ligand in PDB

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Evaluation of binding sites prediction methods
 Since the prediction of ligand binding site is a typical dichotomy(二分法) problem froman algorithmic point of view, the evaluation index for the prediction method in this fieldis very similar to the index for evaluating the accuracy of the dichotomy algorithm

A typical dichotomy example: Taiji

陽陰 The common ligand binding site prediction indicators are sensitivity (Sen), accuracy(Acc), specificity (Spe), precision (Pre), and Mattheu’s correlation coefficients (MCC),
which are defined as below:

𝐴𝐴𝐴𝐴𝐴𝐴 =𝑆𝑆𝑆𝑆𝑆𝑆 =𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹

𝑇𝑇𝑇𝑇 + 𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑆𝑆 =𝑀𝑀𝑀𝑀𝑀𝑀 =𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹

𝑃𝑃𝑃𝑃𝑃𝑃 =𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹

(𝑇𝑇𝑇𝑇 � 𝑇𝑇𝑇𝑇) − (𝐹𝐹𝐹𝐹 � 𝐹𝐹𝐹𝐹)(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹) � (𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹) � (𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹) � (𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)

TP: True Positive ; TN: True Negative; FP: False Positive ; FN: False Negative
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Classifications of binding site prediction methods

 A series of prediction methods have been developed
based on different information:
1. Sequence information
2. Structural templates
3. Three-dimensional structures
 Four types of prediction method
1. 3D structure-based
2. Template similarity-based
3. Traditional machine learning-based
4. Deep learning-based methods
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1. 3D structure-based prediction methods
 Two kinds of 3D structure-based prediction methods:
1. Geometry-based method: only used the 3D structure to search for pockets.
2. Energy-based method: find pockets with specific probe sphere and thencalculate the energy potentials.
• Geometry-based method 1. Grid system scanning; 2. Probe sphere filling; 3. Special
algorithms such as alpha-shape
POCKET, LIGSITE, LIGSITEcsc

PASS

Active Site Points
Probe sphere

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CAST, CASTp

POCASAA series of triangles
to define pockets

A probe of variable size
rolls along protein surface

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1. 3D structure-based prediction methods
 Two kinds of 3D structure-based prediction methods:
1. Geometry-based method: only used the 3D structure to search for pockets.
2. Energy-based method: calculate the energy with specific probe sphere.
• Energy-based method calculates the interaction energy with different probe sphere:
1. carbon probe; 2. phosphate probe; 3. methyl group and so on.
 Q-SiteFinder: use methyl group as a probe

Predicted binding sitesof different ligands

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 FTSite: 16 different small molecule probes

Cluster the predicted binding sites

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2. Template similarity-based prediction method The ligand binding site can be predicted using the known proteins as templates toobtain similar characteristics in the query protein. Template similarity-based
prediction methods mainly include two types:
1. Structure template-based methods; 2. Sequence template-based methods• Structure template-based methods
Search for the most similar proteins with the query structure in databases and thentransfer the known ligand binding site onto the target protein.
 3DLigandSite: Find the 25 template proteins with the highest similarity and the cluster with themost template ligands was chosen as the basis for the prediction of binding site.

Overview of 3DLigandSite process

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2. Template similarity-based prediction method• Sequence template-based methods
The alignment tool is used to align the sequence and then the template is selectedaccording to the similarity. Finally, the ligand-binding residues of target protein are
presumed by referring the known ligand-binding residues on the aligned regions.
 S-SITE: screens similar sequences from the query protein according to the alignment
result in the BioLip database.

Consensus voting (合意投票) is used to score the alignment results of the templates
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3. Traditional machine learning-based Predicting protein ligand binding sites is a typical dichotomous problem that machinelearning algorithm can be used to solve this problem.
Some traditional machine learning algorithms
used for prediction:
1. Logistic regression;
2. K-nearest neighbor;
3. Support vector machine (SVM)

Support vector machine (SVM)

A simple schematic of SVM A
hyperplane divides the points into
two categories.

 COACH: combine five prediction methods and apply SVM to binding site prediction.

Flowchart of the COACH algorithm
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4. Deep learning-based methods Deep learning is a complex machine learning technique that can classify the unlabeleddata by learning from the existing dataset.
Three kinds of networks usually used in deep learning:
1. Convolutional neural networks (CNNs)
2. Deep belief networks (DBNs)
3. Self-encoding neural networks
A typical CNN model

Hidden Layers are used to generate the classification
result (multiple convolutional layers and pooling layers
can be set in a CNN).
PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

DeepCSeqSite: use the seven characteristics to encode
amino acids to generate a feature Map as an input ofDCNN. The output is the predicted binding residues.

Outline of DeepCSeqSite
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Prediction of ligand-protein binding siteAccess to binding site prediction methods
1. Web servers for binding site prediction
FTSite: Energy-based
POCASA: Geometry-based
CB-docker2: Geometry-based
COACH: Traditional machine learning (SVM)
DEEPSITE: Deep learning
2. Open-source programs
DeepCSeqSite: Deep Learning
3. Stand-alone binary
Download from website or Contact authors
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Usage and results of prediction methods
Energy-based method: FTSite, https://ftsite.bu.edu/• Test example
PDB ID 1ION, MW= 26960
ADP-binding complex•••••Input: PDB ID or PDB file
Output files: Pymol pse file
Easy to use, friendly interface
Time consumption: 1 hour
Online display module
multiple molecular probes

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

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Usage and results of prediction method
 Geometry-based method: POCASA, http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html
 Detail of online server•••••

Input: PDB ID, PDB file ormmcif file
Output files downloadable
Time consumption: 0.7 sec
Easy to use
Retrievable URL link

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

 Additional function• 3D model of predictedpocket• Other information:
volume, depth, pocket
center, Top N pocket.

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Usage and results of prediction methods
 Geometry-based method: CB-DOCK2, https://cadd.labshare.cn/cb-dock2/php/index.php• Input: PDB file • Time consumption: several minutes • Input of ligand is mandatory• Easy to use, friendly interface • Various module of display • Blind dock available

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Purpose of predicting protein binding sites
 Machine learning based method: COACH, https://seq2fun.dcmb.med.umich.edu/COACH/•

Input: PDB file or sequence•

Output files downloadable•

Outstanding performance•

Various information•

Retrievable URL link•

Combinedresults

withother five methods•

Online display module•

Predicted ligands for target
protein•Information of template
proteins•

Extreme time consumption(10 hours)

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

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Usage and results of prediction methods Deep learning based method: DEEPSITE, https://playmolecule.com/deepsite/••••
Input: PDB ID or PDB file•
Output files downloadable(only coordinates of pocket center) •No retrievable URL link•

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

Easy to use, friendly interface
Time consumption: 2-3 minutes
Only center point of binding site is given
Some cases doesn’t work

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Prediction methods for ligand-protein binding sites
Advantage

Disadvantage• Stronglydependon• Straightforward algorithmstructure• Various methods has been
3D structure-based• Unable to discover thedeveloped for long years
binding site when it will be• 3D model of binding siteinduced in holo-stateTemplate-based• Depend on the templates• Utilize existing databaseof high similarity• Structure is not always• Priori
information
tonecessarybinding site

Machine learning• Flexibly combine different
information• Sample imbalance• Excellent performance

Deep learning• Outstanding performance• Extremely• Many huge database usablecosts

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hightraining

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Prediction of ligand-protein binding sites

Thanks for your attention!

PF-UAタンパク質結晶構造解析グループ第7回中級者講習会

2022.11.24