GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor

EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network

BACKGROUND

Identifying GPCR-compound interactions (GCI) plays a significant role in drug discovery and chemogenomics. Machine learning, particularly deep learning, has become increasingly influential in this domain. Large molecular models, due to their ability to capture detailed structural and functional information, have shown promise in enhancing the predictive accuracy of downstream tasks. Consequently, exploring the performance of these models in GCI prediction, as well as evaluating their effectiveness when integrated with other deep learning models, has emerged as a compelling research area. This paper aims to investigate these challenges.

RESULTS

This study introduces EnGCI, a novel model comprising two distinct modules. The MSBM integrates a graph isomorphism network (GIN) and a convolutional neural network (CNN) to extract features from GPCRs and compounds, respectively. These features are then processed by a Kolmogorov-Arnold network (KAN) for decision-making. The LMMBM utilizes two large-scale pre-trained models to extract features from compounds and GPCRs, and subsequently, KAN is again employed for decision-making. Each module leverages different sources of multimodal information, and their fusion enhances the overall accuracy of GPCR-compound interaction (GCI) prediction. Evaluating the EnGCI model on a rigorously curated GCI dataset, we achieved an AUC of approximately 0.89, significantly outperforming current state-of-the-art benchmark models.

CONCLUSIONS

The EnGCI model integrates two complementary modules: one that learns molecular features from scratch for the GPCR-compound interaction (GCI) prediction task, and another that extracts molecular features using pre-trained large molecular models. After further processing and integration, these multimodal information sources enable a more profound exploration and understanding of the complex interaction relationships between GPCRs and compounds. The EnGCI model offers a robust and efficient framework that enhances GCI predictive capabilities and has the potential to significantly contribute to GPCR drug discovery.

Decrypting orphan GPCR drug discovery via multitask learning

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

Rationalizing a prospective coupling effect of cannabinoids with the current pharmacotherapy for melanoma treatment

Melanoma is one of the leading fatal forms of cancer, yet from a treatment perspective, we have minimal control over its reoccurrence and resistance to current pharmacotherapies. The endocannabinoid system (ECS) has recently been accepted as a multifaceted homeostatic regulator, influencing various physiological processes across different biological compartments, including the skin. This review presents an overview of the pathophysiology of melanoma, current pharmacotherapy used for treatment, and the challenges associated with the different pharmacological approaches. Furthermore, it highlights the utility of cannabinoids as an additive remedy for melanoma by restoring the balance between downregulated immunomodulatory pathways and elevated inflammatory cytokines during chronic skin conditions as one of the suggested critical approaches in treating this immunogenic tumor. This article is categorized under: Cancer > Molecular and Cellular Physiology.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Author Summary

Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

Computationally-Aided Modeling of Hsp70-Client Interactions: Past, Present, and Future

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70-client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

A Web Server for GPCR-GPCR Interaction Pair Prediction

The GGIP web server (https://protein.b.dendai.ac.jp/GGIP/) provides a web application for GPCR-GPCR interaction pair prediction by a support vector machine. The server accepts two sequences in the FASTA format. It responds with a prediction that the input GPCR sequence pair either interacts or not. GPCRs predicted to interact with the monomers constituting the pair are also shown when query sequences are human GPCRs. The server is simple to use. A pair of amino acid sequences in the FASTA format is pasted into the text area, a PDB ID for a template structure is selected, and then the 'Execute' button is clicked. The server quickly responds with a prediction result. The major advantage of this server is that it employs the GGIP software, which is presently the only method for predicting GPCR-interaction pairs. Our web server is freely available with no login requirement. In this article, we introduce some application examples of GGIP for disease-associated mutation analysis.

The Pathophysiology and the Therapeutic Potential of Cannabinoids in Prostate Cancer

Prostate cancer is the second most frequently occurring cancer diagnosed among males. Recent preclinical evidence implicates cannabinoids as powerful regulators of cell growth and differentiation. In this review, we focused on studies that demonstrated anticancer effects of cannabinoids and their possible mechanisms of action in prostate cancer. Besides the palliative effects of cannabinoids, research from the past two decades has demonstrated their promising potential as antitumor agents in a wide variety of cancers. This analysis may provide pharmacological insights into the selection of specific cannabinoids for the development of antitumor drugs for the treatment of prostate cancer.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

The Endocannabinoid System as a Target in Cancer Diseases: Are We There Yet?

The endocannabinoid system (ECS) has been placed in the anti-cancer spotlight in the last decade. The immense data load published on its dual role in both tumorigenesis and inhibition of tumor growth and metastatic spread has transformed the cannabinoid receptors CB1 (CB1R) and CB2 (CB2R), and other members of the endocannabinoid-like system, into attractive new targets for the treatment of various cancer subtypes. Although the clinical use of cannabinoids has been extensively documented in the palliative setting, clinical trials on their application as anti-cancer drugs are still ongoing. As drug repurposing is significantly faster and more economical than de novo introduction of a new drug into the clinic, there is hope that the existing pharmacokinetic and safety data on the ECS ligands will contribute to their successful translation into oncological healthcare. CB1R and CB2R are members of a large family of membrane proteins called G protein-coupled receptors (GPCR). GPCRs can form homodimers, heterodimers and higher order oligomers with other GPCRs or non-GPCRs. Currently, several CB1R and CB2R-containing heteromers have been reported and, in cancer cells, CB2R form heteromers with the G protein-coupled chemokine receptor CXCR4, the G protein-coupled receptor 55 (GPR55) and the tyrosine kinase receptor (TKR) human V-Erb-B2 Avian Erythroblastic Leukemia Viral Oncogene Homolog 2 (HER2). These protein complexes possess unique pharmacological and signaling properties, and their modulation might affect the antitumoral activity of the ECS. This review will explore the potential of the endocannabinoid network in the anti-cancer setting as well as the clinical and ethical pitfalls behind it, and will develop on the value of cannabinoid receptor heteromers as potential new targets for anti-cancer therapies and as prognostic biomarkers.

The heterotetrameric structure of the adenosine A1-dopamine D1 receptor complex: Pharmacological implication for restless legs syndrome

Dopaminergic and purinergic signaling play a pivotal role in neurological diseases associated with motor symptoms, including Parkinson's disease (PD), multiple sclerosis, amyotrophic lateral sclerosis, Huntington disease, Restless Legs Syndrome (RLS), spinal cord injury (SCI), and ataxias. Extracellular dopamine and adenosine exert their functions interacting with specific dopamine (DR) or adenosine (AR) receptors, respectively, expressed on the surface of target cells. These receptors are members of the family A of G protein-coupled receptors (GPCRs), which is the largest protein superfamily in mammalian genomes. GPCRs are target of about 40% of all current marketed drugs, highlighting their importance in clinical medicine. The striatum receives the densest dopamine innervations and contains the highest density of dopamine receptors. The modulatory role of adenosine on dopaminergic transmission depends largely on the existence of antagonistic interactions mediated by specific subtypes of DRs and ARs, the so-called A_2AR-D₂R and A₁R-D₁R interactions. Due to the dopamine/adenosine antagonism in the CNS, it was proposed that ARs and DRs could form heteromers in the neuronal cell surface. Therefore, adenosine can affect dopaminergic signaling through receptor-receptor interactions and by modulations in their shared intracellular pathways in the striatum and spinal cord. In this work we describe the allosteric modulations between GPCR protomers, focusing in those of adenosine and dopamine within the A₁R-D₁R heteromeric complex, which is involved in RLS. We also propose that the knowledge about the intricate allosteric interactions within the A₁R-D₁R heterotetramer, may facilitate the treatment of motor alterations, not only when the dopamine pathway is hyperactivated (RLS, chorea, etc.) but also when motor function is decreased (SCI, aging, PD, etc.).

Heterodimerization of the prostaglandin E2 receptor EP2 and the calcitonin receptor CTR

G protein-coupled receptors (GPCRs) have been found to form heterodimers and modulate or fine-tune the functions of GPCRs. However, the involvement of GPCR heterodimerization and its functional consequences in gonadal tissues, including granulosa cells, have been poorly investigated, mainly due to the lack of efficient method for identification of novel GPCR heterodimers. In this paper, we identified a novel GPCR heterodimer between prostaglandin E2 (PGE2) receptor 2 (EP2) and calcitonin (CT) receptor (CTR). High-resolution liquid chromatography (LC)-tandem mass spectrometry (MS/MS) of protease-digested EP2-coimmunoprecipitates detected protein fragments of CTR in an ovarian granulosa cell line, OV3121. Western blotting of EP2- and CTR-coimmunoprecipitates detected a specific band for EP2-CTR heterodimer. Specific heterodimerization between EP2 and CTR was also observed by fluorescence resonance energy transfer analysis in HEK293MSR cells expressing cyan- and yellow-fluorescent protein-fused EP2 and CTR, respectively. Collectively, these results provided evidence for heterodimerization between EP2 and CTR. Moreover, Ca²⁺ mobilization by CT was approximately 40% less potent in HEK293MSR cells expressing an EP2-CTR heterodimer, whereas cAMP production by EP2 or CT was not significantly altered compared with cells expressing EP2- or CTR alone. These functional analyses verified that CTR-mediated Ca²⁺ mobilization is specifically decreased via heterodimerization with EP2. Altogether, the present study suggests that a novel GPCR heterodimer, EP2-CTR, is involved in some functional regulation, and paves the way for investigation of novel biological roles of CTR and EP2 in various tissues.