Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes
Copyright © 2023 Elsevier B.V. All rights reserved..
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:1871 |
|---|---|
| Enthalten in: |
Biochimica et biophysica acta. Proteins and proteomics - 1871(2023), 6 vom: 01. Nov., Seite 140948 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Nikam, Rahul [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Binding affinity prediction |
|---|
| Anmerkungen: |
Date Completed 13.09.2023 Date Revised 13.10.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.bbapap.2023.140948 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM360669638 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM360669638 | ||
| 003 | DE-627 | ||
| 005 | 20231226083514.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.bbapap.2023.140948 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1202.xml |
| 035 | |a (DE-627)NLM360669638 | ||
| 035 | |a (NLM)37567456 | ||
| 035 | |a (PII)S1570-9639(23)00062-6 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Nikam, Rahul |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 13.09.2023 | ||
| 500 | |a Date Revised 13.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Binding affinity prediction | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Gibbs free energy | |
| 650 | 4 | |a Protein-protein interaction | |
| 650 | 7 | |a Proteins |2 NLM | |
| 700 | 1 | |a Yugandhar, Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Gromiha, M Michael |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biochimica et biophysica acta. Proteins and proteomics |d 2016 |g 1871(2023), 6 vom: 01. Nov., Seite 140948 |w (DE-627)NLM248648349 |x 1878-1454 |7 nnns |
| 773 | 1 | 8 | |g volume:1871 |g year:2023 |g number:6 |g day:01 |g month:11 |g pages:140948 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.bbapap.2023.140948 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 1871 |j 2023 |e 6 |b 01 |c 11 |h 140948 | ||