Two-thirds of human hormones and one-third of clinical drugs act on membrane receptors that couple to G proteins to achieve appropriate functional responses. While G protein transducers from literature are annotated in the Guide to Pharmacology database, two recent large-scale datasets now expand the receptor-G protein ‘couplome’. However, these three datasets differ in scope and reported G protein couplings giving different coverage and conclusions on G protein-coupled receptor (GPCR)-G protein signaling. Here, we report a common coupling map uncovering novel couplings supported by both large-scale studies, the selectivity/promiscuity of GPCRs and G proteins, and how the co-coupling and co-expression of G proteins compare to the families from phylogenetic relationships. The coupling map and insights on GPCR-G protein selectivity will catalyze advances in receptor research and cellular signaling toward the exploitation of G protein signaling bias in design of safer drugs. A GPCR’s activation of G proteins can be very selective or promiscuous and change upon ligand-dependent biased signaling that alters its profile on the G protein subtype or family levels. The pleiotropic signaling and ligand-dependent bias of GPCRs pose a grand challenge in human biology to map the differential activation of specific G proteins.G protein-coupled receptors (GPCRs) represent the largest family of proteins involved in signal propagation across biological membranes. They recognize a vast diversity of signals going from photons and odors to neurotransmitters, hormones, and cytokines . Their mq wildtype and 10 G protein chimeras employing a transforming growth factor-α (TGF-α) shedding assay (neurotensin 1 [NTS1] and thyrotropin-releasing hormone [TRH1] added herein making it 150 receptors) . In a paper accompanying the present analysis, the Bouvier group quantified the couplings of 100 GPCRs to 12 G proteins: Gs, Gi1, Gi2, Go (GoA and GoB isoforms), Gz, Gq, G11, G14, G15, G12, and G13 but not Golf (couples mainly to olfactory receptors), Gi3, and Gt1-2  and Ggust  (s) or the receptors.The International Union of Basic and Clinical Pharmacology (IUPHAR)/British Pharmacological Society (BPS) Guide to Pharmacology (GtP) database contains reference data from expert curation of literature . GtP couceptors) . Those cceptors) . The autmax and EC50 into a common log(Emax/EC50)   value, aax/EC50) , describTo obtain an overview of the coverages of GPCR-G protein coupling sources, we compared all couplings reported by both the Bouvier  and Inoumax threshold  that gives the best agreement between the Bouvier and Inoue dataset. We believe that this is the best possible means to estimate what is correct data , as large-scale information about what G proteins and GPCRs couple physiologically is not available. This cut-off is more stringent than the minimum of 3% signal above basal used in Inoue’s original report . This demonstrates that the two first systematic coupling profiling studies have substantially expanded the known GPCR-G protein ‘couplome’ and that their assay platforms are amenable to high-throughput profiling also for the G12/13 family for which robust activation assays appeared only recently  value and an aggregation of G proteins onto families based on the maximum subtype values . This uses log transformed ECe values . Based oe values . This unhttps://gproteindb.org/signprot/couplings) in GproteinDb (max/EC50), Emax, and EC50 values. Finally, users can filter datapoints based on a statistical reliability score in the form of the number of SDs from basal response.To enable any researcher to use the coupling map, we have availed a ‘G protein couplings’ browser . Conversely, a G protein family has supported couplings to on average 112 GPCRs  distributed as Gs: 87, Gi/o: 176, Gq/11: 134, and G12/13: 49 receptors . In contrast, activation of only one subtype of a G protein family is only observed for 11 Gs, 4 Gz, 1 G14, 10 G15, 5 G12, and 10 G13-coupled receptors , P2Y14 (Gz), and GPR55 (G13) are fully selective also when considering G proteins from all families, that is, they only couple to a single of the 16 human G proteins . However, the three purinergic receptors have additional couplings although not supported by a second dataset (P2Y4 and P2Y14) or above the 1.4 × SD cut-off (P2Y1 was below), and it is possible that as the characterizations expand even fewer, or no receptors are found to engage only a single G protein.Within each G protein family  pathways suggests that these G proteins often have more versatile functions. In all, our meta-analysis of GPCR-G protein selectivity points to intriguing differences where some receptors selectively signal via a single effector whereas other GPCRs promiscuously activate all four G protein pathways. Such selective or combined profiles, in interplay with differential spatiotemporal expression , GoA-GoB (0.96), and Gq-G11 (0.99). Interestingly, the correlated coupling is lower for the only pairs within the Gs and G12/13 families, Gs-Golf (0.77) and G12-G13 (0.79), respectively. Furthermore, both Gi/o and Gq/11 have an ‘odd’ member, Gz and G15, respectively . The fact that all G protein families have an odd member shows that all signaling pathways have a transducer toolbox allowing them to differentiate signaling.Several G protein pairs stand out with exceptionally high overall correlated coupling (Pearson standard correlation coefficient): Gs and Gi/o families have an inverse correlation in all of the overall, coupling/noncoupling, and activation level comparisons , a direct effector of Gq, can also be activated by Gβγ from Gi (i.e. sensitive to the Gi inhibitor pertussis toxin). Thus, the concept of potential scaffolding of PLC by Gq may be required for Gi-derived Gβγ activation  values. In contrast, Gi3, Gt1, Gt2, Gt3, and G14 have none or only one tissue with a first quartile expression. To facilitate comparison across G proteins, we employed HPA’s nTPM values after z-score transformation of each G protein across tissues. A z-transformation of the same G proteins visualizing the relative tissue levels of expression, shows a very focused expression of the transducins, Gt1 and Gt2, in the retina and gustducin Gt3 in the gastrointestinal tract reflecting their role in vision and taste, respectively  from the Human Protein Atlas (HPA) , which aectively . Notably/13: G12 .s family, Gs and Golf, do not cluster adjacently but not as a pair). Instead, we find a coherent cluster for the G proteins that are predominantly expressed in the brain, Golf, Go, Gz, Gq, and G12 and cover all four families. Furthermore, among the remaining co-expressing G proteins, three out of the four closest pairs also span families . Taken together, this shows that there is no overall clustering of subtypes within the G protein families, which instead exploit differential expression to gain spatial selectivity. For example, in the Gi/o family Go and Gz are restricted to brain regions, while the other subtypes have four different peripheral profiles.Given that the tissue expression varies largely for G proteins, we next sought to determine to what extent their tissue expression correlations differ from the traditional four G protein families that represent phylogeny and GPCR coupling  relation15, Bouvier reported 30% unique couplings, several of which were validated in Ca2+ assays (15 couplings are underrepresented in Inoue’s data and in literature (annotated in GtP). The few published couplings for G15 are likely explained by its lack of expression in the cells most used for in vitro experiments, such as HEK293 cells  effect on this subtype by Gq/11 inhibitor tools YM-254890 and FR900359, respectively , except Gs and G12. This suggests that the many unique couplings in Inoue’s dataset may be due to high sensitivity rather than the use of chimeric G proteins carrying over of Gq coupling to the other G proteins.For Gignaling . Notablymax values are 18% and 15% lower for unique than for supported couplings in the Bouvier and Inoue datasets, respectively. Furthermore, their average pEC50 values are 0.4 and 1.5 log units – 2.5-fold and 32-fold, respectively – lower than the supported couplings. This indicates that a part of the differences across datasets is due to weak couplings that can only be detected in the most sensitive assays for the given G proteins and is difficult to distinguish from basal levels . However, couplings may also be overrepresented and determining couplings definitely would require further studies employing independent  high-sensitivity biosensors. Until then, we recommend the requirement of two independent studies to support of a GPCR-G protein coupling, as is the criterion used herein for the novel couplings and the default in the online G protein couplings resource  promiscuously couple to all four G protein families whereas the receptor numbers instead increase several-fold along with selectivity: 34 triple-, 83 double-, and 126 single-family coupling receptors . As moret1 and Gt2) and gustducin (Gt3) have a restricted expression in mainly one tissue , whereas the most ubiquitously expressed G proteins, Gs and Gi2 are highly expressed in all 50 analyzed tissues. Notably, G proteins belonging to the same family differ largely in their tissue profiles, for example, each G protein family has a single or two subtypes that is/are preferentially expressed in the brain. Given that the activity (here log(Emax/EC50) values) can vary substantially across G protein family subtypes  for each G protein family. To this end, we compared the three datasets to pinpoint, for each dataset and G protein family, the fraction of couplings supported by another dataset. The scientific analyses based on the map feature the most comprehensive analysis to date of GPCR-G protein selectivity. This was done by intersecting the 11 G proteins tested by both Bouvier and Inoue, within and across their families, with respect to the common and unique receptors that they couple to. Finally, the receptor profiles were also used to classify G proteins and to determine any co-correlation between different G protein subtypes and families to GPCRs.The primary objective of this study was to generate a unified map of G protein couplings  across t50, Emax, basal signals, and SD values were supplied by Asuka Inoue. Basal signals, spontaneous AP-TGF-α release  were recalculated from raw data of the previous coupling-profiling campaign . For the easiness of integration into the coupling map, we added a value of 10 to their pEC50 values. Data qualities  for the individual GPCR-G protein pairs were manually inspected and concentration-response curves that did not converge nor exceed a threshold  were regarded as no activity.Updated spreadsheets containing the pECcampaign  and theilication . In the max and pEC50 values because the dose-response curve did not converge were only included if supported, and not contradicted, by the Inoue and/or GtP datasets  contained some datapoints that were included or excluded based on dedicated analyses. First, we excluded ligand-promoted responses of overexpressed receptors that were equivalent to those of endogenously expressed receptors . The Bouvier dataset has low means and spans for Gs (42 and 47) and G12 (71 and 101) indicating a narrow assay signal window . We find that minimum-maximum normalization (to 100%) of each G protein across receptors gives a more uniform distribution  of each receptor across all G proteins   were used for the calculation of log(Emax/EC50) values as recommended in max value was therefore set 0 while the maximum was set to the highest value for the given G protein  or receptor .To enable quantitative correlation of the Bouvier and Inoue couplings, we further filtered the 70 common GPCRs to yield 51 common class A GPCRs tested with the same ligand (excluding 29 receptors tested with different ligands and two class B1 GPCRs). To assess the value of normalization, we calculated the average ‘similarity’ (Bouvier/Inoue ratio) of individual values  and an identical linear correlation (0.37). We combined the minimum-maximum normalized Emax and non-normalized EC50 values into log(Emax/EC50) (max/EC50) values have an average similarity that is nearly as high (0.86 compared to 0.89) and an average linear correlation that is better (0.41 compared to 0.37) than for pEC50, the best individual measure. We did not apply a double normalization, that is, also across G proteins after across receptors, as this worsens the Emax, pEC50, and log(Emax/EC50) value similarity  and linear correlation of each G protein across receptors .Minimum-maximum normalization of Eax/EC50) . The logmax (0.69 vs. 0.65), marginally lower similarities for pEC50 (0.89 relative 0.90), and identical similarities for log(Emax/EC50) (0.88). However, max performs better overall than mean in the comparisons correlating a receptor across G proteins; Emax (0.63 and 0.53), pEC50 (0.56 and 0.56), and log(Emax/EC50) (0.65 and 0.59) or one G protein across receptors; Emax (0.29 and 0.20), pEC50 (0.71 and 0.74), and log(Emax/EC50) (0.69 and 0.71). Notably, the correlation of each receptor across max-aggregated G protein families compared to nonaggregated subtypes is much stronger when considering any of the three pharmacological parameters: Emax (0.63 vs. 0.36), pEC50 (0.56 vs. 0.30), and log(Emax/EC50) (0.65 vs. 0.41). Altogether, this establishes the highest value (max) as the aggregation that yields the most comparable quantitative value for G protein families.Given that G proteins belong to families that are functionally grouped by sharing downstream signaling pathways, we next investigated the best member-to-family aggregation scheme – specifically, whether the most comparable G protein family values are obtained if using the maximum value from any single subtype or the mean of all subtype members. We found that aggregation using max rather than mean values gives a better similarity for Ehttps://www.proteinatlas.org/about/assays+annotation#rna). The correlation analysis was performed using a default Pearson correlation analysis using Seaborn’s clustermap.Consensus transcript expression levels were extracted for 50 different tissues aggregated into 16 organs or systems in the HPA , which aN, P, and the specific statistical test are included in the figure legend or associated manuscript text. For max/EC50) values was assessed as a measure of the strength of the linear relationship between two pathways. The pairwise distances between pathways were calculated in python scipy package using the spatial.distance.pdist method using the correlation metric. Statistical significance was determined by Pearson standard correlation coefficient with a two-tailed p-value as implemented in scipy’s stats.pearsonr method.The aggregated sample size is n = 3 or higher for all analyzed GPCR-G protein couplings. The specific sample size for each such coupling is described in the original articles reporting these data and referenced in the present manuscript. The Bouvier dataset contained some datapoints that were included or excluded based on dedicated analyses (see ‘Coupling datasets’ above). For all figures, the values for  Using data sets are from three distinct sources, the authors present a meta-analysis to arrive at a consensus for G protein-coupled receptor (GPCR) coupling specificity to G proteins, as well as to identify and highlight differences or incongruencies. Compiling these data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies, and a better understanding of receptor-G protein promiscuity will ultimately help in identifying safer therapeutics. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Richard Aldrich as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Roger Sunahara (Reviewer #2).Thank you for submitting your article "GPCR-G protein selectivity -a unified meta-analysis" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The paper should be classified as Tools and Resources.This work provides a resource of broad interest and utility to the large community of researchers interested in GPCR coupling to G proteins. By combining 2 (published) large datasets of interactions with the widely-used (literature-based) Guide to Pharmacology database it substantially increases both the amount of GPCR/G protein coupling validated by multiple studies and quantifies the interactions better. The data are well analyzed, but there are several concerns that need to be addressed. The principal concerns are that 1) there is no analysis of distinctions within a G subfamily, and 2) there is no demonstration of the utility of the analysis, i.e. demonstrating an unexplored coupling in a physiologically relevant context.Overall, it is a well-written (but not well-edited) manuscript. There are several typographical errors in the manuscript, perhaps as a result of the choice of font size. It was hard enough for the reviewers to carefully read through the text, and apparently for the authors as well.1. There are specific properties of some G protein isoforms within each family that need to be considered when making these comparisons. For example, there are vastly different nucleotide exchange properties of Galphao (Gao) from the other Gi isoforms . Moreover, Gao has quite a distinct expression pattern than other isoforms where it is mostly expressed in brain with levels reaching 1%-3% of total membrane protein concentration. These properties are likely related to the capacity of this isoform to exhibit rapid regulation of effectors such as ion channels. Thus, merging the data sets for Gao with other Gai isoforms, particularly when the coupling data are based on recombinant expression in HEK cells, or with Galpha chimeras, should done with caution. One might even consider performing the meta-analysis of Gao separately from the other Gai isoforms.2. The authors raise an interesting and important caveat in the Bouvier and Inoue screens regarding the EC50 and efficacy differences compared to the GtP screens. It is fine to point out the differences, but possible explanations for the differences, especially when comparing three different screening platforms, needs to be provided.3. The data are described mostly by broad numbers, such as the number of receptors / coupling in a subset, or by percentages. While this is helpful to understand the data, it is hard to follow the mountain of numbers. The suggestion is to add a section where the authors pick selected examples of particular experimental data and show how their combined database can resolve previously unanswered (or wrongly answered) questions of GPCR/G protein coupling.4. The paper does not reveal new biological findings, but the authors should be able to do so if they use their new resource for specific examples. For example, while some emphasis is placed on new data on G15, it would be helpful to take the extra step and use this to suggest new biological insights. Looking more deeply into those G proteins that are outliers in their subfamilies, such as Gz, G15, Gt3 would also be useful.5. The authors cautiously label couplings supported by only one data set as "unsupported". It would seem more helpful to grade couplings by a reliability scale, providing users with a wider set of data. Perhaps only couplings that are directly conflicted by negative data should be labeled as unsupported?6. Given that this manuscript includes authors from both the Inoue and Bouvier studies, it is somewhat understandable that they are not directly assessing which of the two datasets (in relation to the GtP) might be more accurate. Nevertheless, this assessment should be done and the advantages and disadvantages of the two experimental systems discussed clearly. The authors should use their new resource to assess the weaknesses and strengths of each of their respective experimental methods, especially when there are data in GtP that can be used for comparisons. They briefly mention such a conclusion in line 136-137 or line 142, but this should be done more broadly and more deeply.7. Lines 218-219 say "The choice of protein systems and biosensors for such future studies should take into account the relative strengths and limitations of the profiling platforms used". Please provide such an assessment here, with the data you provide.8. The sentence "this shows that G protein family pairs have evolved differing levels of receptor co-coupling or selectivity, which they have achieved by weakening or abolishing the binding to the same GPCRs" in line 209-210 begs a deeper investigation of this using this resource and selected G proteins. This is also potentially confounded by the use of overexpression for many of the examples in the companion paper.Reviewer #1 (Recommendations for the authors):To emphasize the last weakness, the authors should use their new resource to assess the weaknesses and strengths of each of their respective experimental methods, esp. when there's data in GtP that can be used for comparisons. They briefly mention such a conclusion in line 136-137 or line 142, but this should be done more broadly and more deeply.Lines 2128-219 say "The choice of protein systems and biosensors for such future studies should take into account the relative strengths and limitations of the profiling platforms used," So please give us such an assessment here, with the data you provide.Another improvement would be to look more deeply into those G proteins that are outliers in their subfamilies, such as Gz, G15, Gt3. This ties into my comment above, that the manuscript lacks a biological story, one that I believe the authors can reach if they use their new resource for specific examples.For me, the sentence "this shows that G protein family pairs have evolved differing levels of receptor co-coupling or selectivity, which they have achieved by weakening or abolishing the binding to the same GPCRs" in line 209-210 begs a deeper investigation of this using this resource and selected G proteins.Reviewer #2 (Recommendations for the authors):This is a timely review/analysis and will be extremely useful for the GPCR field. Overall, it is a well-written (but not well-edited) manuscript that will be greatly appreciated by GPCR biologist. The tables, figures and Gantt charts are useful.There are specific properties of some G protein isoforms within each family that need to be considered when making these comparison. I draw the authors' attention to the vastly different nucleotide exchange properties of Galphao (Gao) from the other Gi isoforms . Moreover, Gao has quite a distinct expression pattern than other isoforms where it is mostly expressed in brain with levels reaching 1%-3% of total membrane protein concentration. These properties are likely related to the capacity of this isoform to exhibit rapid regulation of effectors such as ion channels. Thus, merging the data sets for Gao with other Gai isoforms, particularly when the coupling data are based on recombinant expression in HEK cells, or with Galpha chimeras, should done with caution. One might even consider performing the meta-analysis of Gao separately from the other Gai isoforms.The authors raise an interesting and important caveat in the Bouvier and Inoue screens regarding the EC50 and efficacy differences compared to the GtP screens. One issue is to point out the differences but at some point an explanation for the differences need to be addressed, especially when comparing three different screening platforms. Essential revisions:The paper should be classified as Tools and Resources.The meta-analysis unifying datasets into a common coupling map fits the Tools and Resource format very well. The data normalization protocol (leading to log(Emax/EC50) as a comparable parameter), explanation and overview of the couplings are all tools/resources that are unique to this paper, whereas the resulting coupling map has recently been published as an interactive “G protein couplings” browser in the GproteinDb database (1). Furthermore, we have edited the manuscript to reduce its technical nature.Beyond the Tools and Resources component, the coupling map allowed us to advance GPCR-G protein selectivity with new or updated findings that instead would fit with the publication format “Research Advances” (https://reviewer.elifesciences.org/author-guide/types). This part has now been expanded with analyses of G protein expression profiles and co-expressions. We have clarified the dual nature in the revised manuscript by splitting Results into two subsections named “Tools and Resources – Common coupling map unifying GPCR-G protein data” and “Research Advances – Insights on GPCR-G protein selectivity”. This has also been clarified in the title, which has been updated to “Common coupling map advances GPCR-G protein selectivity”.Given this dual content, we would please like to ask for an updated opinion on the most suitable publication format based on the revised manuscript. Would it be most suitable as a Tools and Resource, Research Advances or Research Article (to cover both aspects)?This work provides a resource of broad interest and utility to the large community of researchers interested in GPCR coupling to G proteins. By combining 2 (published) large datasets of interactions with the widely-used (literature-based) Guide to Pharmacology database it substantially increases both the amount of GPCR/G protein coupling validated by multiple studies and quantifies the interactions better. The data are well analyzed, but there are several concerns that need to be addressed. The principal concerns are that 1) there is no analysis of distinctions within a G subfamily, and 2) there is no demonstration of the utility of the analysis, i.e. demonstrating an unexplored coupling in a physiologically relevant context.Overall, it is a well-written (but not well-edited) manuscript. There are several typographical errors in the manuscript, perhaps as a result of the choice of font size. It was hard enough for the reviewers to carefully read through the text, and apparently for the authors as well.We have increased and differentiated the font size of headings, adjusted the page margin where incorrect and updated the text in several places to increase legibility, including those pointed out by Reviewer 1. We have also rewritten several sections of Results and paragraphs in the Discussion to increase clarity.Reviewer #1 (Recommendations for the authors):To emphasize the last weakness, the authors should use their new resource to assess the weaknesses and strengths of each of their respective experimental methods, esp. when there's data in GtP that can be used for comparisons. They briefly mention such a conclusion in line 136-137 or line 142, but this should be done more broadly and more deeply.Lines 2128-219 say "The choice of protein systems and biosensors for such future studies should take into account the relative strengths and limitations of the profiling platforms used," So please give us such an assessment here, with the data you provide.12/13) and does not provide any information on the level of G protein subtypes, only families. The three-way interactions will always use GtP but adds a second dataset on top of this when validating a third dataset. Our manuscript already included a three-way intersection of datasets, allowing readers to conclude which dataset might be more accurate  on a per-G protein basis.We believe that the three-way intersection of couplings is the most informative and therefore preferred over individual comparison of each of the Inoue and Bouvier datasets to GtP. GtP is unfortunately not suitable as a stand-alone resource – neither to contradict nor support couplings (on the G protein subtype level). This is because GtP is incomplete , and the accompanying paper by Bouvier et al. (4), comparing several aspects of the experimental system used by Inoue et al. It is also important to note that the most advantageous biosensor may be one of the two for which data is analyzed in our paper. For many studies, researchers may instead be better off with another biosensor, for example those from Lambert/Mamyrbekov (5), Roth (2) (Gαβγ sensors first described in (6-11)) or Inoue (unpublished dissociation assays using wt G proteins fused with LgBit and HiBit). These are all referenced in the Discussion.15 and, Gs and G12, respectively”.Additionally, the conclusions in lines 136-137, 142 and 218-219 have now been better described as part of a rewriting of the respective section of Results, “Bouvier’s and Inoue’s biosensors appear more sensitive for GAnother improvement would be to look more deeply into those G proteins that are outliers in their subfamilies, such as Gz, G15, Gt3. This ties into my comment above, that the manuscript lacks a biological story, one that I believe the authors can reach if they use their new resource for specific examples.z and Gt3, which had not been included previously, as there is no coupling data for this G protein. Furthermore, we have increased and clarified the usage of particular examples of G proteins (and GPCRs) within the different sections of the Results subsection “Research Advances – Insights on GPCR-G protein selectivity”.We have now added an additional biological story describing the expression profiles and co-expression of G proteins. This section describes which G proteins are outliers in terms of expression, including GWith the public data currently available, we feel that this is the best balance when avoiding going to far on a limited data basis versus covering the relevant, supported biological stories of G proteins. It would be advisable to await further independent datasets from high-sensitivity biosensors before looking more deeply are outlier couplings. This is because GtP does not provide subtype data, and the outliers are typically where the Bouvier and Inoue datasets differ largely. Therefore, a more detailed analysis may risk not being reproducible until additional datasets become available.For me, the sentence "this shows that G protein family pairs have evolved differing levels of receptor co-coupling or selectivity, which they have achieved by weakening or abolishing the binding to the same GPCRs" in line 209-210 begs a deeper investigation of this using this resource and selected G proteins.This section has now been rewritten to better investigate and distinguish the contributions of binding/non-binding and weak/strong binding, respectively as mechanisms of GPCR-G protein selectivity.Reviewer #2 (Recommendations for the authors):This is a timely review/analysis and will be extremely useful for the GPCR field. Overall, it is a well-written (but not well-edited) manuscript that will be greatly appreciated by GPCR biologist. The tables, figures and Gantt charts are useful.There are specific properties of some G protein isoforms within each family that need to be considered when making these comparison. I draw the authors' attention to the vastly different nucleotide exchange properties of Galphao (Gao) from the other Gi isoforms . Moreover, Gao has quite a distinct expression pattern than other isoforms where it is mostly expressed in brain with levels reaching 1%-3% of total membrane protein concentration. These properties are likely related to the capacity of this isoform to exhibit rapid regulation of effectors such as ion channels. Thus, merging the data sets for Gao with other Gai isoforms, particularly when the coupling data are based on recombinant expression in HEK cells, or with Galpha chimeras, should done with caution. One might even consider performing the meta-analysis of Gao separately from the other Gai isoforms.If not mistaken, Gz also has a slower GTPase activity than the other Gi/o family members?In the revised manuscript, we have added a section “Differential tissue expression gives G proteins in the same family large spatial selectivity” that brings up the point of differential expression. This is revisited in a new paragraph of the Discussion, which includes the text:“The best way to address this would be for future studies to profile the couplings of natively expressed GPCRs and G proteins across different cell types. Meanwhile, a strategy could be to analyze GPCR-G protein selectivity for a subset of G proteins that are selected based on the highest expression in the tissue of interest. Such separated meta-analyses of G proteins may be facilitated by the filtering options in the coupling maps in Spreadsheet S5 and online in the G protein couplings browser in GproteinDb (1).“The same paragraph also mentions the differential nucleotide exchange of Gao and Gaz relative to other members of the Gi/o family. We did not perform a meta-analysis of Gao or Gao separately from the other Gai isoforms, for consistency, length limitations and because the signaling may be inseparable in some tissues. However, readers that wish to do so could do that by using the G protein couplings browser (https://gproteindb.org/signprot/couplings) in GproteinDb (1) or by modifying Spreadsheet S3 in MS Excel.The authors raise an interesting and important caveat in the Bouvier and Inoue screens regarding the EC50 and efficacy differences compared to the GtP screens. One issue is to point out the differences but at some point an explanation for the differences need to be addressed, especially when comparing three different screening platforms.15 and, Gs and G12, respectively”. That response also mentions that aspects to consider for the choice of experimental system are now listed in a new paragraph of Discussion. This paragraph references comparisons in the accompanying paper (4) and a recent review describing the pros and cons for the variety of biosensors available (3).As described in the response to Reviewer 1, the differences of screening platforms in terms of their datasets and sensitivity are described in the section “Bouvier’s and Inoue’s biosensors appear more sensitive for GFurthermore, the difference span not only the screening platforms but also the data annotated from literature in GtP. This is noted in the abovementioned Discussion paragraph in the sentence:“Of note, most studies annotated in GtP have used downstream measurements, which are amplified, and may therefore differ from those obtained by biosensors that measure G protein activation (further discussed in (4)).”


With the development of artificial intelligence, researchers can roughly predict the crystal structure of a protein by computer without the need for biological experiments, which provides new ideas and solutions to problems, such as protein-protein interaction and drug-target predictions. In this study, we proposed strategies to combine predicted protein structures with deep learning networks and evaluated them on different protein binding site prediction tasks. Our computational experiment results showed that all proposed strategies could effectively encode structural information for deep learning models.Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations. Despite recent progress in deep learning-based protein structure prediction, there remains a gap between structure and function . The intAlphaFold2 (AF2)  is a DeeSome studies have indicated that directly feeding the coordinates (the position of residues in space) of the proteins into the model is not an ideal choice ,17,18. Ohttps://github.com/Liuzhe30/space-hhblits (created on 26 February 2022).In this study, we curated a protein-protein interaction site dataset containing 1742 human proteins reported in DELPHI  and geneWe selected 1742 human proteins from the dataset reported in DELPHI  to evaluSince the evolutionary information of adjacent residues may improve the prediction accuracy, we defined the neighborhood of each residue as the entirety of all residues with a certain distance centered on the Cα atom itself C. The orIn this work, we prepared five encoding features to represent the protein sequences: OneHot , HHblitsThe OneHot and HHblits profiles, which represent the sequential information and sequence conservatism, respectively, are two classic features in sequence-based protein-related prediction tasks ,29,30. TA is the matrix to be decomposed, U and V are the left and right singular matrices, respectively, and Σ is the singular value matrix. The distance map was represented by the first k vectors of the m is the dimension of the protein sequence, and n is equal to m since the distance map is symmetric.As for encoding protein 3D structures, distance maps of proteins representing the pairwise distance between residues have proved useful in previous research. However, the truncation of over-long proteins is required to adapt the input size of the neural network. It would result in poor scalability of deep learning methods. Singular value decomposition (SVD) could reduce dimension while retaining important components of the original feature as much as possible . Here, wSite-specific mutation studies are designed using the secondary structure (SS) of proteins, which also aids in identifying functional domains . In thisASA is the surface area that is accessible to solvents, and MaxASA is the residue’s largest conceivable solvent-accessible surface area. Here, the protein sequences were used to predict PDB files using AlphaFold2, and DSSP [The relative accessible surface area (rASA) measures the solvent exposure of protein residues . rASA isand DSSP  was usedand DSSP .To assess the contributions of each feature, we designed an MLP model with many inputs. The input was fed parallelly into two hidden layers, a dropout layer, a batch normalization layer, and a dense layer. The model outputs the classification results from the final dense layer with a softmax activation after concatenating all branches of various features . It shouThe model was implemented using Keras  and TensPrecision is as follows:TP) stands for the number of binding sites (residues) that were predicted properly, and false positive (FP) stands for those that were predicted wrongly. It is important to note that, in order to make comparisons easier, we employed just one assessment metric. This was done since we only wanted to compare the advantages of various features within the same model, not the predictor’s overall performance.Precision was chosen as the assessment parameter in this downstream task because predicting binding sites and non-binding sites are regarded as a classification challenge, and we were more interested in the model’s capacity to identify actual protein binding sites. The formula for calculation of Previous work has proposed spatial neighbor–based PSSM to predict RNA-binding sites using structure-known proteins and proved this new encoding strategy outperformed the standard and smoothed PSSM . HoweverIt can be observed that by taking the average performance of the five replicates as the criterion, with the increase in filtering radius, the predicted performance increased first and then decreased compared with the baseline (average precision = 0.276 ± 0.035), reaching a peak at about 7 Å (average precision = 0.630 ± 0.075), probably due to the large size of filters causing information confusion and introducing noise into the model. The experimental results show that the spatial filtering strategy based on HHblits is an effective and acceptable way to encode structural information for deep learning models.We also evaluated the contribution of other features based on the protein structure predicted by AlphaFold2 besides HHblits. This work evaluated the singular value decomposition (SVD) of the distance map, the secondary structure, and the relative accessible surface area (rASA) of each residue. Previous work usually fetched the secondary structure and rASA using sequenced-based prediction tools when the real structures of proteins were unknown ,36. Now As shown in Membrane proteins (MPs) are an important type of protein involved in various crucial biological functions . LocatinAs shown in Variations in proteins may result in disrupting existing protein interactions or forming new interactions . PredictIn this section, we used a protein chain (UniProt ID: P17081) as an example case to assess whether spatial filtering could efficiently encode the structural effects of mutations. We artificially mutated the 97th residue from Q to R (P17081_Q97R), as shown in HHblits−classicalR and HHblits−spatialR using the following formula:We then produced standard HHblits profiles for P17081 and P17081_Q97R and performed spatial filtering based on their corresponding structures. In spatial filtering, the average value is calculated each time when a neighbor is added to amplify the benefit of smoothing brought by filtering. Next, for standard HHblits and spatially filtered HHblits profiles, we calculated the change rates, As shown in The computational experimental results show that AlphaFold2 might have the ability to predict a shifted 3D structure of the mutant protein from the corresponding wild-type protein, and the spatial filtering strategy might have the potential to embed the structural variation information into deep learning models when a mutation happens. This approach provides a new feature of the mutant protein carrying information different from the original protein, which might be helpful in some sequence-based deep learning tasks, such as predicting the effect of mutations on PPIs. Although AlphaFold2 currently is unable to predict all the structural effects of missense mutations correctly, it is conceivable that the incorporation of structure-disrupting mutations experimental data will enable this feature in future versions of protein structure prediction programs .The appearance of AlphaFold2 sheds new light on some tasks that are limited by the need for known protein structures. In this study, we evaluated the effects of spatial filtering, SVD of the distance map, calculating the secondary structure of proteins, and the rASA feature with the help of AlphaFold2 on single-sequence protein binding site prediction. The computational experiment results showed that all these strategies were effective and suitable to encode structural information for deep learning models. These coding strategies with AF2-predicted structures give new predicted features that might be useful for some deep learning-based prediction tasks.Some limitations can still be found in our work. Firstly, in the computational experimental design, some variables, such as sliding window size, batch size, and model architecture, were not tested. Since we only want to measure the benefit of different features calculated from the PDB files predicted by AlphaFold2, rather than evaluate the overall performance of different deep learning networks, only a simple MLP model was implemented, and the parameters of the model were fixed after simple adjustments based on experience. Moreover, limited by computing cost, we did not evaluate the distance map but reduced its dimension with the help of SVD. Using a sliding window will undoubtedly further reduce the information carried by the distance map, which may lead to the deviation of the original effect of this feature.It is believed that the emergence of AlphaFold2 brings both opportunities and challenges. In the future, we will continue to explore different coding approaches to AlphaFold2′s outputs and validate them on more downstream tasks (including mutation-containing tasks) with more complicated deep learning networks. Since there are many well-established models for protein and protein complexes structure prediction other than AlphaFold, we will also evaluate various coding strategies of the 3D structure predicted from different predictors in our next version.In this study, we evaluated the effects of five processing strategies of coordinates generated by AlphaFold2, namely spatial filtering, the singular value decomposition (SVD) of distance map (8D and 16D), and calculating the secondary structure feature and relative accessible surface area (rASA) feature of proteins. The computational experiment results showed that all these strategies were effective and suitable to encode structural information for deep learning models. These strategies generate new predicted features that might be useful in some deep learning-based tasks, such as predicting the binding site of proteins from primary sequences.


The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors’ degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations. The infection possesses a clustering onset and targets the respiratory framework of its host, causing influenza-like sickness with symptoms such as cough, fever, and then, in progressively serious cases, troubled breathing . COVID-1S protein displays a high degree of genetic variability in the virus genome [SARS-CoV-2 has been confirmed to enter the host cells by contact between its receptor-binding domain (RBD) of the spike (S) protein and angiotensin converting enzyme 2 (ACE2) of the host cell ,5. Althos genome , and itss genome . For exas genome ,10,11. AThe biological network approach, which is based on local and global topological features of the network, has been extensively used in various biological fields , and hasIn this study, we propose a protein co-conservation weighted network (PCCN) model simply based on protein sequence to investigate the mutation effects on the spike protein . We thenCS) and co-conservation (CCS) using ProCon [To calculate the conservation of the spike protein, the protein family was initially searched in InterPro  for the g ProCon ,20.CS is shown in CCS among residues is shown, which followed an overall trend of normal distribution, except that the CCS was small. The results demonstrated that the rate of residues with high conservation residues and low co-conservation was relatively high, which indicated that many of the residues in the spike protein were conserved, but their co-conservation was relatively weak.The distribution of Next, an investigation into the conservation and co-conservation of mutation sites in variants that indicate a significant impact on transmissible, severity, and immunity was conducted. As shown in CCS score that was calculated from the sequence of the protein. As shown in CCS. Thus, the PCCN model could reveal the co-conservation information of residues from a network view.A protein co-conservation weighted network of spike protein was constructed based on the Next, the topological features of PCCN were investigated and the correlation between these network features and the infectious or pathogenicity of SARS-CoV-2 was further explored. The network analysis was performed at two levels: residues and variants.Kw), page rank (P), degree centrality (D), betweenness (B), closeness (C), and shortest path length (L) and found that the mutation nodes had significantly larger Kw, P, D, B, but smaller L than the corresponding non-mutation nodes. The results indicate that the mutation nodes demonstrated more centrality than the non-mutation nodes and that they were topologically important in the PCCN. Moreover, the CS and CCS of the mutation nodes were significantly smaller than those of the non-mutation nodes, which showed that the conservation and co-conservation of the mutation nodes were relatively weak. In the network, 89 nodes (residues) produced substitution mutations in the main variants. To compare the topological features of these 89 mutation nodes with the non-mutation nodes, we randomly selected 89 nodes from the non-mutation nodes and computed their average network parameters for 1000 times to present the network features of non-mutation nodes, which were then compared with the 89 mutation nodes. As shown in Finally, the top three mutation sites and co-mutation sites with the highest network parameters are listed in In this element of the study, 33 special variants were collected that have caused great harm to human society and are considered as highly infectious or virulent. To study the variants from a network view, we compared the network features of variants with those of the non-variants. For the variant group, the average parameter of mutation sites in each variant was used to present the feature for the variant; therefore, there were 33 values in the variant group for each parameter. For the non-variant group, random selection was applied to achieve the same number of non-mutation sites as each variant and computed the average parameter 1000 times. For example, there were five substitutions in the Beta variant, and five nodes were taken from the non-mutation sites 1000 times. The distribution of network characteristics for the same number of nodes is shown in Kw, P, D, and B than the non-variant group  and stability changes with r = 0.271 and p = 6.07 × 10−3 (Kw) and stability changes, as shown in p = 1.10 × 10−2). The results indicate that if the nodes themselves had a high degree of centrality, high page rank, or their neighbors had a high degree, their mutations tended to destabilize the spike protein.DeepDDG  was used7 × 10−3 b. A simiThe binding of the spike protein with the human receptor ACE2 plays a central role in the molecular machinery of the SARS-CoV-2 infection of human cells. Mutations in the spike protein affect the binding affinity with ACE2 and have been established to be proportional to the infectivity of different viral variants in the host cells ,34,35,36p-value of 0.045 and the coefficient of −0.207. Next, we analyzed the co-mutation effects on the spike protein. The correlation of the shortest path length and CCS between two mutations with the BFE changes of the two mutations were calculated, respectively. As shown in p = 1.00 × 10−3) and shortest path length (r = 0.179 and p = 6.15 × 10−8) from the two mutation sites. The results demonstrate that if the two mutation sites have the tendency of high co-conservation or larger distance, they might induce larger BFE changes. Moreover, the top three co-mutation pairs with the largest BFE were D215G–D614G, S371F–D614G, and D614G–T732A. Notably, D614G was involved in all of them, which has been reported to have an important impact on infectivity [TopNetTree  was emplectivity ,25,26,27Over the past years, the constant emergence of new variants of SARS-CoV-2 has presented challenges in the prevention of the spread and treatment of COVID-19. Studying the mutation effects on the spike protein from SARS-CoV-2 remains important. In this study, we proposed a sequence-based network model called PCCN that contains conservation and co-conservation information to study the mutation effects on the spike protein from a network view. We found that the mutation sites in the PCCN not only demonstrated lower conservation and co-conservation tendencies than the non-mutation sites, but also showed significant topological differences such as the average neighborhood weighted degree and page rank, both from non-mutation sites at the residue level and variant level. The results mean that the PCCN model could capture the topological features of mutation sites and thus has the potential to predict the mutation sites from non-mutation sites.The stability and binding free energy of the spike protein were correlated with SARS-CoV-2 infectivity ,34,35,36However, this study had two potential limitations. First, the limited number of variants may result in bias for the topological feature comparison analysis between the variants and non-variants. Second, the relationship between the topological features and spike protein function needs further validation in large datasets or experimental verification. In future study, we will continue to collect the variants to explore and enhance the application of PCCN for the SARS-CoV-2 study.Taken together, the sequence-based PCCN presented here not only suggests topological differences in mutation sites from non-mutations, but also provides new insights into the mutation effects on the spike protein physico-chemical characteristics without any protein structure information. Moreover, the PCCN was only based on the sequence information, which means that it can be quickly migrated to study the effects of continuously emerging variants of SASR-CoV-2. This is an adaptive way to face the emergence of new strains or viruses in the future.https://www.ecdc.europa.eu/en/covid-19/variants-concern, accessed on 9 July 2022). The variants were divided into four types: variant of concern (VOC), variant of interest (VOI), variant under monitoring (VUM), and de-escalated variants (DEV). Then, 33 variants with currently available evidence of increasing transmissibility, immunity, or infection severity were kept for further analysis  of each residue and the co-conservation score (CCS) between residues in the spike protein were calculated by ProCon [The conservation score , and they were mapped between 0 and 1:The raw CCS larger than 0.268 were selected as strong co-conservation.Co-conservation scores for all possible residue pairs can be calculated by ProCon, and the larger the score, the stronger the co-conservation tendency. However, most of the residues pairs had weak co-conservation. We tried to keep more residue pairs with relatively strong co-conservation to construct the network. As shown in CS and CCS denote the normalized conservation score and co-conservation score, respectively, in the later section.In order to simplify the representation of the score, the V is defined as follows:i is the residue id in the protein; iv is the corresponding residue (node) for I; and N is the number of residues in the protein.The spike protein was represented as a network consisting of a set of nodes and edges. Each amino acid residue is presented as a node. The node set CCS between them was larger than 0.268. Thus, the adjacent matrix W of the PCCN was defined as follows:An edge was placed between two residues if the CCS. The links between adjacent residues along the backbone were also kept and their weights were set at the threshold 0.268 if the CCS between them was less than the threshold. Thus, the PCCN is a network model that contains co-conservation information among residues.When two nodes had strong co-conservation, that is, the co-conservation score was greater than or equal to the threshold of 0.268, there was an edge between the nodes and the weight of the edge was the Kw), page rank (P) [B), degree centrality (D), and shortest path length (L). For node iv, the parameters Kw, D, B, and C are defined as follows:iv, and s to node t.After constructing the PCCN, we analyzed the network topological characteristics of the residues in the network. Six network characteristics were used in our work: average neighborhood weighted degree (rank (P) , closenePage rank was originally designed to measure the importance of the website pages. Here, it was used to measure the importance of a node . While iΔΔG) upon a single mutation from variants was estimated using TopNetTree [iΔΔG and jΔΔG are the binding free energy changes following mutation i and j, respectively; and ijΔΔG is the combined changes of both mutations i and j.The change in binding free energy  with the identification code QHD43416 that was generated by the D-I-TASSER/C-I-TASSER pipeline [The effect of the amino acid substitutions on stability was calculated using DeepDDG . Similarpipeline .The Spearman’s correlation coefficient was used to analyze the correlation between the protein properties and network characteristics. The Wilcoxon rank-sum test was applied to analyze the statistically significant differences and was implemented by SciPy .In conclusion, we proposed a network model known as PCCN to study the mutation effects on the spike protein only from the sequence information. The topological features of PCCN showed a significant difference at both the residue and variant levels. In particular, the page rank and average neighborhood weighted degree could reflect the mutation effects on the spike protein stability, while the co-conservation score and shortest path length could indicate the mutation effects on the binding free energy changes. The PCCN model offers a new approach for studying variants that may cause future pandemics based on the sequence and topology information.


Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach  used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis. Proteins play crucial roles in almost all biological processes in cells. These important biomolecules, particularly proteins, accomplish their roles by using intermolecular interactions, such as the identity, dynamics, and specificity of protein interactions ,2. ExperWith the rapid growth in our knowledge of genetic variation at the sequence level, there is increased interest in linking sequences with the change in molecular interactions. However, the current experimental approaches cannot meet the demand for residue-level information on these interactions. Recent work has demonstrated the accuracy of co-evolution-based contact prediction for monomeric proteins using global statistical models ,7,8. TheIn contrast to the extensive work on monomeric proteins, little is known about the utility of such statistical models for predicting protein–protein interactions or protein complexes. Coevolution is at the basis of many modern computational techniques for characterizing protein−protein interactions. Therefore, as shown in Although the above methods developed the genomic-based and phylogeny-based methods to generate protein complex multiple sequence alignments, few standalone pipelines/programs exist that efficiently generate sensitive protein complex MSAs from the input protein complex sequences; hence, there was an urgent need to address this issue. Inspired by the protein monomer MSA algorithm DeepMSA, we developed and released cpxDeepMSA, a new open-source program to construct deep and sensitive protein complex MSAs by merging sequences from three different strategies through a hybrid homology–detection approach.We evaluated our cpxMSA method for contact prediction using the state-of-the-art programs CCMpred  and trRoThe genomic-, phylogeny-, and STRING-based methods for cpxMSA construction complement each other. Generally, for prokaryotic species, the genome-based method works better, and for eukaryotes, our phylogeny-based method works better, as shown in top 10 contacts of RF MSA by CCMpred and trRosettaX were 0.4% and 51.9%, respectively. cpxDeepMSA achieved precision for the top 10, with 38.5% and 55.2%, which were 9225.0% and 6.4% higher than RF MSA with CCMpred and trRosettaX, respectively.To further investigate the effectiveness of cpxDeepMSA, we list in To enhance the value of its practical applications, the web server for cpxDeepMSA was established. Below, we further give a step-by-step guide on how to use the web server to obtain the desired results.https://zhanggroup.org/cpxDeepMSA/, you will see the top page of the cpxDeepMSA on your computer screen, as shown in Opening the web server at The cpxDeepMSA results page consists of seven sections: (i) A summary of the multiple sequence alignments and sequence analysis compressed package files A, (ii) aSection vi F shows tStage 1. First, download the Uniclust30 (version: 2018_08) [Stage 2. There are five steps in the cpxMSA construction based on the protein monomer sequence and species similarity search. First, download the taxonomy database from the National Center for Biotechnology Information (NCBI) public database. Secondly, compare the multiple sequence alignment information MSA_A and MSA_B of sequence A and sequence B in Stage 1 with the taxonomy database, respectively, to obtain the species information of the proteins in MSA_A_phy and MSA_B_phy, respectively. Third, rank the similarity of proteins and query sequences in each species in MSA_A_phy and MSA_B_phy from high to low. Fourth, let plex see .https://cn.string-db.org/) of the public database. (ii) Use the multiple sequence alignment HHblits program to search for the protein interaction sequence information (STRING) of sequence A and sequence B, respectively, and obtain the multiple sequence alignment information MSA_stringA and MSA_stringB, respectively. (iii) According to the protein interaction information (STRING linker), determine whether any two proteins, protein i and j in the MSA_stringA and MSA_stringB, have interactions. If there is an interaction, connect the two. In summary, according to steps (i)–(iii), construct an interaction-based multiple sequence alignment (MSA) of the protein complexes.Stage 3. The main points of the process (according to the protein interaction network to build cpxMSA) are as follows: (i) Download the protein interaction information (STRING linker) and protein interaction sequence information (STRING database) from the protein interaction network database (STRING version 10.5v: Necs):L is the length of the query protein complex and N is the number of sequences in the protein complex multiple sequence alignment (MSA). A in sequence A in sequence B in sequence B in sequence The number of effective sequences of the protein complex multiple sequences alignment .We developed an open-source pipeline, cpxDeepMSA, to provide a cpxMSA algorithm that is high-quality, large-depth, and provides a wide range of sequence sources and strong generalization abilities. cpxDeepMSA was proposed to solve the shortcomings of low-quality cpxMSA results due to a single database and low search depth. The advantages of cpxMSA by cpxDeepMSA are as follows: (i) It increases the depth of MSA. The depth of MSA, not just using the single search algorithm or database to align, can also judge according to the number of valid sequences in the MSA results from the previous layer. (ii) The proposed method enhances the generalization ability by using different protein monomer databases and three different monomer MSA strategies  to join the monomer MSA results in protein complexes. The online server and the standalone program of cpxDeepMSA are freely available at


Identification of proteins is one of the most computationally intensive steps in genomics studies. It usually relies on aligners that do not accommodate rich information on proteins and require additional pipelining steps for protein identification. We introduce kAAmer, a protein database engine based on amino-acid k-mers that provides efficient identification of proteins while supporting the incorporation of flexible annotations on these proteins. Moreover, the database is built to be used as a microservice, to be hosted and queried remotely. Protein databases increase in size as new variants, orthologous and novel genes, often found in metagenomics studies, are being sequenced. This is particularly true within the microbial world where bacterial proteomes’ diversity follows their rapid evolution. For instance, UniProtKB (Swiss-Prot/TrEMBL)4. Although other approaches (such as DIAMOND5) have considerably improved the speed of searching proteins in large datasets, from a database standpoint much can be done to offer a more versatile experience. One such approach would be to expose the database as a permanent service, which can make use of computational resources for increased performance (e.g. memory mapping) and leveraging the cloud for remote analyses via a HTTP API. Another approach would be to extend the result set with comprehensive information on protein targets to facilitate subsequent genomics and metagenomics analysis pipelines.Identification of proteins often relies on accurate, but slow, alignment software such as BLAST or hidden Markov model (HMM) profile-based software6 to genotyping variants7, as well as genomics and metagenomics classification10. In the present study, we introduce kAAmer, a fast and comprehensive protein database engine that was named after the usage of amino acid k-mers (kAAmer: k-amino-acid-mer) which differs from the usual nucleic acid k-mers. We demonstrate the usefulness and efficiency of our approach in protein identification with a protein domain database and antibiotic resistance gene identification from a pan-resistant bacterial genome.Alignment software typically relies on a seed-and-extend pattern using an index (two-way indexing in DIAMOND) to make local alignments between query and target sequences. However, there is a plethora of research techniques to bypass the computational cost of alignment. Alignment-free sequence analyses usually adopt k-mers (overlapping subsequences of length k) as the main element of quantification. They are extensively used in DNA sequence analyses ranging from genome assemblies11. LSM-trees are used in data-intensive operations such as web indexing13, social networking14 and online gaming16. KAAmer uses Badger17, an efficient implementation in Golang (https://golang.org/) of a WiscKey KV  store16. WiscKey’s LSM-tree design is optimized for solid state drives (SSD) and separates keys from values to minimize data movement during the creation of the key-value store. KAAmer will obtain peak performance with modern hardware such as solid-state drives that offer good throughput in input/output (I/O) operations per second (IOPS) and will effectively accommodate use cases where many queries are sent simultaneously. A kAAmer database includes three KV stores  Key-Value (KV) stores18. We evaluated four software: Blastp (v2.9.0+)3, Ghostz (v1.0.2)19, Diamond (v0.9.25)5 and kAAmer (v0.6). Other interesting software that make use of web servers for remote analyses, such as Sequenceserver20 and MMseqs221, are worth mentioning for the functionality that they offer. However, we limited our benchmark to the previously mentioned software for their alignment efficiency and their computational resource requirements. Note that we have tested two modes for Diamond, the more-sensitive and fast ones. Similarly, we tested two sensitivity modes with kAAmer, based on the minimal number of shared k-mers, which are k10 (at least 10 shared k-mers between protein query and target) and k1 (at least 1 shared k-mer). For kAAmer, each sensitivity mode was tested without alignment—the ratio of shared k-mers serving as a scoring function and also with a subsequent alignment form. The alignment purpose in kAAmer is to improve the scoring metrics while using the same result set as the raw alignment-free method.In order to evaluate the performance and precision of kAAmer, we built a speed and a sensitivity benchmark against protein families of the ECOD database (homology groups)Figure 22 and compared the kAAmer results with the ResFinder (v3.2 and database 2019-10-01)23 and CARD (v5.1.0)24 software and database. The query genome is a pan-resistant Pseudomonas aeruginosa strain E613095225. Table aac(6’)-Il, ant(2”)-Ia and aacA8) were only found with kAAmer (NCBI-ARG) and ResFinder. On the other hand, several more antibiotic efflux systems are annotated in CARD and the number of identified efflux proteins in E6130952 goes up to 36 while only 3 were reported by kAAmer (NCBI-ARG) and none by ResFinder. Also 2 genes associated with resistance to peptide antibiotics  and 2 other  associated with multiple antibiotic classes were only reported by CARD. Other tested use cases include genome annotation and metagenome profiling as shown in the Methods section.In order to accomodate real-use cases, we built relevant kAAmer databases and investigated their usage in typical bacterial genomics analyses. It should be noted that annotation of genomes and gene identification rely heavily on the quality of the underlying database. What kAAmer has to offer is the inclusion of the protein information within the database combined with an efficient search functionality to facilitate downstream analyses. Therefore, we also provide utility scripts to demonstrate these use cases. The first use case was to identify antibiotic resistance genes (ARGs) in a bacterial genome and test its accuracy related to other ARG finder software. For ARG identification we used the NCBI Bacterial Antimicrobial Resistance Reference Gene Database v2020-01-06.1)-06.122 aIn summary, kAAmer introduces a fast and flexible protein database engine to accommodate different genomics analyses use cases. It can be hosted on-premise or in the cloud and be queried remotely while offering a flexible protein annotation scheme. Although it can be adapted to find more distant homology, it is best suited to quickly find close sequence homology with its k-mer matching functionality, while providing rich annotations on the identified protein targets.17 engine and protein annotations are encoded using Protocol Buffers26.KAAmer design was influenced by our requirements that protein databases would be permanently hosted (on premise or in the cloud), queried remotely and would have room to scale as sequence databases grow in size. It also needed to be multithreaded for protein searches and would support alignment for more accurate remote homology findings. We opted for a Key-Value store engine that would reside on disk and be optimized for SSDs. We used the Go programming language for its versatility and efficiency. The Key-Value stores use the BadgerKAAmer is first used to build a database in which all amino acid k-mers are associated with proteins in which they are found. It consists of three KV stores to hold the database information . The first KV store (k-mer store) keeps the association of every k-mer (key) with a hash value (key length: 8 bytes) that is the entry to the combination store. The k-mer size is fixed at 7 amino acids to fit k-mer keys onto 32 bits (4 bytes) and thus maintain a manageable final database size while keeping a k-mer size long enough for specificity. The second KV store (combination store) is used to hold all the unique sets of protein identifiers. The method used to build this store can relate to the flyweight design pattern or the hash consign technique. Indeed, hash values are reused to access identical objects and therefore minimize memory usage. The set of protein identifiers are the keys to the third store (protein store) which contains the protein information found in the raw annotation file. The raw input file can be either in the EMBL format, GenBank format, TSV format or in FASTA format.27. Options for the alignment mode include the substitution matrix and the gap open and gap extend penalty values. The user can also control the minimal number of k-mer matches to report a hit as well as the minimal ratio of k-mer matches over the protein queries.Once we have a database, we expose it with the kAAmer server that listens over HTTP for incoming requests. The benefits of using such a service are two fold. First, the database is opened once and is memory mapped to increase the performance of protein searches. Second, the kAAmer server can be hosted virtually anywhere, in the cloud for instance, and be queried remotely by the kAAmer client. Note that it is preferable that the latency (time required for a message to be transported over HTTP) between the server and client be as low as possible. KAAmer supports protein query and translated DNA query from FASTA input as well as short reads sequences (like Illumina) in FASTQ format. The default mode in kAAmer finds and reports k-mer exact matches (“kmatch”) with target proteins from the database. However, kAAmer also provides an alignment mode  that produces protein sequence alignments on the k-mer matches’ result set with the Smith-Waterman algorithm, as implemented in the biogo package28.The ECOD database contains 149,091 protein sequences classified into 88 homology groups. The sensitivity benchmark is based on those homology groups. For each homology group, we identify all the homology pairs where the query is effectively part of the analyzed group. A true positive occurs when the subject of the detected homology belongs to the same group of the query and a false positive when it belongs to a different group. Each protein can only be identified once from the query set and it must not be the same protein that reports the homology, therefore the maximum number of true positives is equal to the size of the database. The ROC curve is built by changing the score threshold (bit score or number of shared k-mers) and by counting the number of true and false positives respecting ithttps://github.com/zorino/kaamer-benchmark.For the speed benchmark on the ECOD proteins database, we randomly and uniquely extracted multiple sets of sequences, with the number of sequences ranging from 1 to 50,000. Each set of sequences was in its own FASTA file to be queried against the whole database with the different alignment software included in the benchmark. The benchmark for all four software (Blastp (v2.9.0+), Ghostz (v1.0.2), Diamond (v0.9.25) and kAAmer (v0.6)) was run on nodes geared with 32 cores (2nd generation AMD EPYC Processor), 64 GB of RAM and with a NVMe connected SSD (Amazon EC2 c5ad.8xlarge instance). Software were run with default parameters, except for the number of threads set to 32 and the maximum number of results equal to the total number of proteins in the database. We should note that Diamond yields faster results with large query sets, however, comparison purposes of speed and sensitivity both benchmarks used the same ECOD dataset. More details on the benchmark is provided at https://github.com/zorino/kaamer_analyses and a Python script is provided for each one of the analyses. For the genome annotation, we used the chromosomal sequence of the same Pseudomonas aeruginosa strain (E6130952) as in the antibiotic resistance genes identification. The kAAmer database that was used for the homology detection is a subset of RefSeq from the Pseudomonadaceae family which is available from the kAAmer repository (see Data availability) along with other bacterial family databases. Essentially the genome annotation script parses the kAAmer results and produces a GFF  annotation file giving some threshold on the protein homology. The other use case is the profiling of a metagenome based on the MGnify database of the human gut29. MGnify includes protein annotations from gene ontology, enzyme commission and kegg pathways, among others. The metagenome profiling script will parse the results and produce a summary file by annotation that counts the presence and abundance of each feature.Apart from the antibiotic resistance gene (ARG) identification use case, we also provide two demonstrations of kAAmer usage in bacterial genome annotation and metagenome profiling. The use cases are documented at


Advances in computational modeling have led to an increasing focus on larger biomolecular systems, up to the level of a cell. Protein interactions are a central component of cellular processes. Techniques for modeling protein interactions have been divided between two fields: protein docking (predicting the static structures of protein complexes) and molecular simulation . Our study combined the two approaches to reach very long simulation times. The study makes the model more adequate to the real cells, to explore cellular processes at atomic resolution to better understand molecular mechanisms of life, and to use this knowledge to improve our ability to treat diseases. Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association . Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution. Rapid progress in experimental and computational techniques is redrawing the map of molecular and cellular biology, eliminating old boundaries between research fields, and creating opportunities for breakthroughs. In structural biology, AlphaFold has achieved unprecedented near-experimental accuracy in predicting the structure of individual proteins  and, at 712The great accomplishments in structure prediction based on deep learning do not solve the protein docking problem. This problem, traditionally thought of as a 3D problem, simply requires adding the missing time coordinate from the docking space-time continuum. Refocusing docking from the problem of finding the unique global minimum solution to sampling the enormous multitude of transient interactions , 16 domiEscherichia coli cytoplasm were run for 20 μs in rigid-body all-atom representation  states, focusing only on the set of high-probability (low-energy) states corresponding to the energy minima. The “minima hopping” paradigm has been widely used since the early days of molecular modeling for the sampling of the energy landscapes of biomolecules, such as conformational analysis of biopolymers , rotamer3vee.molmovdb.org) .Set 2. Set 1 plus green fluorescent protein  for comparison with the experimental data on GFP diffusion.Set 3. Three small proteins  from Feig and coworkers  .Set 1. F3 grid  with periodic boundary conditions. Each protein had an equal share of copies . The total number of protein copies and the step of the grid were calculated according to the preset protein volume fraction V. In this study, we used a range of volume fraction values, from V = 0.10 to close to physiological V = 0.30. SI Appendix, Table S2 shows the total number of molecular copies corresponding to each volume fraction.For the starting point of the simulation, the proteins were placed on a cubical grid of a preset size, with the step of the grid calculated according to the desired protein volume fraction. In this study, we used a 500 × 500 × 500-ÅSI Appendix, Fig. S1 shows a fragment of the initial state of the system before the start of the simulation.The proteins were placed in a random order. They were randomly rotated and translated within half of the grid step interval. No collision check was applied at this stage since the collisions were eliminated at the start of the simulation. An MC procedure was developed to simulate the cellular environment with proteins in rigid-body approximation, using an all-atom representation. The procedure by design is based on proteins transitioning between different protein–protein associations. Thus, our approach applies to crowded protein environments only, in which proteins encounter each other in close proximity and monomeric states  are uncommon. The energy landscape of the system is represented by our GRAMM FFT docking  scores/eThe position of each protein is described with the 3 × 3 rotation matrix and the translation vector relative to the origin of the coordinate system. Protein–protein docking poses are systematically precomputed for all of the rotations and translations of each protein, relative to all of the other proteins in the system by GRAMM docking, unscored and unrefined, at intermediate resolution, previously optimized for the docking of unbound proteins  (grid stThe docking results are stored on 6D grids (3 translations and 3 rotations), accessed during the MC runs. The MC move is initiated by a random selection of a protein (“ligand”) considered for a move to proteins (“receptors”) within a certain neighborhood (described below) from the current position of the ligand. The receptor to move to is selected randomly among all of the neighborhood proteins. Our minima-hopping paradigm, based on the approximation of the Lennard-Jones potential (see above), assumes only the short-distance interactions between the immediate docking partners. The presence of the neighboring proteins not selected for this move is accounted for by the detailed balance condition in the Metropolis acceptance criterion (described below). Once the ligand and the receptor are selected, the move is chosen randomly among the precalculated 30,000 lowest energy-docking matches for that ligand-receptor pair.The simulation step is completed when all of the proteins have attempted to move. Once the ligand moves, the energy (GRAMM docking score) of the new match is added to the energy of the ligand and the energy of the old match it detaches from is subtracted. Correspondingly, the energy of the new receptor adds the energy of that new match, and the energy from the old receptor (the one the ligand is detaching from) subtracts the energy of the detaching docking match.α-Cα minimal distance of 8 Å. The moves resulting in collision are rejected. The move is accepted or rejected based on the Metropolis acceptance criterion . Ligands (L) are allowed to move to the neighboring receptors (R) only , defined as those within the distance between R and L geometric centers less than the sum of the R and L radii, plus 50 Å, to accommodate binding to the first layer of receptors in the crowded environment. Collision check is performed for each attempted move according to the CijP of move from step i to step j had to be the same as jiP from j to i. Accordingly, the Metropolis criterion was normalized  from state m with probability to be selected 1/mN; mE is the energy of state m; and T is the temperature .The detailed balance condition for the system was implemented. The probability rmalized  as[1]Piji and j. We also assume the same curvature of the potential wells of each state. Thus, in the Kramers’ (or Arrhenius) rate equation, which for our system can be written ask is the rate constant and ijP is the energy and temperature-based probability of move from step i to step j  to the acceptance probability of a new state.As noted above, in our system, the monomeric states have energy zero, and all of the minima have negative energy values. Our model assumes no additional barriers between states o step j 1, the prE, the average energy of a molecule ; the shift (the average length of a molecule’s move per simulation step); the MSD (the average mean square deviation of a molecule’s geometric center after unwrapping coordinates from the periodic boundary conditions); acceptance rate (percentage of accepted moves); and the aggregation number cN (the average number of proteins in an aggregate/oligomer formed by docked proteins). To allow off-the-grid relaxation of the system, the reference position for MSD calculation was set at step 100. Diffusion rates tD were calculated from the slope of MSD according to the Einstein relationship tD = MSD(t)/6t, where t is the lag time.The observed parameters of the simulation (per simulation step) were potential energy SI Appendix, Fig. S3) showed that at low temperatures, the system is frozen (little to no movement of the proteins). At high temperatures, the system is overheated (moves accepted regardless of the energy). The melting curves  had a clear inflection point at T = 100, consistently at all volume fractions, at which the system melts (breaks from the freeze) but is not overheated yet, and thus is likely most representative of the physiological conditions. The value of T corresponding to the melting phase transition reflects the docking energy landscape (mapped in GRAMM energy units), as follows from The results of the simulation on the 5 mix set at the physiological volume fraction  and loweT = 100 . This confirms the robustness of our approach and adds evidence to the validity of our approximation. Accordingly, for the rest of this study, we used T = 100 as the temperature of the systems.Simulation on the 3 mix set, which is a very different system from the 5 mix set , yielded virtually identical melting behavior, at all volume fractions, with the same optimal temperature tD value was determined to be 3.5 Å2/ns, which, according to the authors, is three times greater than in the experiment. Our simulation of the villin within the 3 mix protein set at the physiological volume fraction  allowed us to calibrate the time variable t of our system by matching the tD values calculated as tD = MSD/6t , assumes close proximity of the minima to one another . Thus, our approximation would not hold for dilute systems. However, it allows for an observation of quantitative characteristics at a range of volume fractions. In our study, this range was set from 0.1 to close to physiological 0.3.As described above, the melting temperature for very different protein systems—the 5 mix set of average size proteins and the 3 mix set of much smaller proteins—at the full range of volume fractions, from 0.1 to 0.3, is the same. This supports the validity of our approximation and its consistency across different concentrations and size scales of proteins.E. coli  showed that the GFP diffusion rate was 0.3 Å2/ns, in excellent agreement with the experiment. It provides another confirmation of the approach validity and consistency across very disparate systems of proteins.Experimental data on the diffusion of GFP in the cytoplasm of E. coli  puts thetD with the increase in the protein volume fraction V in accordance with long-established evidence (tD = D0exp [−γ V/(1 − V)], where D0 is the dilute diffusion rate and γ is a constant characterizing the slowdown of the diffusion with the increase in the volume fraction (D0 = 4.9 Å2/ns and γ = 7.7 in our simulation). The quantitative scope of this slowdown according to the ratio of the diffusion rates, for our range of volume fractions, is available from the MD simulation for villin as 5.4, from V = 0.1 to 0.3 . The rate of the slowdown scales exponentially with the size of the protein defined by the number of residues N .It is well established by experiment and simulation that larger proteins diffuse at a slower rate , 60. DuetDmax/tD) would have linear dependence on the radius. Our data , based on the protein size defined by the radius-related metric R = N1/3, show that this dependence is close to linear at lower volume fractions. However, the slowdown rate becomes more pronounced for larger proteins, deviating to exponential at closer to physiological concentrations  for lysozyme cN ≅ 5 , since according to our move set, this energy is determined by the number of the protein’s interfaces with other proteins.Experimental data on the aggregation of proteins (cluster formation) at close to physiological concentrations point to the aggregation number e Nc ≅ 5 , and mon= 4 to 6 . Our dat= 4 to 6 A, at theractions A. This ecN = 1 to 10 range (V = 0.3 and 7% for V = 0.1) are monomers .The distribution of the cluster sizes B is in q10 range . On averThe existing estimates of the proteins’ residence time (the lifetime of a protein pair) vary dramatically among the studies. An experimental study of lysozyme protein solution determined that the protein clusters (complexes) have a lifetime longer than the time required to diffuse over a distance of a monomer diameter . Such a 3 box  for 10,000 steps (200 μs) at volume fraction 0.3 . Protein docking algorithms  are far more efficient in sampling the spatial coordinates. However, they do not account for the kinetics of the association . The approach put forward in this study bridges the two modeling techniques. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise FFT docking and sampled in space and time using MC simulations. The approach is capable of reaching unprecedented simulation timescales at all-atom resolution.The simulation protocol was parametrized on existing MD data and validated on observations from experiments and MD simulations. The simulation performed consistently across very different systems of proteins at a broad range of concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells at atomic resolution.The long timescale atomic resolution simulations will provide the tool to explore the dynamics of cellular processes in structural detail and address important biological questions based on the molecular mechanisms involving protein association, such as cell signaling pathways and cellular metabolism. These simulations can provide important insights into fundamental biological problems of the specificity of protein interactions, facilitate studies of multinetwork phenotypes, emergent behavior in cellular protein systems, and advance our ability to modulate interaction networks.This proof-of concept study is obviously just the very beginning of an expansive task of incorporating other types of macromolecules, using more sophisticated force fields that include electrostatics and solvent effects, more accurately accounting for energy barriers, optimizing the move set based on the moves acceptance probability, introducing structural flexibility, adding membrane environment and other cellular components, multiscale modeling, and improving computational efficiency. Nonetheless, our study shows that approaches grounded in protein docking can produce unprecedented dynamic simulations of protein systems at the cellular scale.Supplementary File