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. + 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 +
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 +
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. +
+ 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. +
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. + 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 +
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. + 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). + 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 +
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: + 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. +
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.
@@ -110,17 +154,25 @@ Attention maps
Libraries used
Following libraries were used to build the webserver:
-
-
- ESM -
- NGL viewer -
- jQuery -
- neXtProt -
- Bootstrap -
- Flask -
- Scikit-learn -
- DataTables -
- Plotly +
- ESM +
- NGL viewer +
- jQuery +
- neXtProt +
- Bootstrap +
- Flask +
- Scikit-learn +
- DataTables +
- Plotly
Contact:
+Cite
++ Marin Matic, Gurdeep Singh, Francesco Carli, Natalia De Oliveira Rosa, Pasquale Miglionico, Lorenzo + Magni, J Silvio Gutkind, Robert B Russell, Asuka Inoue, Francesco Raimondi, PRECOGx: exploring GPCR + signaling mechanisms with deep protein representations, Nucleic Acids Research, Volume 50, Issue W1, + 5 July 2022, Pages W598–W610, + https://doi.org/10.1093/nar/gkac426
+Contact
Francesco Raimondi - francesco.raimondi@sns.it
Marin Matic - marin.matic@sns.it
Gurdeep Singh - gurdeep.singh@bioquant.uni-heidelberg.de