Prediction of GPCR activity using machine learning

Yadav, Prakarsh; Mollaei, Parisa; Cao, Zhonglin; Wang, Yuyang; Farimani, Amir Barati

doi:10.1016/j.csbj.2022.05.016

Cited by 20 publications

(20 citation statements)

References 62 publications

(58 reference statements)

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“…However, none of them have reported results to predict the amino acid's position in the molecular dynamics of GPRCs. Yadav et al (2020) [22] developed 3 machine learning approaches to predict the conformation state of GPCR proteins obtaining similar errors ( MAE: 0.0715 -0.0897 Å and RMSD 0.1291 -0.1449 Å ) as it reported in this work but this developments was on no dynamics simulations.…”

Section: Introductionsupporting

confidence: 66%

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

López-Correa

König

Vellido

2022

Frontiers in Artificial Intelligence and Applications

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G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell transmembrane proteins, responsible for numerous biological processes. From a practical viewpoint around 34% of the drugs approved by the US Food and Drug Administration target these receptors. They can be analyzed from their simulated molecular dynamics, including the prediction of their behavior in the presence of drugs. In this paper, the capability of Long Short-Term Memory Networks (LSTMs) are evaluated to learn and predict the molecular dynamic trajectories of a receptor. Several models were trained with the 3D position of the amino acids of the receptor considering different transformations on the position of the amino acid, such as their centers of mass, the geometric centers and the position of the α–carbon for each amino acid. The error of the prediction of the position was evaluated by the mean average error (MAE) and root-mean-square deviation (RMSD). The LSTM models show a robust performance, with results comparable to the state-of-the-art in non-dynamic 3D predictions. The best MAE and RMSD values were found for the mass center of the amino acids with 0.078 Å and 0.156 Å respectively. This work shows the potential of LSTM to predict the molecular dynamics of GPRCs.

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Section: Introductionsupporting

confidence: 66%

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

López-Correa

König

Vellido

2022

Frontiers in Artificial Intelligence and Applications

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“…With that features, ML models could provide activation prediction accuracy of 93.69% for classification task. 32 However, since the main focus of this study is regression task (i.e activity level prediction), we fine-tuned different combinations of structural features to achieve higher accuracy. We found out that ML models trained with only conserved features can provide higher activation prediction accuracy.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…To interpret the high-dimensional data, machine learning (ML) models can be used to gain physical insight into the correlation between conformational structures of GPCRs and their activation states. [29][30][31][32][33] In this study, we develop an ML model based on available experimental information on GPCRs to predict the activity level of a given GPCR structure. Using this model, we evaluate the activity levels of thousands of trajectories of β 2 AR receptor and predict the transition pathways between states of activation by ordering the activity levels corresponding to protein structural features.…”

Section: Introductionmentioning

confidence: 99%

Activity Map and Transition Pathways of G Protein Coupled Receptor Revealed by Machine Learning

Mollaei

Farimani

2022

Preprint

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Approximately, one-third of all FDA-approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning (ML) model to predict activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified by using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method we will be able to design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.

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“…Recently, GNNs have been shown to be a powerful tool for molecule feature representation and property prediction, and have received a significant amount of attention. − At the feature representation stage, GNNs directly work on the molecular structure. It treats the molecule as a graph and utilizes an adjacent matrix to encode the bond edge and connectivity, as well as a node feature matrix to encode the atom and related properties.…”

Section: Introductionmentioning

confidence: 99%

Predicting CO₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

Jian

Wang

Farimani

2022

ACS Sustainable Chem. Eng.

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Ionic liquids (ILs) provide a promising solution for CO2 capture and storage to mitigate global warming. However, identifying and designing the high-capacity IL from the giant chemical space require expensive and exhaustive simulations and experiments. Machine learning (ML) can accelerate the process of searching for desirable ionic molecules through accurate and efficient property predictions in a data-driven manner. However, existing descriptors and ML models for the ionic molecule suffer from the inefficient adaptation of molecular graph structure. Besides, few works have investigated the explainability of ML models to help understand the learned features that can guide the design of efficient ionic molecules. In this work, we develop both fingerprint-based ML models and graph neural networks (GNNs) to predict the CO2 absorption in ILs. Fingerprint works on graph structure at the feature extraction stage, while GNNs directly handle molecule structure in both the feature extraction and model prediction stage. We show that our method outperforms previous ML models by reaching a high accuracy (MAE of 0.0137, R 2 of 0.9884). Furthermore, we take the advantage of GNN representation and develop a substructure-based explanation method that provides insight into how each chemical fragment within IL molecules contributes to the CO2 absorption prediction of ML models. We also show that our result agrees with some ground truth on functional group importance from the theoretical understanding of CO2 absorption in ILs, which can advise on the design of novel and efficient functional ILs in the future.

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Prediction of GPCR activity using machine learning

Cited by 20 publications

References 62 publications

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

Activity Map and Transition Pathways of G Protein Coupled Receptor Revealed by Machine Learning

Predicting CO₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

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Prediction of GPCR activity using machine learning

Cited by 20 publications

References 62 publications

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

Activity Map and Transition Pathways of G Protein Coupled Receptor Revealed by Machine Learning

Predicting CO2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

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Predicting CO₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks