Ophiopogon japonicus as raw material were significantly better than those with Sichuan Ophiopogon japonicus, consistent with our previous research results. Our study reveals the different cardiomyocyte protective effects of Shenmai injections produced by medicinal materials from different origins, laying a scientific foundation for their clinical selection.Shenmai injection is mainly used for the treatment of heart-related diseases, including coronary heart disease, viral myocarditis, chronic cor pulmonale, and shock in Asia. Medicinal materials from different origins produce Shenmai injections for clinical use, and their protective effects on cardiomyocytes may vary with the choice of raw materials. In this study, we compared the protective effects of Shenmai injections produced from different raw materials on cardiomyocytes. Results showed that the protective effects of various Shenmai injections on hypoxia-reoxygenation-induced cardiomyocyte injury were mainly attributed to total ginsenosides extract, with few differences between them. However, the protective effects of different Shenmai injections on doxorubicin and oxidative stress-induced cardiomyocyte injury were significantly different; the protective effects of Shenmai injection with Zhejiang  Cardiovascular diseases are a global health problem that is becoming an increasingly significant cause of death. Many cardiovascular diseases are accompanied by cardiomyocyte ischemia and oxidative stress, leading to cardiomyocyte injury . As adulPanax ginseng C. A. Meyer) and Ophiopogon japonicus (Linn. f.) Ker-Gawl . Its main active components include various ginsenosides, O. japonicas saponins, and O. japonicas flavones. It has the functions of tonifying Qi and preventing exhaustion, nourishing Yin, and promoting body fluid, which could regulate immunity, strengthen the cardiovascular system, and provide resistance against inflammation and oxidization with few adverse reactions [Shenmai injection is a widely used traditional Chinese medicine injection, which is extracted and refined from red ginseng . The first four Shenmai injections (indicated as SM1–4) were made from Sichuan O. japonicas, and the fifth Shenmai injection (indicated as SM5) was made from Zhejiang O. japonicas. The total ginsenosides extract was purchased from the College of Chemistry of Jilin University . H2O2 was purchased from Sigma-Aldrich . A cell counting kit-8 (CCK-8) was purchased from DOJINDO . An annexinV-FITC/PI apoptosis detection kit was purchased from BD Biosciences . A lactate dehydrogenase (LDH) release assay kit, a ROS assay kit, a BCA protein assay kit, N-acetylcysteine (NAC), and resveratrol were purchased from Beyotime .Shenmai injections are produced by five manufacturers; Sichuan and Zhejiang O. japonicas were cut into pieces and soaked with 500 ml 75% ethanol for 15 min at 20°C. Then, the mixture was heat reflux extracted for 2 h twice in 500 ml 75% ethanol. The ethanol extract was collected and condensed to 500 ml under reduced pressure. Extracts were lyophilized and redissolved with an equal volume of Dulbecco's modified Eagle medium (DMEM) for cell experiments.A total of 200 g μg/ml streptomycin at 37°C in a humidified incubator with 5% CO2.Rat cardiomyocytes H9c2 cell line was obtained from American Type Culture Collection  and grown in DMEM with 10% fetal bovine serum, 100 U/ml penicillin, and 100 2 and 95% N2 for 24 h, after which cells were cultured in 5% CO2 and 95% air for another 3 h. Then, cell viability was detected using CCK-8 assays. In this hypoxia-reoxygenation model, the cell survival rate was about 50%, essentially meeting the requirements of the following experiments.H9c2 cells were cultured with mediums containing different concentrations of glucose in a humidified incubator with 5% COμl fresh medium containing 10% CCK-8 agent was added to each well for a further 0.5 h at 37°C. Absorbance at 450 nm was measured using a BioTek Synergy H1 Hybrid Reader .Cells in a 96-well plate were treated with agents for an indicated time, and the medium was collected for LDH release assay. 100 μl of the CCK-8 assay medium was collected in a new 96-well plate. Then, 60 μl of the working solution provided in the LDH release assay kit was added to each well, and the plate was incubated for a further 0.5 h at 37°C. Absorbance at 490 nm was measured.A total of 120 μM ROS probe was added, and cells were incubated for a further 0.5 h at 37°C. After washing with PBS three times, cells were lysed with 300 μl NaOH:CH3OH (V:V = 1 : 1). The fluorescence at 488 nm excitation and 525 nm emission of the cell lysate supernatant was measured.The intracellular ROS levels were detected using a ROS assay kit. Briefly, cells in a 12-well plate were treated with agents for an indicated time, after which the culture medium was discarded. Then, 1 ml of serum-free medium containing a 10 2O2 with agents for an indicated time. Apoptosis was analyzed using an annexinV-FITC/PI apoptosis detection kit. The proportion of apoptotic cells in 1 × 104 labeled cells was quantified with Agilent NoveCyte flow cytometer .H9c2 cells were exposed to doxorubicin or Hn = 3 or 6). Statistical differences were evaluated by a two-tailed Student's t-test or one-way analysis of variance (ANOVA). P < 0.05 was accepted as statistically significant.Data are representative of three independent experiments and were presented as mean ± S.E.M. (in vitro cardiomyocyte hypoxia-reoxygenation model is a suitable simulation of the ischemia-reperfusion of cardiomyocytes in vivo [in vitro. We first explored the model conditions of hypoxia-reoxygenation injury on a rat cardiomyocyte H9c2 cell line. The results showed that hypoxia for 24 h and reoxygenation for another 3 h met the requirements of cell injury under high glucose (4.5 g/L), low glucose (1g/L), and glucose-free (0g/L) conditions. The cell survival results are shown in Cardiomyocyte ischemia is the physiological basis of angina pectoris, myocardial infarction, and other heart diseases. Blood reperfusion plays an important role in the recovery of cardiomyocytes. However, the reperfusion of ischemic tissue produces significant ROS in mitochondria, leading to oxidative damage, cell death, and abnormal immune response . The in  in vivo , 20. TheO. japonicas extracts on H9c2 cells. The results revealed that only total ginsenosides extract could significantly increase cell survival fraction, while Sichuan and Zhejiang O. japonicas extracts had little efficacy (O. japonicas extracts could alleviate the increase of lactate dehydrogenase release induced by hypoxia-reoxygenation, and only total ginsenosides extract could reduce its increase (In the hypoxia-reoxygenation-induced cardiomyocyte injury model, five Shenmai injections had significant protective effects. According to the one-way ANOVA , there wefficacy . The resincrease . These rin vitro. The results are shown in O. japonicas as raw material showed superior efficacy than other Shenmai injections. Therefore, we compared the protective effects of Sichuan and Zhejiang O. japonicas extracts on this model. According to the results in O. japonicas extracts could significantly reduce the death of H9c2 cells exposed to doxorubicin. At the same time, the protective effect of Zhejiang O. japonicas extract was better than Sichuan O. japonicas extract. We investigated the effect of Shenmai injections on doxorubicin-induced cardiomyocyte apoptosis. It could be seen in O. japonicas as raw material might be a better choice.Clinically, the combination of chemotherapeutic drugs with Shenmai injection can enhance the efficacy of chemotherapeutic drugs and reduce their adverse reactions . The mosin vitro and investigated the antioxidative stress effect of Shenmai injections produced by medicinal materials from different origins. The results are shown in Figures μM H2O2 for 3 h, while other Shenmai injections had little efficacy. However, when the dosage of H2O2 increased to 500 μM, only the fifth Shenmai injection showed significant antioxidant activity. The results of antiapoptosis experiments were consistent with the results of cytotoxicity experiments (Figures O. japonicas as raw materials was better than that of the other four Shenmai injections with Sichuan O. japonicas as raw materials, consistent with our previous research results [As cardiomyocyte injury induced by ischemia-reperfusion, anthracycline chemotherapy, and other factors are related to ROS, we next established an oxidative stress model with hydrogen peroxide on the H9c2 cell line  Figures . The one results .Cardiovascular diseases such as coronary heart disease, angina pectoris, and viral myocarditis are often accompanied by cardiomyocyte injury. The main causes include myocardial hypoxia, inflammation, and the accumulation of ROS . In addiO. japonicas have many pharmacological effects related to the cardiovascular field, such as scavenging free radicals, immune regulation, alleviating myocardial ischemia, and arrhythmia. [O. japonicas extract was 3.16 times that in Sichuan O. japonicas extract. Ophiopogonanone B, methyl-ophiopogonanone A, and ophiopogonanone D, among the most important bioactive components of O. japonicas extract, were 5–50 times more abundant in Zhejiang O. japonicas extract than in Sichuan O. japonicas extract. Although the sum of the content of the total ophiopogonins in the two O. japonicas extracts did not differ significantly, the ophiopogonin compositions in the two O. japonicas extracts showed a significant difference and may be used as unique markers to distinguish the two O. japonicas [O. japonicas have noticeable differences, resulting in certain differences in their efficacy and clinical selection of Shenmai injection.hythmia. . Ophiopohythmia. , 31, 32.aponicas . The actO. japonicas extracts had little effect. The main reason may be that the mechanism of hypoxia-reoxygenation-induced cardiomyocyte injury involves ROS and other mechanisms, such as inflammation, apoptosis, and energy metabolism [In this study, we investigated the protective effects of Shenmai injections produced by medicinal materials from different origins on cardiomyocyte injury induced by various factors and preliminarily explored the possible mechanisms. We conducted one-way ANOVA on the efficacy comparison of five Shenmai injections in this study. Results showed that Shenmai injections had a certain effect on cardiomyocyte injury induced by hypoxia-reoxygenation. Their efficacy was mainly attributed to total ginsenosides extract, while tabolism , 34. Muctabolism . Therefo2O2. The efficacy of Shenmai injection with Zhejiang O. japonicas as raw material was significantly better than that of Shenmai injections with Sichuan O. japonicas. Our previous studies showed that there were significant differences in the components of Zhejiang and Sichuan O. japonicas extracts; the anti-inflammatory and oxidative capacity of Zhejiang O. japonicas extract were significantly higher than that of Sichuan O. japonicas extract. These results are due to the higher amounts of ophiopogonones in Zhejiang O. japonicas extract [Shenmai injection also had a significant protective effect on cardiomyocyte injury induced by doxorubicin and H extract . Our stuO. japonicas as the raw material are significantly better than those of Shenmai injections with Sichuan O. japonicas, consistent with our previous research results.In summary, the results of this study showed that the protective effect of various Shenmai injections on cardiomyocyte hypoxia-reoxygenation injury is mainly attributed to total ginsenosides extract, with few differences between them. There were significant differences in the protective effects of various Shenmai injections on doxorubicin and oxidative stress-induced cardiomyocyte injury. Moreover, the protective effects of Shenmai injection with Zhejiang


Physical activity (PA) may influence the secretion of neurotransmitters and thereby have positive consequences for an individual’s vulnerability . This systematic review aims to analyse the potential bidirectional effects of exercise on dopamine from young adulthood to old age. The article search was conducted in PubMed, Scopus, and Web of Science in December 2020. The inclusion criteria were longitudinal and experimental study design; outcomes included dopamine and exercise; effect of exercise on dopamine and vice versa; adults; and articles published in English, Portuguese, or Spanish. Fifteen articles were included in the review. We observed robust findings concerning the potential effects of PA on dopamine, which notably seem to be observable across a wide range of participants characteristics (including age and sex), a variety of PA characteristics, and a broad set of methods to analyse dopamine. By contrast, regarding the potential effects of dopamine on PA, findings were mixed across studies. Thus, there are robust effects of physical exercise on dopamine. These findings further strengthen the idea that innovative approaches could include PA interventions for treating and preventing mental disorders. Therefore, it seems that PA is a potential alternative to deal with mental health issues. Dopamine is a monoamine neurotransmitter that is known to be modulated by physical activity (PA) and exercise . The phyDopamine is critical for the motor system , and it On the other hand, PA seems to influence the central dopaminergic, noradrenergic, and serotonergic systems . In thisThe mechanism that increases dopaminergic activity during exercise is mostly related to fatigue ,32,33. AThese two different lines of research suggest that there is a bidirectional relationship between the practice of PA and dopamine ,11,12,38The review was performed according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses . We searThe inclusion criteria of the articles were (a) longitudinal and experimental study design (study design criterion); (b) outcomes included dopamine and PA (outcome measure criterion); (c) effect of PA on dopamine and vice versa (relationship criterion); (d) adults (participants criterion); (e) articles published in English, Portuguese, or Spanish, regardless of the date of publication (language criterion). Studies were excluded from analysis if they were cross-sectional, did not have PA or dopamine synthesis as the outcome, and if participants were under 18 years of age.In December 2020, a search was performed for articles in PubMed and Scopus databases and the meta-search engine Web of Science. The search was performed using the following terms:  AND (dopamine OR dihydroxyphenethylamine OR oxytyramine OR 3-hydroxytyramine) AND . Two authors screened titles and abstracts to identify articles that met the inclusion criteria. Two authors read the articles and decided whether they should be included in the analysis or excluded. The inclusion decision was consensual and in cases of disagreement, the decision was made by mutual agreement. A reverse search was conducted to screen publications that cited the identified studies. We also checked the reference lists of identified studies for further potential studies to include.From the articles, the author name(s), year of publication, study design, sample characteristics, country, method of the outcome variables, and main results were extracted. The extraction was carried out by one author, and coding was verified by two other authors.We used the Quality Assessment Tool for Quantitative Studies of the Effective Public Health Practice Project to assess the methodological quality and risk of bias of each study . The stuAmong the articles, there was great heterogeneity in the different parameters analysed. This made it impossible to carry out a meta-analysis. To facilitate the interpretation of the results, the data of each article were collected and presented consistently. As the articles presented relationships in different directions, a table was created for articles that analysed the effect of PA on dopamine, and another table was created for articles that analysed the effect of dopamine on PA.n = 38), the article was inaccessible (n = 6), the study design was not experimental or longitudinal (n = 3), the article was classified as a method description article (n = 1), or the article was a review (n = 1). Therefore, 15 articles were included in the systematic review. From the 15 included studies, 7 investigated the effect of PA on dopamine, while the other 8 examined the effect of dopamine on PA. The flow diagram of the study search is presented in From the database search, 940 records were identified. Among those 940 records, 542 were duplicates and were eliminated. The remaining 398 records were analysed based on the title and abstract for potential inclusion in the subsequent full read. At this stage, 334 records were eliminated. The full text of 64 records was evaluated, and 47 were excluded for the following reasons: different outcomes  on dopamine (outcome) are presented in The number of participants in the seven included studies ranged from 6 ,47 to 20Each exercise training protocol and intervention was unique among the seven in-cluded studies. Participants in three studies performed exercise interventions: partici-pants in one study performed bicycle ergometer exercises three times a week for a total of 10 weeks , partici11C) used raclopride positron emission tomography scans to determine the effect of aerobic exercise on the repetitive transcranial magnetic stimulation-evoked release of endog-enous dopamine in the dorsal striatum [18F) fallypride [To examine dopamine, three studies collected blood samples, of which two inves-tigated dopamine-ß-hydroxylase ,46, and striatum . One stustriatum . Lastly,llypride .Overall, six studies found a positive effect of physical exercise on dopamine and one study found no effect. Regarding the exercise intervention studies, one study showed that urine dopamine significantly increased at four weeks , anotherThe characteristics and main findings of the eight studies analysing the effect of dopamine (exposure) on PA (outcome) are presented in Among the eight included studies, the number of participants ranged from 5  to 1635 Seven studies used drug exposure, while one study analysed dopamine without exposure to drugs. Regarding the studies that used drug exposure, in three studies, dopamine was directly administered to the participants at different doses, 2 µg/kg/min  and 3 µgThe seven studies that used drug exposure also assessed PA by performing submaximal or maximal cycle exercise protocols ,54,55,56Concerning the three studies that directly administered dopamine, no significant effects were observed in the cardiac system or aerobic exercise capacity ,51,52. TWe systematically reviewed the existing research on reviewed the existing re-search on the bidirectional relationship between exercise or PA and dopamine. Regarding the effects of exercise on dopamine, the majority of included studies (6 out of 7) reported potential effects of exercise on dopamine ,46,48,50The mental health benefits of PA are well documented . RegularAs exercise is a complex behaviour , there could be different results among the analysed studies because studies differed substantially regarding the characteristics of the respective exercise. However, the majority of the studies spoke for the possibility that the relationship of exercise influencing dopamine may be observable across a variety of exercise characteristics ,48,49,50It is well known that dopamine has a role in motor functioning ,10, whicIt is important to mention methodological details that systematically varied across the eight studies that analysed the effect of dopamine on PA. The seven studies that used drug exposure assessed PA by performing submaximal or maximal cycle exercise protocols and reported either non-significant or mixed results ,54,55,56The current systematic review findings should be interpreted in light of some limitations. Although study quality was assessed, studies were not weighted or ranked, nor were any removed from the review. Therefore, studies with weaker quality were given no less importance than findings from studies with greater quality. Nevertheless, most studies on the potential effects of dopamine on PA presented a ‘weak’ quality assessment. Furthermore, the studies included presented a variety of methods to assess both PA and dopamine, leading to difficulties and caution in the comparisons presented. Grey literature was not searched or included, and six studies were inaccessible. In addition, there was a lack of trials, indicating that larger representative trials are still warranted. Moreover, we found few studies investigating the effect of PA on dopamine and no studies investigating the effect of dopamine on PA in the long term, which also warrants further investigation. Finally, the present review did not focus on the different aspects of the dopamine system. Future research will have to focus on this issue and determine the detailed mechanisms of PA-related changes in the different roles of dopamine in the system.Summarizing the prior empirical evidence on the reciprocal relationship between PA and dopamine, we observed robust findings concerning potential effects of PA on dopamine, which seem to be observable across a wide range of participant characteristics, a variety of PA characteristics, and a broad set of methods to analyse dopamine. By contrast, for potential effects of dopamine on PA, findings were mixed across studies, with only the study of neutral conditions  able to demonstrate a coherent positive effect of dopamine on habitual PA measured by accelerometers.With respect to translating this research to clinical practice, it is appropriate to suggest that health professionals should encourage engagement in PA as a strategy to improve dopamine levels and possibly promote mental health. Furthermore, these findings reinforce the chain of thought suggesting that PA should be considered as an intervention strategy for improving mental health.


Subtraction coronary CT angiography (CCTA) may reduce blooming and beam-hardening artifacts. This study aimed to assess its value in improving the diagnostic accuracy of readers with different experience levels.We prospectively enrolled patients with target segment who underwent CCTA and invasive coronary angiography (ICA). Target segment images were independently evaluated by three groups of radiologists with different experience levels with CCTA using ICA as the standard reference. Diagnostic accuracy was measured by the area under the curve (AUC), using ≥50% stenosis as the cut-off value.p < 0.001). The mean sensitivity of conventional CCTA for each group ranged from 83.3 to 88.0%. Following calcification subtraction, the mean sensitivity decreased for the novice (p < 0.001) and junior (p = 0.017) radiologists but was unchanged for the senior radiologists (p = 0.690). With subtraction CCTA, the mean AUCs of CCTA significantly increased: values ranged from 0.53, 0.54, and 0.61 to 0.70, 0.74, and 0.85 for the novice, junior, and senior groups .In total, 134 target segments with severe calcification from 47 patients were analyzed. The mean specificity of conventional CCTA for each group ranged from 22.4 to 42.2%, which significantly improved with subtraction CCTA, ranging from 81.3 to 85.7% (all Subtraction CCTA could improve the diagnostic accuracy of radiologists at all experience levels of CCTA interpretation. Coronary computed tomography angiography (CCTA) has become a cost-effective first line technique in the diagnosis of patients with chest pain , 2. The In addition, inter-reader variability in CCTA interpretation also differs according to the readers' levels of experience. Even with the use of the Coronary Artery Disease Reporting and Data System (CAD-RADS), the standardized reporting system for coronary artery diseases, the inter-reader agreement of expert readers was stronger than that of early career readers . Severe To address this issue, our research aimed to evaluate the value of subtracting CCTA in diagnosing severely calcified coronary artery stenosis by investigating the impact of this method on the diagnostic accuracy and diagnostic confidence of readers with different levels of experience.Patients with suspected coronary artery disease were prospectively selected to undergo subtraction CCTA at our institution from October 2019 to June 2020. The inclusion criteria were as follows: (1) age ≥ 50 years, (2) basal or drug control heart rate (HR) ≤ 65 bpm, (3) no contraindications for iodinated contrast material, (4) no history of cardiac surgery, (5) sinus rhythm, no history of arrhythmia or heart failure, and (6) willingness to provide written informed consent. The exclusion criteria were as follows: (1) no severe calcifications, (2) no invasive coronary angiography (ICA) or ICA performed more than 1 month after subtraction CCTA, and (3) severe motion artifacts. This study was approved by the ethics committee of our hospital, and all patients provided informed consent.SURE Exposure) with target image noise with a standard deviation (SD) of 22 Hounsfield units. Depending on the body mass index (BMI), 100 or 120 kV was used for both scans. The phase window was set at 70–80% of the R-R interval. Iodinated contrast medium with an iodine concentration of 370 mg I/ml  was injected at a rate of body weight (kg) × 0.07 mL/s in 10 s (fixed), followed by a 50-mL saline chaser bolus.The CT scanner used in this study was a 320-detector row CT scanner  with 0.5 mm detector elements, a rotation time of 275 ms, and the Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm. A two-breath hold acquisition technique was used . A cardiCanon Sure Subtraction Coronary software was used for subtraction CCTA as previously reported . First, Canon Workstation perfusion software  was used to post-process the conventional and subtraction CCTA images. Eight radiologists, including three novices (who had performed between 50 and 100 CCTA interpretations), two junior radiologists (who had performed ~500 CCTA interpretations) and 3 senior radiologists , independently read the CCTA images twice. The first time, only conventional CCTA images were available; the second time, both conventional and subtraction CCTA images were available. All images were anonymized and presented in random order. As the memory washout period, the interval between the two readings was at least 2 weeks. All readers were blinded to the patient's clinically relevant information and coronary angiography results. Coronary artery stenosis was assessed in segments with a diameter of ≥ 1.5 mm based on the 18-segment Society of Cardiovascular Computed Tomography (SCCT) model . The estAll ICA images were interpreted by two expert interventional cardiologists with more than 10 years of clinical ICA experience who were blinded to all patient characteristics and CCTA findings. Any discrepancy between the observers was settled by consensus. Stenosis equivalent to at least 50% of the diameter was defined as obstructive CAD for ICA and CCTA.t-test. Taking the ICA results as the standard reference, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of conventional and subtraction CCTA were calculated for each radiologist and each reading group. Because of the presence of paired samples, the McNemar test was applied to explore group differences in sensitivity and specificity. The overall diagnostic accuracy was measured with respect to the ICA results by the AUC using the total number of points. The AUCs were compared by using the Hanley-McNeil method . Diagnostic confidence was computed for each radiologist and each reading group with different levels of experience. Group differences in diagnostic confidence were compared by using Student's l method . All tesForty-seven patients (26 men and 21 women) were enrolled, with a mean age of 67.1 ± 7.3 years. The median Agatston score was 427.1 (IQR: 235.4–886.3). The baseline characteristics of the patients are summarized in p < 0.001, For the conventional CCTA data, the mean diagnostic confidence was 2.6 ± 1.0, 3.2 ± 0.5, and 2.7 ± 0.9 for the novice, junior, and senior reading groups, respectively. Comparatively, each reading group showed significantly stronger diagnostic confidence with subtraction CCTA data, with mean values of 3.4 ± 0.8, 4.0 ± 0.7, and 4.5 ± 0.7, respectively  and junior radiologists (p = 0.017) but remained similar for the senior radiologists (p = 0.690). The mean specificity of conventional CCTA for each group ranged from 22.4 to 42.2%, which significantly improved following the inclusion of subtraction CCTA, ranging from 81.3 to 85.7% . The mean PPVs and NPVs of subtraction CCTA were both higher than those of conventional CCTA.As shown in p < 0.001), with values of 0.73 (95% CI: 0.67–0.79), 0.76 (95% CI: 0.69–0.83), and 0.85 (95% CI: 0.81–0.90) for the respective groups , 0.56 (95% CI: 0.49–0.64), and 0.63 (95% CI: 0.57–0.69), respectively. Comparatively, the mean AUCs of subtraction CCTA were significantly increased , National Natural Science Foundation of China (grant 81971569), and Beijing Municipal Science and Technology Commission (grant Z201100005620009).YX was employed by Canon Medical Systems (China) Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


However, the scanning tip probes only the molecular surface with limited resolution, missing details required to fully deduce functional mechanisms from imaging alone. To overcome such drawbacks, we developed a computational framework to reconstruct 3D atomistic structures from AFM surface scans, employing simulation AFM and automatized fitting to experimental images. We provide applications to AFM images ranging from single molecular machines, protein filaments, to large-scale assemblies of 2D protein lattices, and demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original topographic AFM image. We show that simulation AFM further allows for quantitative molecular feature assignment within measured AFM topographies. Implementation of the developed methods into the versatile interactive interface of the BioAFMviewer software, freely available at  Atomic force microscopy (AFM) allows to visualize the dynamics of single biomolecules during their activity. All observations are, however, restricted to regions accessible by a fairly big probing tip during scanning. Hence, AFM images only the biomolecular surface with limited spatial resolution, missing important information required for a detailed understanding of the observed phenomena. We employ simulation atomic microscopy (sAFM), computationally emulating experimental scanning, to automatically fit available biomolecular structures into experimental AFM images. This allows to obtain 3D atomistic molecular structures from experimental AFM surface scans. For applications ranging from single molecular machines, protein filaments, to assemblies of protein lattices, we demonstrate how the full atomistic information advances the molecular understanding beyond experimental images. The developed computational methods are embedded within the user-friendly BioAFMviewer interactive software interface, providing the convenient platform for the Bio-AFM community to employ simulation AFM to better understand experimental results. Nowadays nanotechnology allows to observe how single proteins work. Under atomic force microscopy (AFM), e.g., the protein surface can be scanned to image functionally important conformations –5. The dOn the other side, high-resolution molecular structures of proteins are known  and confThe strengths of the stand-alone BioAFMviewer software are its user-friendly versatile interface and rich functionality. The visualization of molecular structures and simulated scanning to display corresponding AFM graphics proceeds in a synchronized way, even for very large protein structures. Scanning parameters such as tip-shape and spatial resolution, as well as the displayed range of topography heights, can be conveniently adjusted. Furthermore, molecular movies of conformational motions can be processed, which in principle allows simulated AFM experiments of functional dynamics in biomolecules.Here we report the developed computational framework which employs simulated AFM scanning to obtain 3D atomistic structural information from resolution-limited AFM surface scans by applying automatized rigid-body fitting of biomolecular structures to AFM images. We also present a toolbox of methods for detailed analysis and comparison of surface topographies in simulated and real AFM images, which allows molecular feature assignment and further validation of experimental observations. We demonstrate the broad applicability of the developed methods for AFM images ranging from single molecular machines, protein filaments, to large-scale 2D protein lattices. As we will show, the obtained full atomistic information clearly advances the molecular understanding beyond the original topographic AFM images.Our report shall highlight the applications of the developed computational methods to AFM images and demonstrate the achieved improvements in understanding experimental observations. For a detailed explanation of the simulated AFM scanning method and its implementation into the BioAFMviewer software, we refer to our previous report . The devThe core improvement of the BioAFMviewer is a toolbox which implements optimal fitting of high-resolution biomolecular stuctures to resolution-limited experimental AFM images. The purpose of this application is to extract information on the three-dimensional molecular structure from just the surface topography available from real AFM images.all possible corresponding simulated AFM graphics to the target experimental AFM image uploaded by the user. The fitting strategy should therefore aim to efficiently sample the space of possible rigid-body orientations of the loaded biomolecule, score the similarity of the corresponding simulated AFM to the user-defined target AFM image, and select candidates with show the best match.Generally, fitting of a molecular structure to an AFM image can be viewed as a complex optimization problem of identifying, from the pool of all possible conformations, a subset for which simulated and experimental AFM image match best. The interactive BioAFMviewer interface, shown in Global Search was based on extensive unbiased structure sampling, while the Local Search methods provides a more sophisticated search algorithm for refined optimization.We developed a procedure which employs two search strategies to identify molecular structures which best match the target AFM image. A method of Global Search method relies on unbiased sampling orientations of the loaded structure in the molecular viewer. The search space was defined by rigid-body rotations around all three spatial axes within the coordinate system of the molecular viewer in discrete steps  ligand was inhibited by the presence of the competitive antagonist cGMP, whereas the other scan monitored channel conformations in the activated state after the supply of cAMP at saturating concentration. In the experiments scanning the extracellular side, a thorough interpretation of AFM images, possibly providing a structural-level understanding of the mechanism underlying channel activation was particularly challenging, because of the tetramer structure only the rather small fraction of protruding regions could be monitored, whereas most domain parts including the important voltage-sensing domains were located within the lipid bilayer and therefore inaccessible. It should be noted that neither the use of a sharper AFM tip, nor a higher spatial resolution, would resolve those issues. For both AFM surface scans shown in  we perfoGlobal Search using a coarse searching grid, and refined Local Search was always employed  individually for all target single channel topographies within the experimental array. To determine the optimal match between a simulated AFM image of the SthK structure and the particular experimental target image, a combination of oyed see . In Fig oyed see . The corquasi-frozen state, which is confirmed by HS-AFM movies [The reconstruction of atomistic SthK structures, including the conformations of transmembrane domains which were not accessible to the scanning tip during experimental imaging, allows to understand important aspects underlying the mechanism of channel activation beyond what was possible from the original topographic AFM images. The lattice representing the resting state reveals a densely packed arrangement of symmetrically aligned channel conformations with a molecular orientation in which the voltage-sensing domains (VSDs) of neighboring channels can form tight surface contacts (marked in M movies . The sitM movies . An impoSince reconstruction of both SthK lattices relied on the atomic structure of the inactive channel conformation (active state data is unfortunately lacking), our model cannot directly resolve changes of peripheral VSDs related to channel activation. However, the arrangement obtained for the SthK active state lattice leads us to hypothesize that such motions may indeed be involved in the activation process as a result from cAMP ligands binding at the remote intracellular NBDs. The existence of this allosteric communication provides a possible structural rationale for the relationship between ligand binding, transmembrane voltage modulation, and channel activation observed in functional assays of SthK  and otheThis application demonstrates well the purpose of the BioAFMviewer automatized fitting function. Employing available structural templates allows to obtain 3D atomistic conformations from AFM surface scans. Thus, within the limitations of this approach see  section,Quick-Fit function. The heavy computational resources required to conduct Global Search would be a complete waste, given the obvious fact that the relevant search space is confined to the elongated quasi-planar orientations of the filament placed on the Mica surface under HS-AFM observations. Interestingly, the obtained result of optimized fitting for F-actin seems to allow the identification of filament polarity in the HS-AFM image from the reconstructed atomistic structure in which the barbed- and pointed-end is clearly distinguished . In the case of F1-ATPase the all nucleotide-free structure (PDB 1SKY) was used for fitting, validating the symmetric AFM topography. As we have previously shown, fitting of a partially nucleotide-complexed atomic F1-ATPase structure to an experimental HS-AFM image obtained under ligand saturation conditions allowed to identify the domain which was in the unliganded conformation .Line Tool allows the user to draw an arbitrary line in the window canvas of the simulated AFM graphics and that of the uploaded AFM image. The height topography of the scanned molecular surface along the chosen line is then computed and displayed. As a demonstration, this tool was applied to analyze surface topographies of filamentous actin (F-actin) obtained from simulated scanning . Simulated scanning was performed from the extracellular and the opposing intracellular side. The orientation of corresponding structures was conveniently chosen in the molecular viewer  view. This representation helps in the recognition of secondary structure motifs when biomolecules are compared and fitted to AFM images. However, the user should be aware that simulated scanning is obviously always performed for the atomistic VdW representation. For the graphical representation of the simulated AFM image we included an option to upload customized color palettes according to which topography heights are translated into displayed colors.Since the BioAFMviewer software is already used by many AFM groups worldwide, and valuable response helping to improve its functionality has been received, we use this opportunity to mention some of the important improvements we implemented in the BioAFMviewer 2 version. The loading of large PDB files, displaying the molecular structures, and, computing the corresponding simulated AFM images, has been highly accelerated to enable a more efficient handling of large protein structures. The integrated molecular viewer now also provides the option to view the loaded structure in the https://www.youtube.com/channel/UCKdeegdAX_ak6DlCs33x9Yg. The first manual explains the general usage, following the path from loading PDB files, to visualization of the molecular structure and the simulated AFM images, the adjustment of scanning parameters and the displayed range of surface heights including the color palette, and, eventually, the export of obtained results as image and movie files. The second manual proceeds with the application of automatized fitting molecular structures to uploaded experimental AFM images, showing a typical workflow in which a combination of Global Search and the Quick Fit function is executed. The third video manual demonstrates applications of the topography analysis tools. The illustration of the Membrane tool is available in a separate video manual.To illustrate the workflow within the BioAFMviewer platform as easy-to-follow steps we have recorded video manuals which are available on the BioAFMviewer YouTube channel We report simulation AFM as a computational framework to obtain 3D atomistic information from resolution-limited topographic AFM images by applying automatized rigid-body fitting of biomolecular structures, and provide the convenient implementation of developed methods into the BioAFMviewer software platform.Applications ranging from single molecular machines, protein filaments, to large-scale assemblies of protein lattices, demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original AFM topography image. We have shown that the molecular arrangement of functional domains in multimeric proteins and even their nucleotide-state can be disambiguated from AFM surface scans. In particular, applications to AFM images of 2D lattices for the cyclic nucleotide-gated SthK channel stand out to exemplify the explanatory power of atomic reconstruction. Structural characterization of the transmembrane domains, which are not accessible to the scanning tip during imaging, allows to identify possible interactions sites between neighboring channels within the membrane and elucidate the pattern distinguishing the resting state from the activated state.Of major importance to us was that the computational procedure of simulated AFM scanning together with the developed mathematical methods of fitting and topography analysis are embedded within a well organized user-friendly graphical interface. In that regard, the integrated molecular viewer, synchronized computation and visualization of AFM graphics, convenient adjustment of scanning and model parameters, and export of obtained results are central. It distinguishes AFM simulations from previous implementations.Global Search method is similar, the fitting approach presented here was aimed to go beyond inefficient exhaustive unbiased search. The interactive BioAFMviewer interface allowed us to implement the Quick Fit function for highly efficient fitting to AFM images, which can also consider the tip-shape dependence on fitting. As a further distinction, the BioAFMviewer interface allows applications beyond static PDB files. Simulation AFM can be conducted for entire molecular movies, e.g., obtained from modeling simulations [The rigid-body fitting to AFM images was recently presented in another work , where aulations . This opulations .Its user-friendly environment and rich functionality establishes the BioAFMviewer software package as a convenient platform for the broad Bio-AFM community to employ the enormous amount of existing structural and modelling data to facilitate the interpretation of resolution-limited AFM images.Application of simulation AFM can reach much beyond just the interpretation of what is observed in an AFM image. We have previously shown that new structural models with atomistic resolution can be retrieved for higher oligomeric arrangements of the Annexin V protein seen under HS-AFM . SimulatThe explanatory power of simulation AFM to predict correlated conformational dynamics in proteins from topopgraphic AFM imaging  will plaSimulated AFM scanning employed in the BioAFMviewer can only approximate the complex experimental scanning process, and its limitations have been discussed in detail in our previous report . An impoWhile more sophisticated formulations of simulated scanning are possible, e.g., resolving tip-sample interactions at a higher level of detail, this would correspond to a significant increase in computational resources and prevent the smooth implementation of synchronized AFM simulations. More importantly, applications to AFM images of biomolecular structures clearly evidence the superb performance of approximate modelling.The presented fitting method also has limitations. It treats the loaded PDB structure as a rigid object which cannot undergo internal conformational changes. The fitting quality to an experimental AFM image is therefore confined to simulation AFM of possible rigid-body molecular orientations. Our applications show indeed that fitting of PDB templates works very well for AFM images of proteins displayed in equilibrium conformations. As mentioned above, BioAFMviewer applications using molecular modelling data can overcome this drawback.In this report, we have not considered cases of fitting fragmented structural data into AFM images. This situation becomes relevant for the interpretation of HS-AFM images of highly flexible proteins with intrinsically disordered regions , 35, or A possible future objective is to provide a script-driven batch processing of the simulation AFM calculations, which can automatize the comparison of movie frames obtained from molecular simulations to those from HS-AFM videos.S1 Text(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as a variety of machine learning algorithms, including decision trees, multitask learning, and deep neural networks. We validate the proposed AGBT framework on eight molecular datasets, involving quantitative toxicity, physical chemistry, and physiology datasets. Extensive numerical experiments have shown that AGBT is a state-of-the-art framework for molecular property prediction. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Here, the authors propose an algebraic graph-assisted bidirectional transformer, which can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy and assisted with 3D stereochemical information from graphs. Experimental determination of molecular properties is very time-consuming and expensive. In addition, experimental testing involving animals or humans is subject to serious ethical concerns. Therefore, various computer-aided or in silico approaches have become highly attractive because they can produce quick results without seriously sacrificing accuracy in many cases2. One of the most popular approaches is the quantitative structure- activity relationship analysis. It assumes that similar molecules have similar bioactivities and physicochemical properties3.The fact that there is no specific and effective drug for coronavirus disease 2019 (COVID-19) 1 year after the outbreak reminds us that drug discovery remains a grand challenge. Rational drug discovery involves a long list of molecular properties, including binding affinity, toxicity, partition coefficient, solubility, pharmacokinetics, pharmacodynamics, etc6, convolutional neural networks (CNNs)7, and recurrent neural networks (RNNs)8, have become popular for drug discovery and molecular analysis10. Generative adversarial networks (GANs)11 combined with some machine learning strategies, such as supervised learning and reinforcement learning, have also been applied to the generation of novel molecules and drug design12. However, DL methods require large datasets to determine their large number of weights and might not be competitive for small datasets13.Recently, machine learning (ML), including deep learning (DL), has emerged as a powerful approach for data-driven discovery in molecular science. For example, graph convolutional networks (GCNs)14. Earlier molecular descriptors are designed as the profiles or fingerprints of interpretable physical properties in a bit string format15. Various fingerprints have been developed in the past few decades17. There are four main categories of two-dimensional (2D) fingerprints17, namely substructure key-based fingerprints18, topological or path-based fingerprints19, circular fingerprints16, and pharmacophore fingerprints20. However, 2D fingerprints lack three-dimensional (3D) structural information of molecules, especially stereochemical descriptions.Although DL methods, particularly CNN and GANs, can automatically extract features from simple data, such as images and/or texts, the performance of ML and DL methods for molecules, particularly macromolecules, crucially depends on the molecular descriptors or molecular representations due to their intricate structural complexity21. However, the molecular structural complexity and high dimensionality are the major obstacles in designing efficient 3D fingerprints14. Recently, a variety of 3D molecular representations based on advanced mathematics, including algebraic topology22, differential geometry23, and algebraic graph24 have been proposed to simplify the structural complexity and reduce the dimensionality of molecules and biomolecules25. These methods have had tremendous success in protein classification, and the predictions of solubility, solvation-free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, and Drug Design Data Resource (D3R) Grand Challenges26, a worldwide competition series in computer-aided drug design. However, this approach depends on the availability of reliable 3D molecular structures.To deal with the aforementioned problems, 3D-structure-based fingerprints have been developed to capture 3D patterns of molecules28. For example, bidirectional encoder representations from transformers (BERTs) are designed to pre-train deep bidirectional transformer representations from unlabeled texts27. The techniques developed in understanding sequential words and sentences in NLP have been used for understanding the fundamental constitutional principles of molecules expressed as a simplified molecular-input line-entry system (SMILES)29. Unlabeled SMILES strings can be considered as text-based chemical sentences and are used as inputs for SSL pre-training30. It is worth noting that the availability of large public chemical databases such as ZINC31 and ChEMBL32 makes SSL a viable option for molecular representation generation. However, latent-space representations ignore much stereochemical information, such as the dihedral angle33 and chirality34. In addition, latent-space representations lack specific physical and chemical knowledge about task-specific properties. For example, van der Waals interactions can play a greater role than the covalent interactions in many drug-related properties35, and need to be considered in the description of these properties.Alternatively, a self-supervised learning (SSL) strategy can be used to pre-train an encoder model that can produce latent space vectors as molecular representations without 3D molecular structures. The unlabeled data is used in the SSL strategy, but unlike unsupervised learning, the data input to the model is partially masked, and then the model is trained to predict the masked part in the training process, where the originally masked data can be used as labels. This strategy allows a large amount of unlabeled data to be utilized. The initial development of SSL was due to the need for natural language processing (NLP)24. Deep bidirectional transformer (DBT) utilizes an SSL-based pre-training process to learn fundamental constitutional principles from massive unlabeled SMILES data and a fine-tuning procedure to further train the model with task-specific data. The resulting molecular fingerprints, called bidirectional transformer-based fingerprints (BT-FPs), are latent-space vectors of the DBT. The proposed AGBT model is applied to eight benchmark molecular datasets involving quantitative toxicity and partition coefficient37. Extensive validation and comparison suggest that the proposed AGBT model gives rise to some of the best predictions of molecular properties.In this work, we introduce algebraic graph-assisted bidirectional transformer (AGBT) to construct molecular representations via combining the advantages of 3D element-specific weighted colored algebraic graphs and deep bidirectional transformers. The element-specific weighted colored algebraic graphs generate intrinsically low-dimensional molecular representations, called algebraic graph-based fingerprints (AG-FPs), that significantly reduce the molecular structural complexity while retaining essentially physical/chemical information and physical insight32 dataset was used in the pre-training. More descriptions of the datasets can be found in Supplementary Note In this section, we present the proposed AGBT model and its results for molecular prediction on eight datasets, i.e., LD50, IGC50, LC50, LC50DM, partition coefficient, FreeSolv, Lipophilicity, and BBBP datasets. Supplementary Table s-FPs . The random forest algorithm is used to rank the importance of fused AG-FP and BT-PF features and select an optimal set of AGBT-FPs of a fixed number of components. The downstream machine learning algorithms are fed with optimal features to achieve the best performance on four benchmark toxicity datasets.As shown in Fig. 38. To eliminate systematic errors in the machine learning models, for each machine learning algorithm, the consensus of the predicted values from 20 different models (generated with different random seeds) was taken for each molecule. Note that the consensus value here refers to the average of the predicted results from different models for each molecule of each specific training-test splitting. In this work, the squared Pearson correlation coefficient (R2), root-mean-square error (RMSE), and mean absolute error (MAE) are used to assess the performance of the regression task, while the classification accuracy and the area under the receiver operating characteristic convex hull (AUC-ROC) are used to evaluate the performance of classification model. All definitions are given in Supplementary Note We carry out our final predictions by using some standard machine learning algorithms, namely, gradient boosted decision tree (GBDT), random forest (RF), and deep neural networks (DNNs), including single-task DNN , which are better than those methods.The oral rat LD50 set measures the number of chemicals that can kill half of the rats when orally ingested41. Wu et al.2 used physical information including energy, surface energy, electric charge, and so on to construct molecular descriptors. These physical properties are related to molecular toxicity, achieving the prediction accuracy of R2 0.771. In this work, our AGBT-FPs with MT-DNN deliver the best R2 of 0.776. We also test the performance of our BT-FPs, which achieve R2 0.783 with MT-DNN. As listed in Table LC50 dataset reports the concentration of test chemicals in water by milligrams per liter that cause 50% of fathead minnows\ to die after 96 hP, derived from the ratio of the concentration of a mixture of two mutually insoluble solvents (octanol and water in these data) at equilibrium, measures the drug relevance of the compound as well as its hydrophobicity to the human bodies. The logarithm of this coefficient is denoted as logP43. The training set used for logP prediction includes 8199 molecules44. A set of 406 molecules approved by the Food and Drug Administration (FDA) is used as organic drugs were used as the test set44 and its logP values range from −3.1 to 7.57. The comparison of different prediction methods for FDA molecular data set is listed in Table 45 can also be used in logP prediction, however, there is no guarantee that the training set of ALOGPS are independent of the test set and thus its result is not included in the comparison. As we can see from Table s-FPs with STDNN model produce the best R2 of 0.905. The predicted result of AGBTs-FPs with STDNN model for FDA data set are shown in Supplementary Fig. Partition coefficient denoted 9, which is a benchmark for molecular property prediction. There are 643 samples and 4200 samples for FreeSolv and Lipophilicity datasets, respectively. For comparison, the datasets were split into train, validation, test sets with the ratio of 8:1:1, which follows the same procedure as MoleculeNet9. We set different random seeds and follow the same procedure ten times to obtain ten different data splitting. To eliminate systematic errors in downstream machine learning models and to better compare molecular descriptors, for each machine learning algorithm, the consensus of the predicted values from 20 models of different random seeds for each data-splitting was taken for each molecule. And the final score for the dataset is the average score over ten different data-splittings. As shown in Table 46 (RMSE = 1.075), MMNB47 (RMSE = 1.155), and MoleculeNet9 (RMSEGraphConv = 1.15). For the Lipophilicity dataset, using the RF algorithm, the RF method, our descriptors obtains the best RMSE of 0.573 ± 0.026, a result that is close to the best result reported by Chemprop46 (RMSEGraphCov = 0.555). A complete result with multiple evaluation metrics is available in Supplementary Table Solvation-free energy and lipophilicity are basic physical and chemical properties for understanding how molecules interact with solvents. In this work, FreeSolv and Lipophilicity datasets are derived from MoleculeNet48. The binary labels for compound permeability properties are used in this study. For a better comparison, we follow the same scaffold splitting method described in MoleculeNet9. The dataset was split into training, validation, and the test set follow the ratio of 8/1/1. Table 46 (AUC-ROC = 0.738), MMNB47 (AUC-ROC = 0.739), and MoleculeNet9 (AUC-ROCECFP = 0.671).The BBBP dataset contains 2042 small molecules and original from a study on the modeling and prediction of barrier permeabilityIt is worth noting that to validate the performance of our AGBT framework, for downstream machine learning models, we did not over-tune the parameters to optimize our results in this study. For all the above-mentioned datasets, the same set of parameters is taken for each type of descriptors in our model. The details of the parameter settings can be found in Supplementary Note In this section, we discuss how the AGBT model brings insights to molecular property predictions, as well as the enhancement that algebraic graph-based fingerprints and deep bidirectional transformers-based fingerprints give rise to our proposed AGBT method.Pre-trained on a large number of molecules, deep SSL-based molecular fingerprints could achieve high accuracy. Many deep learning-based molecular fingerprints have shown a better performance than conventional fingerprints. However, deep learning fingerprints, including our BT-FPs, are prone to the loss of molecular stereochemical information. This lack of information often makes "activity cliff” a nuisance. An example in Supplementary Fig. R2 = 0.842 on IGC50 and R2 = 0.830 on LC50DM. The orange bar at each point is the deviation of predicted toxicity with 20 models (with different random seed). For each model, R2 was calculated and the distribution of R2 is shown in subfigures. The performance on the LD50 and LC50 datasets is shown in Supplementary Fig. R2 of the toxicity predictions from the three machine learning algorithms, i.e., GBDT, ST-DNN, and MT-DNN, are shown in the bar plot of Fig. Figure R2 0.776, exceeding the other reported methods. As shown in Table R2, RMSE, and MAE of FreeSolv and Lipophilicity’s prediction also show that AGBTs-FP can obtain the most stable performance in most cases (5/6). Therefore, the fusion of AG-FPs and BT-FPs improves the accuracy and stability of predictions for most datasets. Mathematically based molecular descriptors can complement data-driven potential spatial descriptors.For the IGC50 dataset, 1434 molecular structures were used to train the AGBT model, leading to fluctuation in prediction Fig. R2 = 0.830, RMSE = 0.743).In this work, we develop two strategies in the fine-tuning stage: SSL and SL with task-specific data. It is found that SSL strategy , 0.815 (ST-DNN), and 0.830 (MT-DNN), respectively. This indicates that AGBTs-FPs can capture general toxin-related information during the sequential fine-tuning process. There is no significant difference among the three predictions based on GBDT, ST-DNN, and MT-DNN. In contrast, AGBT-FPs are derived from the model after self-supervised training. Their pre-training and fine-tuning processes do not involve any labeled data. The resulting prediction accuracies with GBDT and ST-DNN are quite low with R2 being 0.587 and 0.659, respectively. Through the MT-DNN model, the performance of AGBT-FPs can be improved from R2 0.587 to 0.781.Figure The above discussion indicates that SSL can acquire general molecular information and universal molecular descriptors without the guidance of labels. In downstream tasks, the MT-DNN model can also help to extract the task-specific information from related data. As for small datasets, such as the LC50DM dataset (300 samples), the subsequent fine-tuning with an SL strategy is much more promising.s-FP, and AGBT-FP/AGBTs-FP are shown in Table s-FP) achieved the best performance in 5/8 of the tasks. For the LC50 dataset, the AGBT-FP prediction of 0.776 is very close to the best performance of 0.783 obtained by BT-FPs. For Lipophilicity dataset, the performance of AGBTs-FP is RMSE = 0.579. It is close to the best RMSE (0.57). And for the BBBP dataset, the classification performance of AGBTs-FP is AUC-ROC = 0.761, which is almost the same as the best 0.763. The complete results for all 8 datasets with multiple evaluation metrics are shown in Supplementary Table The results of all eight datasets using AG-FP, BT-FP/BTIn chemistry, the properties of molecules, such as toxicity, are often determined by some specific functional groups or fragments. Similar to biological genes, molecules have some determinants of their properties, which are called structural genes in this work. For some path-based fingerprints, such as FP2, a molecule is represented by 256 length vectors, each corresponding to a specific fragment. However, it is difficult to achieve the best results from such a fingerprint, as shown in Fig. Using a random forest algorithm, we performed a feature importance analysis of AGBT-FPs. As shown in Supplementary Fig. s-FPs into an orthogonal subspace by principal component analysis. As shown in Fig. s-FPs are given in Fig. s-FPs we only need 48 components. The top two principal components of AGBT-FPs are just explaining 9% and 8% of the variance, which indicates that, since there is no labeled data to train the model, the generated AGBT-FPs represent general information about the molecular constitution rather than specific molecular properties. The first two components for AGBTs-FPs can explain 40% and 13% of the variance respectively, which indicates that by using SL-based fine-tuning training, the model can effectively capture task-specific information.We projected both AGBT-FPs and AGBTs-FP model performs better in predicting specific properties because the labeled data are used to train the model during fine-tuning. It should be noted that some molecular information irrelevant to that particular property might be lost in this way. This strategy leads to better results for some datasets with minimal data, such as LC50DM, whose small amount of data is not enough to effectively obtain property-specific information in downstream tasks. However, if more downstream data are available, such as LD50, IGC50, and LC50, downstream machine learning methods can also derive property-specific information from general molecular information. For example, AGBT-FPs perform better on LD50, IGC50, and LC50 datasets.The AGBTR2 values of 0.671, 0.842, 0.783, 0.830, and 0.905 on LD50, IGC50, LC50, LC50DM, and FDA logP dataset, respectively. In the datasets FreeSolv and Lipophilicity, we obtained RMSE scores of 0.994 and 0.579, respectively, and in the classification dataset BBBP, we obtained an AUC-ROC score of 0.763. Our model can be easily extended to the prediction of other molecular properties. Our results show that the proposed AGBT is a robust, and powerful framework for studying various properties of small molecules in drug discovery and environmental sciences.Despite many efforts in the past decade, accurate and reliable prediction of numerous molecular properties remains a challenge. Recently, deep bidirectional transformers have become a popular approach in molecular science for their ability to extract fundamental constitutional information of molecules from massive SSL. However, they neglect crucial stereochemical information. The algebraic graph is effective in simplifying molecular structural complexity but relies on the availability of 3-D structures. We propose an AGBT framework for molecular property prediction. Specifically, element-specific multiscale weighted colored algebraic subgraphs are introduced to characterize crucial physical/chemical interactions. Moreover, for small datasets, we introduce a supervised fine-tuning procedure to the standard pre-trained SSL to focus on task-specific information. These approaches are paired with random forest, gradient boosted decision trees, multitask deep learning, and deep neural network algorithms in AGBT. We demonstrate that the proposed AGBT framework achieves 24. Figure 24.Graph theory can encode the molecular structures from a high-dimensional space into a low-dimensional representation. The connections between atoms in a molecule can be represented by graph theory, as shown in Fig. ith atom in a N-atom subset is labeled both by its element type αi and its position ri. We denote all the pairwise interactions between element types ith and jth atoms in a molecule, ri and rj are the atomic radii of ith and jth atoms, respectively, and σ is the mean standard deviation of ri and rj in the dataset. Figure 49. Here, ith atom50. The k1 and k2, the characteristic distance Ψ, we finally construct a family of element-specific, scalable , multiscale geometric graph-based molecular descriptors24.As shown in Fig. 24To generate associated algebraic graph fingerprints, we construct corresponding graph Laplacians and/or adjacency matrices. For a given subgraph, its matrix representation can provide a straightforward description of the interaction between subgraph elements. To construct a Laplacian matrix, we consider a subgraph Similarly, an element-specific weighted adjacency matrix can be defined by28. Based on the DBT architecture, Devlin et al.27 introduced a representation model called BERT for natural language processing. There are two tasks involving BERT, masked language learning, and consecutive sentences classification. Masked language learning uses a partially masked sentence  as input and employs other words to predict the masked words. The consecutive sentences classification is to classify if two sentences are consecutive. In the present work, the inputs of the deep bidirectional transformer are molecular SMILES strings. Unlike the sentences in traditional BERT for natural language processing, the SMILES strings of different molecules are not logically connected. However, we train the bidirectional encoder from the transformer to recover the masked atoms or functional groups.Unlike RNN-based models, DBT is based on the attention mechanism and it is more parallelable to reduce the training time with massive data51. Then, a SMILES string is split into symbols, e.g., C, H, N, O, =, Br, etc., which generally represent the atoms, chemical bonds, and connectivity, see Supplementary Table s> and <\s>. Here, <s> means the beginning of a SMILES string and <\s> is a special terminating symbol. All symbols are embedded into input data of a fixed length. A position embedding is added to every symbol to indicate the order of the symbol. The embedded SMILES strings are fed into the BERT framework for further operation. Supplementary Fig. 32 are used for the pre-training so that the model learns basic "syntactic information” about SMILES strings and captures global information of molecules.Because a molecule could have multiple SMILES representations, we first convert all the input data into canonical SMILES strings, which provide a unique representation of each molecular structures-FPs are created in the fine-tuning training step, which further learns the characteristics of task-specific data. Two types of fine-tuning procedures are used in our task-specific fine-tuning. The first type is still based on the SSL strategy, where the task-specific SMILES strings are used as the training inputs, as shown in Supplementary Fig. Both BT-FPs and BTs-FPs), which significantly strengthens the predictive power of the model on the smallest dataset.The second fine-tuning procedure is based on an SL strategy with labeled task-specific data. As shown in Supplementary Fig. s> of a molecular SMILES string as the bidirectional transformer fingerprints (BT-FPs or BTs-FPs) of the molecule. In the downstream tasks, BT-FPs or BTs-FPs are used for molecular property prediction. Detailed model parameters can be found in Supplementary Note In our DBT, an input SMILES string has a maximal allowed length of 256 symbols. During the training, each of the 256 symbols is embedded into a 512-dimensional vector that contains the information of the whole SMILES string. In this extended 256 × 512 representation, one can, in principle, select one or multiple 512-dimensional vectors to represent the original molecule. In our work, we choose the corresponding vector of the leading symbol <Further information on research design is available in the Supplementary InformationSupplementary Data 1Description of Additional Supplementary FilesReporting SummaryPeer Review File


The study of dopamine and closely related chemical probes reveals how neurotransmitter binding translates into the activation of distinct subsets of D2R effectors . Ligand interactions with key residues in TM5 (S5.42) and TM6 (H6.55) in the D2R binding pocket yield a dopamine-like coupling signature, whereas exclusive TM5 interaction is typically linked to preferential G protein coupling (in particular GoB) over β-arrestin. Further experiments for serotonin receptors indicate that the reported molecular mechanism is shared by other monoaminergic neurotransmitter receptors. Ultimately, our study highlights how sequence variation in position 6.55 is used by nature to fine-tune β-arrestin recruitment and in turn receptor signaling and internalization of neurotransmitter receptors.Brain functions rely on neurotransmitters that mediate communication between billions of neurons. Disruption of this communication can result in a plethora of psychiatric and neurological disorders. In this work, we combine molecular dynamics simulations, live-cell biosensor and electrophysiological assays to investigate the action of the neurotransmitter dopamine at the dopaminergic D Neurotransmitter contacts within the receptor binding site differentially contribute to the overall functional response: transmembrane helix (TM) 5 contacts promote G protein coupling whereas concerted TM5–TM6 contacts enhance β-arrestin recruitment. In response to dopamine, the D2R is known to signal through both G protein  and β-arrestin (βarr2) signaling pathways. This coupling profile modulates important processes in the brain related to memory, learning, attention, mood and movement. The observation that some ligands can preferentially engage one pathway over others has led to the concept of signaling bias or functional selectivity.4,5 This groundwork has initiated the quest for more efficient and safer CNS-targeting drugs able to preferentially engage therapeutically relevant pathways over those responsible for deleterious side-effects.6–8 Despite first insights,9,10 the rational design of drugs with a desired signaling profile remains a significant challenge as it is difficult to pinpoint ligand–receptor interactions responsible for a specific coupling profile. Even subtle changes in ligand–receptor interactions can result in a dramatic change of the signaling profile.11 We envisage that disclosing the molecular link between dopamine binding and the induced D2R coupling profile to different intracellular partners can contribute to a wider understanding of neurotransmission. Beyond, it can also guide the development of ligands with a tailored coupling profile for this receptor and other aminergic GPCRs.Neurotransmitters are chemical messengers that mediate communication between billions of neurons within an enormous network that constitutes the central nervous system (CNS). Disruptions in the regulation of this system are known to result in numerous disorders, including depression, psychosis, bipolar disorder, general anxiety disorder and Parkinson's disease.12–15 Unfortunately, the results are not always consistent between studies, likely due to inter-study differences in experimental setups. In addition, the atomistic resolution of how dopamine binding translates into the recruitment of distinct intracellular signaling proteins  remains unclear despite the recently solved D2R in complex with the Gi protein.16 To address this knowledge gap, we carried out all-atom molecular dynamics simulation  accumulating ∼40 μs of simulation time. The power of enhanced sampling techniques for capturing biologically relevant events has been shown in previous studies.17–19 Here, we use this approach to construct the complete energetic binding landscape of dopamine and closely related signaling probes. The small size and low number of rotatable bonds of studied compounds allow for an exhaustive sampling of their binding. The signaling signature of each ligand was characterized by assessing receptor coupling to a selection of intracellular signaling proteins with relevance for neurotransmission  using live-cell BRET-based biosensors. Ultimately, this approach allowed us to detect a mechanism shared by several aminergic GPCRs with relevance for neurotransmission.Throughout the last decades, several research groups have studied the binding and functional outcome for dopamine and its analogues.i proteins  and βarr2 by D2R using live-cell BRET-based biosensors. The BRET-based assay confirms robust coupling of the D2R to all tested intracellular effector proteins upon dopamine binding  and the para (p-OH) hydroxyl groups of dopamine to TM5 and TM6   .p-/m-OH group and the polar residues (S5.42 or S5.46) in TM5. For the p-OH group, we find one binding peak for S5.42 (m-OH group which becomes evident when plotting the binding preference of the m-OH group relative to S5.42 (via p-OH) and TM6 (via m-OH). It is worth noting that TM6 interaction can be direct (peak 2a) or indirect via a water molecule (peak 2b), as also suggested by unbiased simulation  of the concentration–response curves which captures changes in both potency (EC50) and efficacy (Emax). Calculating AUC-based coupling ratios  for p-tyramine corroborates a strong coupling preference of GoB over βarr2 compared to the reference compound dopamine (p-tyramine). Note that a similar preferential coupling tendency is found for Gi2 and Gz over βarr2. This finding suggests that exclusive TM5 interaction favors G protein recruitment, whereas additional interactions with TM6 via the m-OH promote the engagement of βarr2.To dissect the contribution of 2 in TM5  similar 2 in TM5 . Due to p-tyramine vs. dopamine, m-tyramine, a molecule that only carries a m-OH group, with the expectation that this compound should recruit βarr2 signaling to a similar extent as dopamine. Surprisingly, βarr2 was only partially engaged as seen in the concentration–response curves tetralins (7-OH-DPAT).22 Similar to m-tyramine, the OH-group of 7-OH-DPATs is separated by 5 carbons from the amine group (R and S enantiomers should adopt only one m-tyramine-like binding mode at a time. The obtained energetic landscape of ligand binding (R)-7-OH-DPAT interacts with S5.42 via its OH group and simultaneously with H6.55 in TM6 (S enantiomer binds in an inverted position directing its OH group towards the bottom of the binding pocket interacting either with S5.42 or S5.46 (S)-5OH-DPAT establishes simultaneous TM5/6 contacts (R)-5OH-DPAT enantiomer adopts an inverted position with exclusive TM5 interactions ractions . AltogetoB, Gi2, Gz and βarr2. In contrast, DPATs with exclusive TM5 contacts (orange curves) lose potency and efficacy (in particular for βarr2) when compared to dopamine (blue curves) while maintaining highest potency/efficacy at GoB. This trend is also reflected by the AUC-based coupling ratios, which show a dopamine-like profile for DPATs with TM5/6 interaction -7-OH-DPAT and (R)-5-OH-DPAT) reach a 12- and 274-fold bias of GoB over βarr2, respectively -7-OH- and (S)-5-OH-DPAT). We selected a phenylalanine substitution as this is the residue most similar to histidine but lacking hydrogen donor/acceptor heteroatoms and thus effectively preventing polar interactions with position 6.55. Compared to the WT D2R, the H6.55F mutant displays a dramatic reduction in βarr2 recruitment ΔΔlog of ∼30 fold for dopamine and even ∼1000 fold for (R)-7-OH-DPAT or ≫1000 fold for (S)-5-OH-DPAT -5-OH-DPAT ≥ dopamine > (R)-5-OH-DPAT > p-tyramine. We further tested the ability of βarr2 to desensitize the D2R-evoked G protein response, as measured at the level of GIRK channel activation, upon prolonged agonist exposure. Oocytes are known to express no detectable endogenous β-arrestins.23 In agreement, we observed that the GIRK channel responses to all four of the tested agonists decayed towards baseline at a faster rate in oocytes co-expressing exogenous βarr2, compared to dopamine-evoked responses in control oocytes which had not been injected with βarr2 cRNA (R)-5-OH-DPAT and p-tyramine were slower than those with dopamine and (S)-5-OH-DPAT.To investigate how different ligand coupling profiles translate into proximate downstream signaling events, we used a G protein-coupled inward rectifier potassium (GIRK) channel activation assay in on assay  overall on assay  and 2A–Drr2 cRNA . In the 2R. Within this contact network, specific interactions with TM6 seem to promote βarr2 recruitment. To investigate if this represents a common mechanism for other neurotransmitter receptors, we studied the action of serotonin on the serotonergic receptors 1A (5-HT1AR) and 2A (5-HT2AR). Similar to dopamine  ΔΔlog of ∼10 fold based on the operational model  (1AR (A6.55N) is further illustrated via calculation of AUC-based coupling ratios ΔΔlog based on the operational model  . However14, G15) . These c14, G15) . The higTable S4E. This fi, βarr2) . Howeverg ratios . The higTable S4F. Overall2R. Using compounds such as p-tyramine, we find that exclusive TM5 interaction via the p-OH group  while antagonizing β-arrestin engagement downstream of D2R. These data are in line with our findings. As the authors measured the functional outcome as cAMP inhibition, no information is available about the specific Gi-family member engaged. Using live-cell BRET-based biosensors, we tested the ability of hordenine to recruit different Gi-family G protein subtypes as well as βarr2. We find a preferential GoB coupling profile for hordenine, which seems to be related to exclusive TM5 interaction similar to p-tyramine /(S)-5-OH and (R)/(S)-7-OH-DPATs). Interestingly, depending on their chirality, enantiomeric DPATs are able to favor one binding mode with either simultaneous TM5/TM6 or exclusive TM5 interactions, resulting in a distinct coupling outcome. It is worth noting that the described mechanistic link between the binding mode and the coupling outcome for chiral DPATs may also explain recent findings for the signaling preference of extended ligands that include a chiral DPAT scaffold.29We further conclude that DOH group . We demoi.e. Y7.43) for dopamine, p- and m-tyramine but not for DPAT derivatives. As DPATs extend hydrophobic di-propyl groups towards TM7, they are not able to form such polar contacts. It is worth noting that our data suggests that polar contacts in TM7 are not important for coupling specificity as they are found in unbiased (e.g. dopamine) as well as G protein biased compounds (e.g. p-tyramine).A general overview of ligand-receptor contacts for dopamine-like and G protein biased compounds is provided by a contact heatmap Fig. S5. The hea2R conformational state with low affinity towards βarr2 and are therefore less susceptible to receptor desensitization which is reflected in prolonged GIRK openness. Ultimately, this finding provides additional evidence that (R)-5-OH-DPAT and p-tyramine exhibit a preference for G protein coupling over βarr2 compared to dopamine and (S)-5-OH-DPAT.Electrophysiological experiments show that specific ligand coupling profiles propagate further downstream to the level of GIRK channel activation. An important observation is that compounds with G protein-coupling preference result in prolonged GIRK channel responses . Mechaniet al. suggest that βarr2 bias in the D2R can be induced by exclusive contacts to Ile184 in the extracellular loop 2 (ECL2).30 Computing the frequency of Ile184 contacts for the ligands studied herein  indicates that these compounds are too small to establish significant contacts with this residue in the ECL2  versus D1,5R (N6.55). Interestingly, our mutational experiment carried out at the D2R and the obtained biased factor show that a H6.55N variation yields an unbiased coupling profile similar to the D2R WT  and D1,5R (N6.55) share a similar unbiased coupling profile (i.e. G proteins vs. βarr) between each other.A sequence analysis of dopamine receptor subtypes (DTable S4B. This su2AR is linked to simultaneous TM5/TM6 interaction (2 adrenergic receptor (β2AR). In a recent study, authors focused on salmeterol, a β2AR-targeted drug used for the treatment of asthma and chronic obstructive pulmonary disease.32 They report that limited βarr recruitment of salmeterol is linked to reduced contacts with N6.55 in the β2AR which is in line with the proposed mechanism.Most importantly, our data propose further that also serotonin-mediated neurotransmission at the 5-HTeraction . In this33). According to our model, non-polar residues would partially dampen βarr2 coupling to the WT receptor. We indirectly prove this by introducing a polar residue into position 6.55 of the 5-HT1AR -5-OH-DPAT or (S)-7-OH-DPAT) appear to provoke a greater reduction in D2R coupling to Gi2 and Gzvs. GoB  leads to a switch of its dual coupling profile (Gs and Gi) to a preferential Gs coupling.34 Note that this tendency does not appear to be strongly conserved in the 5-HT1AR since although a A6.55N mutation enhanced βarr2 recruitment, we find only a marginal impact on G protein coupling across GoB, Gz and Gi2 proteins . Most importantly, our study indicates that the identified molecular features govern the coupling outcome of serotonin as well as adrenergic receptors and are likely shared by other aminergic neurotransmitter receptors. Furthermore, our work highlights how nature deploys sequence variations to fine-tune the coupling outcome of neurotransmitter receptors across different receptor subtypes. Ultimately, obtained structural insights provide novel hints for the rational design of more efficient and safer drugs for this important drug target class.In this study, we use all-atom molecular simulation combined with live-cell biosensor and electrophysiological assays to dissect the molecular mechanism of dopaminergic neurotransmission at the prototypical neurotransmitter receptor D2 receptor was obtained from the Uniprot database (accession number: P14416). The sequence was aligned with that of the β2AR obtained from the template structure (PDB code: 3P0G) using Clustal Omega.35 The alignment was manually refined to maintain the position of highly conserved residues. The first 36 residues of the D2R sequence were truncated as they formed a flexible N-terminal tail. The long intracellular loop 3 was shortened and the ends were fused. Based on the obtained alignment, we generated 500 models using homology model tool implemented in the MOE package (http://www.chemcomp.com). The best model was selected based on the lowest DOPE (Discrete Optimized Protein Energy) score. The hydrogen network was optimized at pH 7 using Protonate3D36 available in the MOE package (http://www.chemcomp.com). It is worth noting that the active D2R in complex with Gi (PDB code: 6VMS) has been recently obtained while this manuscript was in preparation.16 This structure provides high-resolution insights into the orthostatic binding pocket of the active D2R. Importantly, the backbone atoms for residues in the binding pocket that complex dopamine adopt a RMSD value of only 0.895 Å which supports the high quality of our computational receptor model . Then, initial poses for m-tyramine and p-tyramine were obtained by removing the meta or para hydroxyl group of the dopamine pose obtained after the molecular dynamics optimization.The starting poses of dopamine were obtained by docking with GOLD software.1AR and 5-HT2AR in complex with serotonin were obtained based on the work from Martí-Solano et al.11 The structures were subjected to a short minimization using the MOE package (http://www.chemcomp.com).Structural models for the 5-HT2R. To ensure proper orientation of the receptor in the membrane, the complexes were aligned to the structure used as the template (PDB code: 3P0G) obtained from the OPM database.38 Subsequently, we used the output aligned structures to generate systems for molecular dynamics. The systems were generated using CHARMM-GUI.39 The receptor was embedded in a ∼80 × 80 Å POPC bilayer. The resulting complex was solvated with ∼8200 TIP3 molecules. The ionic strength of the solution was kept at 0.15 M NaCl. Additional chloride ions were added in order to keep the charge of the system neutral. Disulfide bonds were introduced in accordance with data obtained from the Uniprot database. Parameters for the simulation were obtained from the CHARMM36 forcefield.40 Parameters for the ligand were assigned from the CGenFF forcefield automatically by the ParamChem tool implemented in CHARMM-GUI.41,42 The systems were first equilibrated in conditions of constant pressure  for 100 ns. Over the first half of the simulation we applied constraints to the backbone atoms. The constraints were gradually released over the first 50 ns of the simulation. After the NPT step, we have carried out simulations in conditions of constant volume (NVT) of the system for 600 ns in 4 replicates. The simulations were run in ACEMD.43 We used a time-step of 4 fs. Such a large time-step was possible due to the hydrogen mass repartitioning scheme being employed in ACEMD.44 A non-bonded interaction cutoff was set at 9 Å. A smooth switching function for the cut-off was applied, starting at 7.5 Å. Long-distance electrostatic forces were calculated using the Particle Mesh Ewald algorithm. The algorithm had grid spacing of 1 Å. The bond lengths of hydrogen atoms were kept constrained using the RATTLE algorithm. Simulations were carried out at a temperature of 300 K in periodic boundary conditions. A summary of molecular dynamics simulations carried out in this study is found in Table S1.†To generate starting systems, ligands (in accordance with their poses obtained in the previous step) were placed in the active state model of the D45 It is implemented in the molecular dynamics software ACEMD using the PLUMED plugin interface.46 Here, we use this approach to construct the complete energetic binding landscape of dopamine and its derivatives. For this, we used as collective variables the distance of oxygen atoms of m- and/or p-OH groups of the ligand to the OG atom of S5.42 (CV1) and/or S5.46 (CV2) in TM5 . Each system was simulated for an accumulated time of at least 1.1 μs or until the free-energy landscape converged. General simulation parameters were kept as described for the production run in the previous section. Ultimately, we plotted the energies as a function of the distance between the ligand's oxygen group and S5.42 or S5.46 , GAPL-, GAPL-GoB (cat# DTNA A32), GAPL-Gz (cat# DTNA A33) and βarr2-PM + GRK2 WT (cat# DTNA A46). Information pertaining to the βarr2-PM biosensor has been previously published.48 Experiments with the D2R were performed with the long isoform . All receptor point mutations were produced by TOP Gene Technologies Inc. . Transfections were performed using 25 kDa linear PEI  at a 3 : 1 μl of PEI/μg of DNA ratio. Briefly, DNA and PEI were diluted separately in 150 mM NaCl, mixed and then incubated for at least 20 minutes at room temperature ). During the 20 minute incubation, HEK-293T cells were detached, counted and re-suspended into cell culture medium to a final density of 350 000 cells per mL. At the end of the 20 minute incubation, DNA/PEI complexes were added to cells followed by a gentle mixing. Cells were subsequently distributed in cell culture-treated 96-well plates  at a density of 35 000 cells per well  and incubated at 37 °C for 48 h. At 48 hours post-transfection, the transfection medium was removed and cells were washed once with 100 μl of Tyrode–Hepes buffer  per well. Wash buffer was then replaced by 100 μl of fresh Tyrode–Hepes buffer per well and plates were incubated for 60 min at room temperature. At the end of this equilibration period, 10 μl of 20 μM e-Coelenterazine Prolume Purple  was added to each well followed immediately by the addition of increasing test compound concentrations. Cells were then incubated at room temperature for 10 minutes and BRET readings subsequently collected with a 0.4 s integration time on a Synergy NEO plate reader . The BRET signal was calculated as the ratio of GFP10 emission to RLucII emission. All resulting dose response curves are represented as baseline-corrected uBRET .Bioluminescence resonance energy transfer (BRET)-based biosensor assays (bioSensAll™) were conducted at Domain Therapeutics NA Inc. . Assays were performed in HEK-293T cells, which were cultured in Dulbecco's Modified Eagle Medium (DMEM) (Wisent # 319-015-CL) supplemented with 1% penicillin-streptomycin G  and 10% fetal bovine serum (Wisent # 090150) and maintained at 37 °C with 5% CO50), efficacy (Emax) and the Hill slope. AUCs were obtained using GraphPad Prism 6 software. In order to facilitate the comparison of individual pathways for tested compounds, we computed the AUC-based coupling ratios for all combinations of individual effector proteins . For D2R, coupling ratios for dopamine are used as the reference to which all other dopaminergic ligand coupling ratios are normalized. For 5-HT mutant receptors, the coupling ratios for the WT receptor in response to serotonin are used as reference  of the concentration–response curves. This AUC takes into account potency (ECeference . Furthereference .R)-7-OH-DPAT: Cedarlane (Axon Medchem # 1013), (S)-7-OH-DPAT: Cedarlane (Axon Medchem # 1014), (R)-5-OH-DPAT: Cedarlane (Axon Medchem # 1007), (S)-5-OH-DPAT: Cedarlane (Axon Medchem # 1008), hordenine: Sigma # 04476, m-tyramine hydrochloride: Sigma # D017, Lot: 063K4620, p-tyramine hydrochloride: Sigma # T90344, noradrenaline hydrochloride: Sigma # 74480, serotonin hydrochloride: Sigma # H9523, dopamine hydrochloride: Sigma # H8502.. Ligand bias was quantified by analyzing the concentration–response curves using the operational model of agonism according to the equationwhere: E = effect of the ligand; [A] = concentration of agonist; Em = maximal possible response of the system; Basal = basal level of response in the absence of agonist; log KA = logarithm of the functional equilibrium dissociation constant of the agonist; n = slope of the transducer function that links occupancy to response; log R = logarithm of the transduction ratio, τ/KA, where τ is an index of the coupling efficiency (or efficacy) of the agonist.The operational model of agonism was used to estimate ligand bias when applicable according to recently published protocols.Em, and n were shared between all agonists; for full agonists, log KA was constrained to a value of zero; for partial agonists, log KA was directly estimated by the curve fitting procedure. The log R  parameter was estimated as a unique measure of activity for each agonist.The following parameters were used for fitting of all families of agonist curves at each pathway to the model: Basal, τ/KA) was then obtained from fitted concentration–response curves for the recruitment of GoB and βarr2 (determined BRET-based assay). To account for cell-system-dependent factors between different assay systems, the transduction coefficients (τ/KA) were normalized to the response of the reference agonist dopamine:Coupling bias was obtained by calculating the difference between two investigated pathways for the same ligand:The logarithmic form of the transduction ratios (https://www.cdna.org) were in pcDNA3.1+. cDNA encoding the human dopamine D2S receptor and βarr2  were in pXOOM . For in vitro transcription, plasmids were linearized with the appropriate restriction enzymes  and transcribed in vitro using the T7 mMessage mMachine kit . cRNA concentration and purity were determined using a spectrophotometer.Human GIRK1 (Kir3.1) and GIRK4 (Kir3.4) cDNA  and RGS4 , and injected with cRNA as previously described.24 The surgical procedures had been approved by the Swedish National Board for Laboratory Animals. 1 ng of each GIRK1/4 subunit cRNA, 40 ng of RGS4 cRNA, 0.2 ng of dopamine D2S receptor cRNA, and when used 5.6 ng of βarr2 cRNA, were injected per oocyte. RGS proteins are GTPase-activating proteins expressed in native tissues, which speed up the G protein cycle such that GIRK channel activity more closely follows receptor occupancy by agonist.Oocytes were surgically isolated from female African clawed toads, −1 (for concentration–response experiments) or 3.5 ml min−1 (for desensitization experiments). Data were acquired at membrane potentials of −80 mV and sampled at 156 Hz using OpusXpress 1.10.42 software (Molecular Devices). To increase the inward rectifier potassium channel current at negative potentials, a high-potassium extracellular buffer was used , yielding a potassium reversal potential of about −40 mV. Ascorbic acid prevented the spontaneous oxidation of dopamine, which was purchased from Sigma-Aldrich .Following oocyte injection with cRNA and 6 days of incubation at 12 °C, electrophysiology experiments were conducted using the parallel two-electrode voltage-clamp apparatus, OpusXpress 6000A . Continuous perfusion, mediated by Minipuls 3 peristaltic pumps , was maintained at 0.5 ml mini.e.; same toad and preparation date). Normalized concentration–response data were fitted to the following equation:where Y is the normalized GIRK current response, top is the maximal response to the agonist in question, X is the logarithm of agonist concentration, and n is the Hill slope.Electrophysiological data were analyzed in Clampfit 10.6 (Molecular Devices). Concentration–response curves were calculated using the variable-slope sigmoidal functions in GraphPad Prism 6. In each cell, the current responses evoked by each concentration of agonist were normalized to the mean response evoked by 1 μM dopamine in oocytes from the same batch .All data needed to evaluate the conclusions are present in the paper, the supplementary material or have been made available at GPCRmdTMS, JS performed computational experiments which guided wet lab experiments carried out by AM. TMS, AM, BB and JS analysed and interpreted obtained results. MTF carried out the sequence analysis of receptors and their natural genetic variants. RA carried the GIRK channel experiments under supervision from KS. TMS, MTF and JS prepared figures for publication. TMS and JS wrote the manuscript with input from AM, KS and BB. JS supervised and coordinated the project.The authors declare no conflict of interest.SC-012-D1SC00749A-s001