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

Mollaei, Parisa; Farimani, Amir Barati

doi:10.1021/acs.jcim.3c00032

Cited by 6 publications

(8 citation statements)

References 64 publications

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“…The activation states are essential for receptor functions and associated with physiological responses. The study of switch residues in GPCRs can shed light on the mechanisms of cell signaling and the transition pathways between activation states in the receptors at residue level. ,,− We analyzed the conformational changes in β 2 AR receptor which plays a crucial role in regulating bronchodilation, heart rate, and blood pressure. − We assessed the switch residues and the correlation between the SS residues and activation states in this receptor. For this study, we randomly selected 5000 MD short trajectories (consisting of 110,000 frames) representing structures of β 2 AR receptor in the presence of the partial inverse agonist carazolol.…”

Section: Resultsmentioning

confidence: 99%

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Mollaei,

Barati Farimani

2023

J. Chem. Theory Comput.

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Dynamics of individual amino acids play key roles in the overall properties of proteins. However, the knowledge of protein structural features at the residue level is limited due to the current resolutions of experimental and computational techniques. To address this issue, we designed a novel machine-learning (ML) framework that uses Molecular Dynamics (MD) trajectories to identify the major conformational states of individual amino acids, classify amino acids switching between two distinct modes, and evaluate their degree of dynamic stability. The Random Forest model achieved 96.94% classification accuracy in identifying switch residues within proteins. Additionally, our framework distinguishes between the stable switch (SS) residues, which remain stable in one angular state and jump once to another state during protein dynamics, and unstable switch (US) residues, which constantly fluctuate between the two angular states. This study also illustrates the correlation between the dynamics of SS residues and the protein’s global properties.

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Section: Resultsmentioning

confidence: 99%

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Mollaei,

Barati Farimani

2023

J. Chem. Theory Comput.

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“…The recognition of meta-stable states and associated collective variables could be accelerated and automated using methods such as principal component analysis (PCA), time-independent component analysis (TICA), and variational approach for Markov processes (VAMP). , However, it is necessary to note that such methods require a large amount of MD data, and the resulting collective variables generated from these methods may be abstract and challenging to interpret directly. , In this study, the collective variable, d , is a biophysics-aware feature, as it gives insight into the depth of the ligand within the binding pocket and also distinguishes between the meta-stable and most-stable states. Therefore, it can be used as a target variable for correlation and machine learning-based studies concerning ligand dynamics …”

Section: Resultsmentioning

confidence: 99%

“…Phe 6.52 participates in edge-to-face aromatic interaction with the ligands and is also coupled to the rotation of Trp 6.48 . Trp 6.48 , apart from being a transmission switch, , is also a part of the conserved polar network with Asp 3.32 and plays a major role in GPCR activation. , Asp 3.32 is a part of the 3–7 lock switch, and the opening of 3–7 lock is integral to ligand binding and is one of the first molecular switches to be activated in GPCRs . Asn 7.39 forms a hydrogen bond with adrenaline, stabilizing the receptor conformation, which is consistent with prior mutagenesis experiments .…”

Section: Resultsmentioning

confidence: 99%

Molecular Dynamics and Machine Learning Study of Adrenaline Dynamics in the Binding Pocket of GPCR

Seshadri

Krishnan

2023

J. Chem. Inf. Model.

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G-protein coupled receptors (GPCRs) are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β2-adrenergic receptor (β2AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β2AR and adrenaline. In this article, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β2AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β2AR–adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on the clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β2AR–adrenaline complex are also investigated.

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“…We developed GPCR-BERT which investigates the high-order interactions in GPCRs. The reason we chose GPCRs is that they are an essential class of cell membrane proteins found in organisms ranging from bacteria to humans. – Currently, over one-third of U.S. Food and Drug Administration-approved medications target the receptors. ,– GPCRs exhibit remarkable functional diversity, including sensory perception, neurotransmission, immune response, cardiovascular regulation, vision, metabolism, and energy homeostasis. ,,– Although each of the receptors has its own amino acid sequence, there are a few conserved motifs among them, such as NPxxY, CWxP, and E/DRY. Each conserved motif serves as a crucial component in the GPCRs – and the type of each x can vary according to the receptor class.…”

Section: Introductionmentioning

confidence: 99%

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

Kim,

Mollaei,

Antony

et al. 2024

J. Chem. Inf. Model.

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With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G proteincoupled receptors (GPCRs). GPCRs are the target of over onethird of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

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

Cited by 6 publications

References 64 publications

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Molecular Dynamics and Machine Learning Study of Adrenaline Dynamics in the Binding Pocket of GPCR

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

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