PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

• Published in: Scientific data Vol. 9; no. 1; pp. 548 - 10
• Main Authors: Korlepara, Divya B., Vasavi, C. S., Jeurkar, Shruti, Pal, Pradeep Kumar, Roy, Subhajit, Mehta, Sarvesh, Sharma, Shubham, Kumar, Vishal, Muvva, Charuvaka, Sridharan, Bhuvanesh, Garg, Akshit, Modee, Rohit, Bhati, Agastya P., Nayar, Divya, Priyakumar, U. Deva
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07-09-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/563/606; 639/638/630; Affinity; Animals; Computer applications; Data Descriptor; Datasets; Drug development; Energy; Humanities and Social Sciences; Humans; Learning algorithms; Ligands; Machine Learning; Molecular dynamics; Molecular Dynamics Simulation; multidisciplinary; Protein Binding; Proteins; Proteins - chemistry; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-022-01631-9

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.
Measurement(s)
Binding Affinity
Technology Type(s)
Molecular dynamics simulation/MM-PBSA
Factor Type(s)
3D-protein structures
Sample Characteristic - Organism
NA
Sample Characteristic - Environment
NA
Sample Characteristic - Location
NA