Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus’s butyrylcholinesterase inhibitors.
Abstract: Machine learning (ML), an emerging field in drug design, has the potential to predict toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.
Publication Date: 2021-10-25 PubMed ID: 34696689DOI: 10.1080/07391102.2021.1994012Google Scholar: Lookup
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- Journal Article
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- N.I.H.
- Extramural
Summary
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The researchers developed and validated a machine learning model to predict inhibitors of the horse enzyme butyrylcholinesterase (BChE), which has similar properties to human BChE. This model aims to improve the prediction of potential drug toxicity and suitable drug inhibitors for BChE.
Homology Model and Docking Protocol
The researchers started with the development of a homology model that can mimic the actual behavior of horse BChE enzyme. This model is based on the similarity or homology between the horse and human BChE.
- A docking protocol was also developed to forecast how potential drugs can bind to the enzyme.
- The validation of the model was determined by comparing the predicted interactions with the results of a similar experiment done on human BChE. A high degree of similarity indicated that the model has been validated.
- The researchers used a tool called Cavityplus to determine the active site residues of the enzyme, and this information was used to generate a grid box for docking.
Scoring Function
After developing and validating the model, the next step involved creating a scoring function (SF).
- The SF was used to score potential inhibitors based on how well they could bind to the BChE enzyme.
- The researchers tested for the validity of the SF by comparing its scores to known BChE inhibitors.
- The binding energies obtained from SF were correlated with IC values of inhibitors, although the initial results suggested there was poor predictivity from the native SF.
Machine Learning-Based Scoring Functions
Due to the poor predictivity of the native SF, the researchers decided to develop machine learning models to improve the scoring function.
- Information about protein-ligand binding energy, interaction profile, and ligand descriptors were fed into these machine learning algorithms.
- Several models including classification, regression, random forest and extra tree were developed and validated.
- The research team found that their machine learning models gave better results compared to the Autodock scoring function, with an improved area under the curve for the receiver operating characteristic (ROC).
- The final validated model was made available to the public through a web application and a Python library.
Cite This Article
APA
Ganeshpurkar A, Singh R, Kumar D, Gutti G, Sardana D, Shivhare S, Singh RB, Kumar A, Singh SK.
(2021).
Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus’s butyrylcholinesterase inhibitors.
J Biomol Struct Dyn, 40(24), 13693-13710.
https://doi.org/10.1080/07391102.2021.1994012 Publication
Researcher Affiliations
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Institute of Pharmacy Harish Chandra, Post Graduate College, Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
MeSH Terms
- Horses
- Humans
- Animals
- Butyrylcholinesterase / metabolism
- Ligands
- Molecular Docking Simulation
- Cholinesterase Inhibitors / pharmacology
- Cholinesterase Inhibitors / chemistry
- Machine Learning
Grant Funding
- P41 GM103311 / NIGMS NIH HHS
Citations
This article has been cited 2 times.- Kamguia SD, Njabon EN, Patouossa I, Emadak A, Forlemu N. A Comparative Analysis of Cockroach and Mosquito, Octopamine Receptor Homologues Produced Using Chimera, Swiss-Model, and AlphaFold Molecular Modeling Tools. ACS Omega 2025 Mar 4;10(8):7907-7919.
- Singh GK, Kumari B, Das N, Zaman K, Prasad P, Singh RB. Design, synthesis, molecular docking and pharmacological evaluation of some thiadiazole based nipecotic acid derivatives as a potential anticonvulsant and antidepressant agents. 3 Biotech 2024 Mar;14(3):71.
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