Korean, Edit

Protein-Protein Interaction (PPI) Model

Higher category : 【Bioinformatics】 Ligand-Receptor Interaction Database


1. Key Notes

2. Models

3. References



1. Key Notes

⑴ Binding affinities are commonly quantified by the dissociation constant (Kd) or inhibition constant (Ki).

⑵ General consideration

① General properties (e.g., atom types)

② Physico-chemical properties (e.g., excluded volume, partial charge, heavy-atom neighbors, hetero-atom neighbors, hybridization)

③ Pharmacophoric properties (e.g., hydrophobicity, aromaticity, H-bond donor/acceptor, ring member)

⑶ Datasets

① PDBbind database of version 2016

Subset 1. The general set contains all available data : 13,285 protein-ligand complexes

Subset 2. The refined set, the subset of the general set, contains 4,057 high-quality complexes in total

Subset 3. The core 2016 set : 290 complexes from the refined set and for a high-quality benchmark

CASF-2013

③ CSAR-HiQ

CSAR-HiQ_51 : Derived from 176 protein-ligand complexes.

CSAR-HiQ_36 : Derived from 167 protein-ligand complexes.

④ Biolip

⑷ Protein-ligand interactions are common, but protein-protein interaction models are still relatively scarce.



2. Models

⑴ Binding position prediction model

① Example: AlphaFold multimer

② Generally, it is assumed that a ligand-receptor distance of less than 3Å will result in high binding affinity.

⑵ Binding affinity prediction model

Class 1. Sequence-based method

○ Example: DeepDTA, DeepDTAF, DeepFusionDTA, GraphDTA, CAPLA

Class 2. Structure-based method

○ Example: Pafnucy, OnionNet, FAST, IGN, IMCP-SF, GLI


image

Table. 1. The subclasses of structure-based binding affinity prediction models


③ Performance comparison


image

Table 2. Scoring performances of binding affinity models



3. References

CAPLA: improved prediction of protein–ligand binding affinity by a deep learning approach based on a cross-attention mechanism

PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein–Peptide and Protein–Protein Binding Affinity

Structure-based, deep-learning models for protein-ligand binding affinity prediction



Input : 2024.03.31 01:08

results matching ""

    No results matching ""