About - Methods

We developed PRECOGx, a machine learning predictor of GPCR interactions with G-protein and β-arrestin, by using the ESM1b protein embeddings as features and experimental binding datasets.

Embeddings generation

Embeddings of the protein sequences were generated by using pre-trained protein language models that have been recently released. We computed embeddings from fasta sequence using the extract.py function of the ESM library and by specifying the ESM-1b model (esm1b_t33_650M_UR50S) with embedding for individual amino acids as well as averaged over the full sequence using the option “--include mean per_tok”.

We generated embeddings for each individual layers separately, including the final one, by specifying their corresponding number in the “--repr-layers” option.

Data sets

We obtained experimental binding affinities from two distinct sources: TGF assay(12), which captures the binding affinities of 148 GPCRs with 11 chimeric G-proteins, and the ebBRET assay, which profiles the binding affinities of 97 GPCRs with 12 G-proteins and 3 β-arrestins/GRKs binders, available at gpcrdb. We also used an integrated meta-coupling dataset derived from a meta-analysis of the aforementioned assays, entailing binding affinities of 164 GPCRs for 14 G-proteins. For the TGF assay, we considered a receptor coupled to a G-protein if the logarithm (base 10) of the relative intrinsic activity (logRAi) was greater than -1, and not-coupled otherwise. Similarly, for the GEMTA assay, we considered a receptor coupled to a G-protein (or β-arrestins/GRK) if the binding efficacy (dnorm Emax) was greater than 0, and not-coupled otherwise. For the integrated meta-coupling dataset, we considered a receptor coupled to a G-protein if the integrated binding affinity was greater than 0, and not-coupled otherwise.

Model training

We developed the new PRECOGx by training multiple models using the protein embeddings derived from the pre-trained ESM-1b model as features. For every pair of a coupling group (G-protein/β-arrestins) and assay dataset (TGF/GEMTA assays), we created a training matrix with vectors, each containing the decomposed PCA values of a receptor embedding along with the binary label (coupled/not-coupled) as the last element. We implemented the predictor using either a logistic regression or support vector classifier from the Scikit library library. A grid search was performed using a stratified 5-fold cross validation (CV) to select the best hyperparameters of the classifier. We repeated the process 10 times to ensure a minimum variance. We generated a total of 34 models per G-protein (or β-arrestin) and assay. The best models were chosen based on the highest AUC (Area Under the Curve) score during the 5-fold cross-validation.

Model testing

We benchmarked our method against PRECOG, the web-server for GPCR/G-protein coupling predictions that we previously developed. We obtained an independent list of 117 (TGF assay data as the training set), and 160 receptors (GEMTA assay as the training set) from the GtoPdb that are absent in both the assay datasets. Since GtoPdb lacks a proper true negative set, we used Recall (REC) as a measure to compare the performance of PRECOGx with PRECOG. To assess over-fitting, we performed the randomization test by randomly shuffling the original labels of the training matrix, while preserving the ratio of the number of coupled to not-coupled receptors.

PCA of the GPCRome embedded space

We generated embeddings for the human GPCRome, comprising a total of 377 receptors (279 Class A, 15 Class B1, 17 Class B2, 17 class C, 11 class F, 25 Taste receptors and 14 in other classes). We considered either the embeddings generated by considering all the layers. Embeddings were subjected to Principal Component Analysis (PCA), using the PCA function from the Scikit library. Each GPCR sequence within the embedding is annotated with functional information (i) coupling specificities (known from the TGF assay, GEMTA assay, the GtoPdb, and the STRING (for β-arrestins) databases; (ii) GPCR class membership (known from the GtoPdb).

Contact analysis

To interpret the determinants of binding specificity, we first calculated predicted contacts for each sequence using a logistic regression over the model's attention maps, available in the ESM library through the predict_contacts function. Then, the predicted contact maps were grouped on the basis of G-protein binding specificity and contrasted to the contact maps of all the GPCRs which was used as a background. We computed a differential contact maps by calculating a log-odds ratio, employing the following formula:

Where AA and BB terms represent a number of coupled GPCR to a specific G-protein depending on the assay , that has or does not have a specific contact pair respectively. CC and DD terms represent the number of not-uncoupled GPCR for a specific G-protein depending on the assay, that has or does not have a specific contact pair respectively. Contacts contributed from the loops, N-terminal, and C-terminal of the GPCR were aggregated. Contact pairs considered are those with a probability higher than 0.5 calculated based on predict_contacts function and those appearing in at least 15% of all GPCR in the assays. We computed log-odds ratio using the Table2x2 function from StatsModels. The resulting log-odds ratio was normalized using the MaxAbsScaler from Sscikit-learn.Contacts with a positive log-odds ratio (enriched) are seen more frequently in receptors coupled to a specific G-protein, while contacts with a negative log-odds ratio (depleted) are seen less frequently in receptors coupled to a specific G-protein.

Attention maps

By inspecting the weights of the trained classifiers, we extracted the most important attention head, for the best performing layer of each coupling group. Attention maps are calculated during embedding generation for each input sequence and are displayed as 2D heatmaps.

Libraries used

Following libraries were used to build the webserver:

• ESM
• NGL viewer
• jQuery
• neXtProt
• Bootstrap
• Flask
• Scikit-learn
• DataTables
• Plotly

Contact:

Francesco Raimondi - francesco.raimondi@sns.it

Marin Matic - marin.matic@sns.it

Gurdeep Singh - gurdeep.singh@bioquant.uni-heidelberg.de