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DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict...

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Autores principales: Ahmed, Asad, Mam, Bhavika, Sowdhamini, Ramanathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274096/
https://www.ncbi.nlm.nih.gov/pubmed/34290496
http://dx.doi.org/10.1177/11779322211030364
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author Ahmed, Asad
Mam, Bhavika
Sowdhamini, Ramanathan
author_facet Ahmed, Asad
Mam, Bhavika
Sowdhamini, Ramanathan
author_sort Ahmed, Asad
collection PubMed
description Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.
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spelling pubmed-82740962021-07-20 DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity Ahmed, Asad Mam, Bhavika Sowdhamini, Ramanathan Bioinform Biol Insights Original Research Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes. SAGE Publications 2021-07-07 /pmc/articles/PMC8274096/ /pubmed/34290496 http://dx.doi.org/10.1177/11779322211030364 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Ahmed, Asad
Mam, Bhavika
Sowdhamini, Ramanathan
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title_full DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title_fullStr DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title_full_unstemmed DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title_short DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity
title_sort deelig: a deep learning approach to predict protein-ligand binding affinity
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274096/
https://www.ncbi.nlm.nih.gov/pubmed/34290496
http://dx.doi.org/10.1177/11779322211030364
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