What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three-dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a practical option for high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. We analyzed the sequences and 3D structures of GPCRs from various families that bind to the same ligand. To conduct the analysis, we used currently available tools as well as newly developed Python scripts. These include MEME for motif search, FATCAT for 3D structural comparison, P2Rank for pocket prediction, APoc for pocket comparison, and our own Python codes for computing overlap scores. Comparison of 3D GPCR structures that bind to the same ligand revealed local 3D structural similarities and the similar regions often overlap with locations of binding pockets. Using Apoc, these pockets were found to be similar based on backbone geometry and side-chain orientation, and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can lead to improved protein function inference, drug repurposing, and drug toxicity prediction, which can, in turn, accelerate the development of new therapeutics. Furthermore, the computational workflow and program codes established for this analysis can be developed into a software pipeline for more extensive investigation of GPCR-ligand binding mechanisms

What Makes GPCRs From Different Families Bind To The Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three-dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a practical option for high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. We analyzed the sequences and 3D structures of GPCRs from various families that bind to the same ligand. To conduct the analysis, we used currently available tools as well as newly developed Python scripts. These include MEME for motif search, FATCAT for 3D structural comparison, P2Rank for pocket prediction, APoc for pocket comparison, and our own Python codes for computing overlap scores. Comparison of 3D GPCR structures that bind to the same ligand revealed local 3D structural similarities and the similar regions often overlap with locations of binding pockets. Using Apoc, these pockets were found to be similar based on backbone geometry and side-chain orientation, and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can lead to improved protein function inference, drug repurposing, and drug toxicity prediction, which can, in turn, accelerate the development of new therapeutics. Furthermore, the computational workflow and program codes established for this analysis can be developed into a software pipeline for more extensive investigation of GPCR-ligand binding mechanisms

Integration of 3D Structural and Sequence Features to Predict GPCR Ligand Binding

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. Such predictions are cost-efficient and can be important aides for planning wet lab experiments to help elucidate signaling pathways and expedite drug discovery. There are existing computational tools for GPCR ligand binding prediction using various sequence and structural derived features. However, these methods have been typically tested on specific families of GPCRs and none has focused on features that characterize binding of a single ligand to multiple GPCR families. In this work, we have established that there are ligands that bind across two or more distinct GPCR families. In many cases the involved GPCRs share a conserved sequence motif and structural similarities. These results suggest possibilities for predicting GPCR ligand binding through the integration of sequence and 3D structural information. The prediction process can be guided by features that characterize binding of one ligand to multiple GPCRs of the same or different families. For my PhD dissertation research, I propose to further explore the combination of such features to predict GPCR ligand binding. Our computational approach, which involves integrating GPCR classification, structure predictions, and molecular docking, will be based on statistical and machine learning as well as energy optimization techniques to predict what ligands will bind to a given GPCR. The resulting algorithm will be implemented in Python and R programming packages and incorporated into the publicly accessible GPCR-PEn webserver (gpcr.utep.edu) for distribution to the scientific community

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs

Adaptation of the Wound Healing Questionnaire universal-reporter outcome measure for use in global surgery trials (TALON-1 study): mixed-methods study and Rasch analysis

BackgroundThe Bluebelle Wound Healing Questionnaire (WHQ) is a universal-reporter outcome measure developed in the UK for remote detection of surgical-site infection after abdominal surgery. This study aimed to explore cross-cultural equivalence, acceptability, and content validity of the WHQ for use across low- and middle-income countries, and to make recommendations for its adaptation.MethodsThis was a mixed-methods study within a trial (SWAT) embedded in an international randomized trial, conducted according to best practice guidelines, and co-produced with community and patient partners (TALON-1). Structured interviews and focus groups were used to gather data regarding cross-cultural, cross-contextual equivalence of the individual items and scale, and conduct a translatability assessment. Translation was completed into five languages in accordance with Mapi recommendations. Next, data from a prospective cohort (SWAT) were interpreted using Rasch analysis to explore scaling and measurement properties of the WHQ. Finally, qualitative and quantitative data were triangulated using a modified, exploratory, instrumental design model.ResultsIn the qualitative phase, 10 structured interviews and six focus groups took place with a total of 47 investigators across six countries. Themes related to comprehension, response mapping, retrieval, and judgement were identified with rich cross-cultural insights. In the quantitative phase, an exploratory Rasch model was fitted to data from 537 patients (369 excluding extremes). Owing to the number of extreme (floor) values, the overall level of power was low. The single WHQ scale satisfied tests of unidimensionality, indicating validity of the ordinal total WHQ score. There was significant overall model misfit of five items (5, 9, 14, 15, 16) and local dependency in 11 item pairs. The person separation index was estimated as 0.48 suggesting weak discrimination between classes, whereas Cronbach's α was high at 0.86. Triangulation of qualitative data with the Rasch analysis supported recommendations for cross-cultural adaptation of the WHQ items 1 (redness), 3 (clear fluid), 7 (deep wound opening), 10 (pain), 11 (fever), 15 (antibiotics), 16 (debridement), 18 (drainage), and 19 (reoperation). Changes to three item response categories (1, not at all; 2, a little; 3, a lot) were adopted for symptom items 1 to 10, and two categories (0, no; 1, yes) for item 11 (fever).ConclusionThis study made recommendations for cross-cultural adaptation of the WHQ for use in global surgical research and practice, using co-produced mixed-methods data from three continents. Translations are now available for implementation into remote wound assessment pathways