We experimentally confirm on a promiscuous receptor (CCKAR) that mutations of some of these specificity-determining positions bias the coupling selectivity. Interestingly, Gs-GPCR complexes have more conserved interfaces, while Gi/o proteins adopt a wider number of alternative docking poses, as assessed via structural alignments of representative 3D complexes. Binding energy calculations demonstrate that distinct structural properties of the complexes are associated to higher stability of Gs than Gi/o complexes. AlphaFold2 predictions of experimental binary complexes confirm several of these structural features and allow us to augment the structural coverage of poorly characterized complexes such as G12/13.GPCRs are master regulators of cell signaling by transducing extracellular stimuli into the cell via selective coupling to intracellular G-proteins. Here we present a computational analysis of the structural determinants of G-protein-coupling repertoire of experimental and predicted 3D GPCR-G-protein complexes. Interface contact analysis recapitulates structural hallmarks associated with G-protein-coupling specificity, including TM5, TM6 and ICLs. We employ interface contacts as fingerprints to cluster G Selective GPCR-G protein complexes formation is critical for signal transduction regulation. Here, the authors use a data-driven approach to show that the structures of experimental and predicted complex interfaces inform, at least partially, on G protein binding preferences. They transduce extracellular physico-chemical stimuli to intracellular signaling pathways by coupling to one or more heterotrimeric G-proteins3, which are grouped into four major families: Gs, Gi/o, Gq/11 and G12/13 based on homology of their α-subunits4. GPCRs’ downstream activity is controlled by β-arrestins, which desensitize GPCRs’ activity and provide an additional layer of signaling modulation via ERK5. Receptor’s conformational change upon ligand binding leads to recognition and activation of intracellular G-proteins. Every mammalian GPCR displays a unique repertoire of G-protein-coupling preferences, ranging from highly selective to promiscuous profiles, which orchestrate specific cellular responses6. Aberrant signal transduction is linked to many pathological states, including cancer12. A mechanistic understanding of the signal transduction processes, integrated with multi-modal data associated with a disease state, can inform targeted therapies and personalized medicine procedures (e.g.13).G-protein-coupled receptors (GPCRs) constitute the largest family of cell-surface receptors, making them a primary pharmacological class which is targeted by approximately one-third of the marketed drugs17. Based on binding profiling from these large-scale experimental assays, sequence-based machine learning for coupling specificity has been proposed19. Phylogenetic analysis of co-evolutionary patterns inferred from sequence alignments of GPCRs and G-proteins have also provided insights into the sequence determinants of coupling specificity, for the entire GPCR family20 as well as for specific subfamilies21. The abundance of experimental structures for GPCRs in ligand-dependent, alternative functional states has also shed light on the structural hallmarks controlling receptor activation for class A GPCR22 as well as across classes23.The experimental profiling of specific coupling preferences is critical to understanding GPCR biology and pharmacology. The binding activities of GPCRs for transducer proteins are being quantitatively screened via medium-throughput methodologiesi coupled receptor complexes allowed for initial comparisons with Gs counterparts and highlighted a role of TM5 and TM6 as selectivity filter28. As a complement to the release of the MT1-Gi complex, we also systematically compared available Gs and Gi/o complexes with Class A receptors in terms of interface contact networks and G-protein docking mode similarity assessed via structural alignment29. The recent determination of four structures of the serotonin receptors  confirmed the role of TM5 and TM6, and in particular their variable length, as a selectivity filter for G-protein binding30. The authors also showed via bioinformatics analysis that this macro-switch is conserved among other class A GPCRs30. Yet, a comprehensive picture of the structural hallmarks of coupling specificity remains elusive.The determination of receptor-G-protein complex structures is also progressing rapidly, with over 360 complex structures deposited in the PDB (as of March 2023). The determination of the first structures of GIn this work we analyze through structural bioinformatics experimental, as well as predicted, GPCR-G-protein 3D complexes to shed further light on the structural basis of coupling specificity through the analysis of interaction interface contact networks, G-protein docking modes and binding energies Fig. .Fig. 1wos, 184 Gi/o, 9 Gq/11 and 3 G12/13 complexes, corresponding to 93, 17, 10, 3, and 2 unique receptors from Class A, B1, B2, C and Frizzled, respectively, and entailing 9 different G-proteins  , we derived specific SSE contact networks by pooling contacts on the basis of the bound G-protein. SSE contact networks highlight structural signatures specific to each coupling group. Certain SSEs are invariably central within the interface network, such as TM5, ICL2, or ICL3 for GPCRs .We considered a total of 362 3D experimental GPCR-G-protein complexes, comprising 166 GCRs Fig.  or H5 foCRs Fig. . Other eCL3 Fig. . Differenes Fig. . Overalls and Gi/o couplings  and Gq/11 (55% vs 45% in Cluster 1)  Fig. . The higs couplings are characterized by a significantly higher number of enriched contacts with respect to Gi/o ones  are example of Gs, class B-specific contacts . Particularly striking is the enrichment of the contact involving the highly conserved D3.49, which is found exclusively in Gi/o complexes  as well as all the contacts mediated by positions 8.47, 8.48, 8.49, 8.50  and Gi/o (PDB ID: 7EZH), and we focused the mutagenesis on the receptor positions forming the contact pairs most enriched in Gs or Gi/o complexes  and one (K3086.30R) were found to be Gs-over-Gi biased and Gi-over-Gs biased, respectively, and the rest of the five had no effect on the Gs-vs-Gi balance . These data confirmed the importance of the identified contacts in switching coupling preferences between these two G-proteins.To demonstrate the effect on G-protein coupling of the identified contact fingerprints, we employed a multistate-design computational protocoly14 Fig. ; Methods Gs Fig. . We founing Fig. . These ws complexes tend to cluster separately from Gi/o ones , as well as the BDKRB2-GNAQ and S1PR2-GNA13 complexes , Gi/o  as well as two G12/13 complexes (i.e. ADGRG1 and ADGRL3). Finally, a third, smaller outgroup cluster contains complexes involving class A, C, and F receptors most deviating from the other structures. Also in this case, the receptors in the largest, Gi/o enriched cluster show a more promiscuous tendency, with Gq/11 and G12/13 as most recurrent secondary couplings and Gs as the least recurrent one , for the whole Ras GTPase domain and in particular for regions such as H2, H3 as well as the C-terminal lobe of the Ras domain  and then calculated the Root Mean Squared Deviation (RMSD) of the Cα-atoms of conserved positions of the Gα subunit  used to stabilize the bound G-protein on the observed G-protein docking modes. First, we annotated the presence/absence of the nanobody for each complex subjected to RMSD clustering. We observed no correlation between RMSD clusters and the presence or absence of nanobodies . By considering all available GPCR Gα-subunit pairs with an experimentally resolved complex, we showed that the ∆G of binding of Gs complexes is significantly lower than Gi/o complexes  with both Gs and Gi/o proteins. Notably, the ∆G of binding for Gs is always lower and is characterized by higher ∆SASA compared to Gi/o irrespective of the slight docking modes variations observed in Gs complex structures of the same receptor . We first benchmarked AF-multimer by assessing the prediction performances with or without 3D templates for 125 representative GPCR-G-protein complexes with available experimental structures. Using 3D templates in the prediction slightly improves the performances, assessed by measuring the deviation of predicted vs. experimental interfaces via either the DockQ metric xes Fig. . As expeups Fig. . Notablying Fig. . On the In the present study, we have performed a computational, comparative analysis of 3D GPCR-G-protein complexes in their nucleotide-free state, to identify structural hallmarks of the interaction interfaces that might be linked to coupling specificity.28). More recently, structural determination of serotonin receptors in complex with either Gs or Gi proteins, accompanied by bioinformatics analysis of a representative set of class A complexes, supported the role of these secondary structure elements by highlighting a macro-switch, operated by TM5 and TM6 terminals’ variable length, which dictates selectivity towards Gs vs Gi/o30. Our unsupervised analysis of interface contacts entails complementary interactions between key positions on both receptor’s and G-protein’s sides. Gs complexes are characterized by significantly higher fractions of enriched contacts, that are mainly imposed by contacting positions on the G-protein’s side. However, the combination of GPCR and G-protein residues leads to maximum discrimination between the Gs and Gi/o groups. These results are reminiscent of the G-protein barcode model for coupling specificity, which emerges by the presentation of an evolutionary more rigid G-protein barcode to a more flexible receptor counterpart20. At the same time, Gs bound receptors are characterized by a less promiscuous binding, suggesting that the structural requirements for Gs specific binding overall impose discrimination for secondary couplings.Complexes involving different G-proteins are characterized by distinctive structural signatures, like contact networks displaying a different engagement of secondary structural elements such as TM5, TM6, and ICLs. This notion is in line with earlier comparative analysis highlighting a selectivity filter operated by TM5 and TM6 (commented oni/o). Notably, the DRY motif-mediated contacts, particularly D3.49’s ones, can be considered one of the main structural hallmarks of Gi/o vs Gs complexes. The latter is indeed characterized by a greater bending of G-protein’s H5 towards TM3’s C-term and ICL2, which function as hinges to concomitantly detach the G-protein C-term from the DRY motif. Other regions are characterized by a switch-like character, meaning that certain positions form contacts enriched in Gi/o and others in Gs, including: ICL2, which encompasses the known ICL2.51 specificity determinant position38, TM5, ICL3 and TM6. We noted that the switching behavior might pertain also to individual positions and depend on the specific contact partner. These residue-level features likely work in coordination with the macro-switch described for the serotonin receptors30. We leveraged our findings to implement a computational multi-state design procedure to predict mutations biasing a given coupling, starting from a promiscuous WT sequence (e.g. CCKAR), which we experimentally verified via NanoBiT G-protein dissociation assays. Experiments identified two mutations (V3116.33H and R3768.49V) to bias Gs and one (K3086.30R) to bias Gi signaling, confirming the importance of the identified contacts in switching coupling preferences between these two G-proteins.Certain SSEs are exclusively characterized by contact enrichments for specific G-protein  for structural prediction also allowed us to expand the structural repertoire of GPCR-G-protein complexes. This is particularly valuable for poorly characterized groups, such as G12/13 ones. Indeed, we predicted peculiar contact patterns at the TM5 and ICL3 that are characteristic of this group and might suggest unique structural requirements. Indeed, the critical importance of these regions also emerged in our previous effort to engineer a G12-DREADD, which was achieved by swapping shorter ICL3 loops from GPR183 or GPR132 on hM3D and experimentally validated to be functional14.The usage of a state-of-art AI model  compared to class A one (ADRB2)34. On the other hand, a comparison of the binding energies of Gs and Gi/o complexes also revealed significant differences, which holds true even when considering the complexes with the same receptor, further stressing the major contribution from the G-protein residues in priming the binding. Such differences in binding energies are also confirmed in AF-multimer predicted models, which also allow us to characterize the members of other families such as Gq/11 and G12/13. We speculate that Gs has a higher energetic barrier for activation due to its peculiar biological role of AC activation, which should be tightly regulated. The conformational restriction in Gs complexes might contribute to spatio-temporally fine-tune AC activation. On the other hand, looser Gi/o binding to cognate receptors could explain their reported faster nucleotide turnover40. Moreover, lower structural conservation and less affine Gi/o complexes are likely connected to the success of this coupling33, which is instrumental in providing a redundant mechanism for AC inhibition. Structural features and energetics are certainly not the only factors governing the evolutionary success of a certain coupling: indeed G12/13, which form complexes predicted as weak as the Gi/o ones, are the least successful couplings. Higher-order spatio-temporal dynamics, such as the more recent evolution of G12/1339, might explain these patterns.The observed structural differences are linked with different predicted binding affinities for the distinct coupling group complexes, with G41. Nucleotide-decoupled G proteins mutants, which bypass the intermediate-state complexes, are characterized by a degradation of coupling selectivity, indirectly highlighting the importance of the conformational dynamics of these intermediate-state complexes in guiding selectivity42. Future structural studies systematically targeting the intermediate states of GPCR-G protein complexes will improve the understanding of coupling selectivity.A limitation of this study is that most of the structures considered are in the nucleotide-free state. Indeed, previous studies have suggested that intermediate states other than the nucleotide-free complexes influence G-protein-coupling selectivityThe greater availability of experimental complex structures and increasingly accurate predicted models, including context-aware and diffusion-based deep learning techniques to predict alternative conformational states, will allow us to better understand the structural basis of G-protein-coupling specificity in the future. This knowledge will be key to design better-biased drugs, able to modulate only certain transducers, as well as it will be leveraged to improve the design of novel chemogenetic probes such as DREADDs.43 and Pfam44 provided by SIFTS45 (2023/01/28 update) to retrieve all the structures containing both a GPCR and a G-protein α subunit. We used the Pfam entry PF00503 to identify structures of G-α subunits, and Pfam entries PF00001 (rhodopsin receptor family - class A), PF00002 (secretin receptor family - class B), PF00003(class C receptors), PF01534 (Frizzled/Smoothened family) to identify GPCRs. We found 376 structures that met this criterion. If more than one GPCR or Gα chain were in the structure, we considered the pair of chains with the highest number of contacts between them  were kept for further analysis. We excluded the remaining structures that were considered as chimeric. In this way, we obtained 362 structures.We considered as contacts all the pairs of amino acids with less than 8 Å between their Cβ (Cɑ for glycine - see below), following standard practices employed for contact analysis in structural predictionsWhenever we found more than one structure representing the same GPCR-G-protein pair, we considered the one with the highest resolution to be the representative for each complex. In case that more than one structure had the same resolution, we chose the structure which covered the largest portion of the corresponding G-protein according to SIFTS. We found 125 GPCR-Gprotein pairs with at least one solved structure .To assess the reliability of AF-multimer models, we predicted the structure of the 125 GPCR-G-protein complexes with available experimental structures. We used the human sequences of the corresponding GPCR-Gα pairs in all predictions. We generated 3D models “using templates”, that is allowing AlphaFold-Multimer to use the 3D structural models available in the PDB (they are used only to predict the structure of single chains), and “without templates”, running the predictions setting --max_template_date=01-01-1900 instead, to avoid the usage of any available experimental template.33, were considered, respectively corresponding to 164 and 13 human GPCRs and Gα proteins (the three members of the transducin family were not considered). We removed 21 predicted complexes (975 structures left) based on unrealistic complex topologies, i.e. not having any of the following G-protein’s H5 positions, i.e. H5.16, H5.19, H5.20, H5.23, H5.24, H5.25, that we found to most recurrently mediate contacts with GPCR’s residues in experimental structures. To further remove low-quality models produced by Alphafold, we used the pDockQ metric47, which is fine-tuned on predicting the DockQ of the predicted complex with respect to experimental complexes. We kept only the structures with pDockQ ≥ 0.2348 (118 out of 975 structures removed)36.A total of 996 GPCR-Gɑ pairs, reported to bind in UCM36  and we kept only models with ∆SASA ≥ 1500 Å2, which is slightly less than the minimum ∆SASA found in all experimental structures. In this way we removed 32 more structures, leading to a total of 825 high-quality complex structures for downstream analysis , to remove protein terminals predicted with low confidence which might lead to artefactual contacts. Hence, we removed all the residues with pLDDT <70. We then performed the interface analysis on these trimmed sequences using the same procedure used for experimental complexes. As a last filter to remove low-quality structures, we used the output of Rosetta InterfaceAnalyzer46). We analyzed 362 solved PDB GPCRs-G-protein complexes, which according to G protein family classification comprised 184 Gi/o, 166 Gs, 9 Gq/11, and 3 G12/13 structures. We mapped interface PDB residues to Uniprot canonical sequences residues by using SIFTS residue level mappings from individual PDB XML files. The interface sequence positions were then mapped to GPCRdb numbering31  over the total number of GPCRs in contact. Such a fraction reflects the conservation of contact in each G-protein group. We constructed an interface contact network by considering the consensus list of GPCR-G-protein contacts from all complexes. Contacting positions were projected to the secondary structure elements of both GPCRs and G-proteins, represented as nodes of the networks. Node diameter is proportional to the total number of contacts mediated by the position of that secondary structure element. Edge width is proportional to the number of unique GPCRs mediating contact between the two linked SSEs. Edge coloring (bright to dark red) is proportional to the average of the contact fraction of individual position pairs, and it reflects the overall contact conservation between two SSEs. Dashed lines indicate contact not formed in each G-protein class but present in the other. We calculated network statistics such as node degree and centrality betweenness distribution through Cytoscape (https://cytoscape.org/)49 and customized Python scripts. All the analyzes have been done through customized Python scripts, using biopython libraries (version 1.78). Network drawings have been generated through Cytoscape.We considered residue-residue contacts mediating GPCR-G-protein interfaces as those having the Cβ spatially closer than 8 Å (Cɑ for glycine)  in python (https://cran.r-project.org/web/packages/vegan/index.html). We performed 106 and 104 permutations for PERMANOVA and PERMDISP, respectively.To analyze the statistical significance of the difference between Gi/o, 265 Gq/11, 87 Gs and 43 G12/13 structures according to UCM. Networks have been created for all position pairs as well as for cases where >0.2 complexes have the position pairs.Analysis was also done on Alphafold predicted complexes considering 430 GWard” method and Euclidean distance as metric, employing the clustermap function from seaborn library (version 0.11.1). Rows are color annotated to indicate: G-protein bound in the experimental structure, GPCR class, experimentally reported couplings (according to UCM). The top plot indicates the enrichment of the contacts observed at each position.We have generated interface fingerprints by creating position vectors by mapping the contacts  of either residue pairs , or the individual positions separately for the receptor and G-protein . We performed unsupervised, hierarchical clustering on rows (unique GPCR-G-protein complex) using the “For each consensus position of GPCR and Gprotein we calculated the log-odds ratio (LOR) from contingency Table CC and CN terms represent the number of GPCRs coupled to a specific Gprotein group (Gi/o or Gs) that are or are not, respectively, in contact at that position . NC and NN terms represent the number of non-coupled GPCRs for a specific G-protein, that are or are not in contact, respectively, at a given position . Contacts contributed from the loops, N-termini and C-termini of the GPCR where aggregated. We calculated the binning statistics of the log-odds ratio of contacts.s over Gi/o coupling, or vice versa, we have designed mutations through the Multi-state design by Rosetta51, as availale in the RosettaCommon software suite (version 2021.16.61629). As a starting template for the design, we have chosen CCKAR (UniProt Accession: P32238), a cholecystokinin receptor that has been solved in complex with Gs (PDB ID: 7EZK), Gi/o (PDB ID: 7EZH”) and Gq/11 (PDB ID: 7EZM)52. The CCKAR positions selected for design were either involved in specific contacts, based on comparative contacts analysis of Gs (7EZK) and Gi/o (7EZH) experimental complexes, or those with the highest log-odds ratios from GPCRomE-wide contact pair statistics. To switch Gs coupling, we chose to mutate the following positions: 3.54, ICL2.51, ICL2.52, ICL2.53, ICL2.55, ICL3(299), 6.26, 6.30, 6.32, 6.33, 6.36, 7.56, 8.47, 8.48, and 8.51. To switch Gi/o coupling we chose the following positions: 2.39, 3.49, 3.50, 3.53, ICL2.51, 6.25, 6.26, 6.29, 6.33, 8.48, 8.49, and 8.50. Next, we designated positive state and negative state complexes (7ezk or 7ezh). The positive state being the one whose structure and binding energy are preserved by mutations and the negative state being the one which is destabilized by mutations. We used a custom fitness function to describe the energy states of binding interfaces of both complexes with 12 different binding weights (1-12) and getting for each of them the best fitted structure with a different sequence of mutations for destabilizing either Gs or Gi/o coupling. We calculated the binding energy of the interface with InterfaceAnalyzer  and redocked the structures with RosettaDock using local refinement of the interface between GPCR and G-protein structures53. We chose the best redocked structures based on the Total score of the redocking tool and calculated the interface binding energy (REU) with InterfaceAnalyzer.To bias G53. Then we ran Rosetta InterfaceAnalyzer for the best redocked structures and assess the binding interface energy.To redock the mutated GPCR-G-protein complexes we used the Rosetta docking protocol with a docking:docking_local_refine flag to refine GPCR-Gα interface. We created top 20 redocked structures for each of the complexes created by MultiState design binding weights (1–12) and chose the best redocked structure for each of them based on the Total score provided by the protocol14, in which the interaction between a Gα subunit and a Gβγ subunit was monitored by the NanoBiT system (Promega). Specifically, a NanoBiT-G-protein consisting of the Gα subunit fused with a large fragment (LgBiT) at the α-helical domain (Gα-LgBiT) and an N-terminally small fragment (SmBiT)-fused Gγ2 subunit with a C68S mutation (SmBiT-Gγ2-CS) was expressed along with untagged Gβ1 subunit and a test CCKAR construct. The full-length human CCKAR was inserted into the pCAGGS expression vector with an N-terminal fusion of the hemagglutinin-derived signal sequence (ssHA), FLAG epitope tag and a flexible linker . The resulting construct was named as ssHA-FLAG-CCKAR. HEK293A cells  were seeded in a 6-well culture plate at a concentration of 2 × 105 cells ml-1 (2 ml per well in DMEM (Nissui) supplemented with 5% fetal bovine serum (Gibco), glutamine, penicillin and streptomycin), one day before transfection. Transfection solution was prepared by combining 5 µL (per dish hereafter) of polyethylenimine (PEI) Max solution , 200 µL of Opti-MEM (Thermo Fisher Scientific) and a plasmid mixture consisting of 200 ng ssHA-FLAG-CCKAR (or an empty plasmid for mock transfection), 100 ng Gα-LgBiT subunit (Gαs-LgBiT or Gαi1-LgBiT), 500 ng Gβ1 subunit and 500 ng SmBiT-Gγ2-CS subunit. For Gαs-LgBiT, to enhance expression of the NanoBiT-Gs sensor, 100 ng RIC8B plasmid was co-transfected. After incubation for 1 day, the transfected cells were harvested with 0.5 mM EDTA-containing Dulbecco’s PBS, centrifuged and suspended in 2 ml of HBSS containing 0.01 % bovine serum albumin  and 5 mM HEPES (pH 7.4) (assay buffer). The cell suspension was dispensed in a white 96-well plate at a volume of 80 µL per well and loaded with 20 µL of 50 µM coelenterazine (Angene) diluted in the assay buffer. After a 2 h incubation at room temperature, the plate was measured for baseline luminescence  and titrated concentrations of sulfated CCK-octapeitide  were manually added. The plate was immediately read for the second measurement as a kinetics mode and luminescence counts recorded from 5 min to 10 min after compound addition were averaged and normalized to the initial counts. The fold-change values were further normalized to those of vesicle-treated samples and used to plot the G-protein dissociation response. Using the Prism 9 software (GraphPad Prism), the G-protein dissociation signals were fitted to a four-parameter sigmoidal concentration-response curve with a constrain of the HillSlope to absolute values less than 2. For each replicate experiment, the parameter Span (= Top – Bottom) of the individual CCKAR mutants were normalized to those of WT CCKAR performed in parallel and the resulting Emax values were used to calculate ligand response activity of the mutants.Ligand-induced G-protein dissociation was measured by the NanoBiT-G-protein dissociation assayPlasmid transfection for the ssHA-FLAG-CCKAR and the NanoBiT-Gi sensor was performed according to the same procedure as described in the “NanoBiT-G-protein-dissociation assay”. One day after transfection, the cells were collected by adding 200 μl of 0.53 mM EDTA-containing Dulbecco’s PBS (D-PBS), followed by 200 μl of 5 mM HEPES (pH 7.4)-containing Hank’s Balanced Salt Solution (HBSS). The cell suspension was transferred to a 96-well V-bottom plate in duplicate and fluorescently labeled with an anti-FLAG epitope (DYKDDDDK) tag monoclonal antibody ) and a goat anti-mouse IgG secondary antibody conjugated with Alexa Fluor 488 . After washing with D-PBS, the cells were resuspended in 200 μl of 2 mM EDTA-containing-D-PBS and filtered through a 40-μm filter. The fluorescent intensity of single cells was quantified by an EC800 flow cytometer equipped with a 488 nm laser (Sony). The fluorescent signal derived from Alexa Fluor 488 was recorded in an FL1 channel, and the flow cytometry data were analyzed with the FlowJo software (FlowJo). Live cells were gated with a forward scatter (FS-Peak-Lin) cutoff at the 390 setting, with a gain value of 1.7. Values of mean fluorescence intensity (MFI) from approximately 20,000 cells per sample were used for analysis. Typically, we obtained an MFI value of 2000 (arbitrary unit) for WT CCKAR and 20 for the mock transfection. For each experiment, we normalized an MFI value of the mutants by that of WT performed in parallel and denoted relative levels.i/o and Gs complexes by performing Root Mean Square Deviation (RMSD)-based clustering. To calculate RMSD, we created a list of consensus positions based on all the sequences of GPCR and all G-protein in 362 complexes, by first mapping PDB residues to Uniprot canonical sequences via SIFTS45 and then to GPCRdb consensus numbers31. We considered 141 GPCR and 73 G-protein consensus positions defining respectively the consensus core of the 7TM domain and the Ras GTPase domain solved in all experimental structures. The complexes were fitted using the Cα atoms of the GPCR core, while the Cα of the G-protein core were used to calculate the RMSD after superimposition. Calculations were performed using the Superimposer function of the PDB Biopython module54 (version 1.78) through customized scripts. We performed hierarchical clustering on RMSD using the Ward method with Euclidean distance as metrics, using the clustermap function from seaborn library (version 0.11.1). We compared the distribution of the RMSD calculated among complexes of the Gi/o and Gs groups using a Wilcoxon rank-sum test. Results were displayed through matplotlib (https://matplotlib.org/) and seaborn (https://seaborn.pydata.org/) libraries using customized python scripts. We also calculated the root mean squared fluctuations of the G-protein consensus positions using the following equation:i and i in the reference structure ‘, which is the complex with the least RMSD deviation from the other complexes (i.e. centroid) in the Gs (PDB: 8E3X) and Gi/o (PDB: 8F7S) groups.We compared Gs and Gi/o groups RMSFs by performing a Wilcoxon test and we plotted each position and its standard deviation.We compared G55, using backbone constraints. Then we ran Rosetta InterfaceAnalyzer36 , from RosettaCommon software suite (version 2021.16.61629), specifying the chains of the GPCR and the Gα which are interacting in the complex. This protocol takes a multichain complex as input and computes a new structure in which the two chains of interest are separated. The interface energy and the ∆SASA are calculated as the difference in energy and SASA in the bound and unbound structure. We run InterfaceAnalyzer with the “-pack_input” and “-pack_separated” flags to optimize the side chain configuration before and after separating the chains. If a nanobody was present in a structure, we removed it before the relaxation step, to limit its influence on the analysis.To analyze the GPCR-Gα interface in a 3D structural model, we first relaxed the structure using the Rosetta relax application56.The interface energy is computed according to the Rosetta energy function, which includes physics-based terms that represent electrostatic and van der Waals’ interactions, as well as statistical terms representing the probability of finding the torsion angles in the Ramachandran plots. This score is indicated in Rosetta Energy Units (REU) and cannot be converted into the actual binding energy, but it gives a reasonable estimation of the stability of the complexhttps://mmcif.wwpdb.org/docs/sw-examples/cpp/html/index.html).We employed Pymol (v2.4.1) and ChimeraX (v1.5) to generate 3D cartoon representations. We employed customized scripts in python (version 3.8.11), using matplotlib (v3.6.0), seaborn (v0.11.1), and biopython (v1.78) libraries. We calculated residue-residue contact by using a customized script derived from the CIFPARSE-OBJ C + + library (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-5Reporting Summary


Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows. Designing such ligands for protein targets is an essential task in drug discovery. Molecular docking aims to predict the 3D pose of the ligand in the protein binding site. Docking is commonly used in structure-based virtual screening, wherein a large library of compounds are docked to a protein structure of interest. Their poses are used to score their binding affinity and the highest-scoring compounds are selected for experimental testing . Virtual1.1.Traditionally, molecular docking has been framed as a problem of energy minimization . The groRecently, several methods have been proposed that use machine learning (ML) to directly predict ligand poses . NotablyOne disadvantage of these ML approaches is that they focus on the problem of whole-protein (or “blind”) docking, implicitly combining the tasks of binding site prediction and ligand pose prediction. In practice, whole-protein docking makes the problem unnecessarily difficult; in most practical applications, researchers already know the binding site of their protein target and only want compounds that bind to that site . In this1.2.We note that both traditional docking methods that rely on energy minimization and the diffusion-based DiffDock share some cursory similarities. Namely, both methods initialize a ligand in a random pose and permute the pose  to give the final prediction. Traditional methods explicitly minimize an energy function, while the diffusion approach uses a neural network to propose new poses directly. Because DiffDock does not explicitly compute any pose score when position. the pose is generated, it requires a separate confidence model to rank the poses generated by the diffusion model.We hypothesized that we could train a pose scoring model in a diffusion-inspired manner. When inferring, we then explicitly minimize this score. This approach is conceptually simpler than DiffDock. Additionally, by explicitly training on proteins and ligands from different crystal structures, we hoped that our learned scoring function would be less sensitive to the specific protein conformation than physics-based methods.2D representation of the ligand, will compute coefficients for a scoring function that can then be quickly computed for a given 3D ligand pose. This score function computes the predicted mean absolute error (MAE) of the current pose as compared to the correct pose. We then use this function to minimize the score of the ligand pose using the L-BFGS optimizer . PLANTAIN uses a novel neural network that, given a protein binding pocket and a ptimizer .Using the rigorous benchmarking techniques outlined below, we show that our method improves upon the accuracy of GNINA , the nex1.3.Proper benchmarking of docking methods is difficult; it is very easy for hidden biases in the data to artificially inflate reported accuracy. We identify three best practices that should be followed to make docking benchmarks align as closely as possible with actual prospective docking runs.different crystal structure of the same protein. This procedure is called cross-docking  similarity, and place different clusters in different splits .Finally, when docking to a defined binding site, additional bias can be introduced by the definition of the binding pocket. Prior work has often defined the pocket by drawing a box around the ligand with a certain amount of padding. However, the actual pocket might be larger, and we cannot assume knowledge of the specific location within the pocket. Thus, in this paper, we follow 2.2.1.PLANTAIN consists of two parts: (i) a neural network encoder that uses a 3D protein pocket and a 2D ligand graph to produce coefficients, and (ii) a scoring function that uses these coefficients to rapidly evaluate candidate ligand poses. Following Autodock Vina , we use The encoder, which we call the Twister, is composed of 32 TwistBlocks, each of which updates the internal representation of the ligand atoms and bonds, the protein binding pocket at both the residue and atomic level, and interactions between the ligand atoms with themselves, the protein residues, and the protein atoms. After every other Twist-Block, there is a residual connection; each of the hidden features is summed with the features from two layers earlier.The Twister encoder takes as input the ligand molecular graph and a graph computed from the protein binding pocket. The nodes in the ligand graph are the heavy atoms, labeled with the element, formal charge, hybridization, number of bonded hydrogens, and aromaticity; the edges are the bonds, labeled with the bond order. Following previous studies , the nodEGATConv graph convolutions from DGL  loss function when training. We note that, when training, our model is given the protein pocket from a different crystal structure than the ligand cognate structure . This is done to ensure our model performs well on cross-docking tasks.2.2.1.In order to follow the best practices for docking benchmarking outlined above, we start with the CrossDocked 2022 dataset . This da2.3.To predict the ligand pose for a new protein-ligand complex, we start by running the Twister encoder to produce the score function coefficients from the pocket structure and 2D ligand graph. We then use 3.We benchmarked PLANTAIN on the CrossDocked test set and compared the performance with GNINA and Vina. Following prior convention, a pose is considered to be correct if it is within 2 Å root-mean-square deviation (RMSD) of the correct pose. We also report the accuracy within 5 Å RMSD. To account for the large disparity in the number of ligands per protein in the dataset, we report the average accuracy per pocket in the dataset .In order to compare our method to DiffDock, we created a subset of the test set without any proteins from the DiffDock training set. We then run all the methods, including Diff-Dock, on this set. We note that DiffDock is disadvantaged in this benchmark because it is not given any knowledge of the binding pocket.The results are shown in 4.In this paper, we present PLANTAIN, a docking method that uses a novel neural network encoder to generate coefficients for a very fast scoring function. This scoring function is used to optimize an initially random ligand conformation. We show, using best practices for benchmarking, that our method achieves state-of-the-art accuracy while running significantly faster than competing methods.There are many future directions to pursue. Most importantly, we desire to see how much PLANTAIN is able to assist in virtual screening. We plan on combining PLANTAIN pose prediction with GNINA’s binding affinity prediction in order to test its effect on virtual screening benchmarks.Additionally, the speed of PLANTAIN opens up many new possibilities. Previously, researchers have avoided flexible docking  because of performance concerns. It remains to be seen if PLANTAIN’s fast scoring method is amenable to flexible docking.https://github.com/molecularmodelinglab/plantain.Overall, PLANTAIN improves the state of the art in pose prediction, and there are clear directions for future improvement. We hope that this method will be useful in future drug discovery scenarios. Full source code for training and evaluating PLANTAIN is available at


The sequence analysis of the proteins and nucleic acids is most fundamental element of bioinformatics. Without these elements, it is impossible to gain insight deeper molecular and biochemical mechanisms. For this purpose, the computational methods like bioinformatics tools assist experts and novices alike in resolving issues relating to protein analysis. Similarly, this proposed work, for the graphical user interface (GUI) based prediction and visualization through the computations-based method done on Jupyter Notebook with tkinter package which allows the creation of a program on a local host platform and accessed by the programmer.The aim of this paper is to meet the requirements of experimentalists, not just hardcore bioinformaticians related to biophysical properties prediction and comparison with other proteins. The code for it has been uploaded on GitHub (an online repository of codes) in private mode.  Specifications tableThe functions of a protein is defined and given by their unique structure provided by their amino acid (AA) compositions and folds. Proteins with similar AA sequences exhibit similar bioactivity, similar structure, and function. However, Point Mutations, deletions, or alterations in the expression of the genes results in mutant proteins leading to many incurable diseases; proteins translated by these genes have different AA sequence than native protein which leads to the variation in the structure of proteins, different structures lead to different functions or deregulation in the biological processes. These deregulated biomolecules, such as nucleic acids and proteins, more specifically enzymes, cytokines, antibodies, aptamers, etc. which are crucial for certain complex diseases pathogenesis such as cancer, diabetes, Alzheimer's, Parkinson's disease, are known as biomarkers. The identification of biomarkers that are indicative of a specific biological state, is a major research topic in biomedical applications of computational biology Proteins are depicted as having diverse roles in the body because of their functions such as catalysts, cofactors, hormones, transporters, structure, signaling for biochemical reactions, maintaining physiology and many more. Therefore, it becomes necessary to examine the protein sequence and structure http://www.tulane.edu/~biochem/WW/PepDraw/). Most of these tools give individually computed parameters. No option is available to compare the biophysical properties computed for a query protein sequence with other protein in these above-mentioned tools. Lack of interactive interface is also there while using these tools. Instances of microheterogeneity can be identied by using our program. In classical biochemistry methods, solutions of high concentrations are required to calculate the physiochemical properties of proteins which our program predicts and compares with other proteins AA sequence.A catalog of bioinformatics resources known as ExPASy SIB Bioinformatics Resource Portal has several protein sequence analysis tools for biophysical properties among other key resources. Online tools Compute pI/mW, ProtParam, ProtScale, and AAcompIdent are related to the functionalities of our tool which are discussed here Hence, our proposed work has been created to investigate protein's physicochemical properties. This program provides precise results and a user-friendly interface. It compares the two protein sequences, using a set of parameters such as atomic composition, molecular weight, theoretical pI, AA composition, number of amino acids present in our AA sequence, aliphatic index, and GRAVY. These parameters can be used in various studies like protein structure prediction, function prediction, mutation prediction, etc. It also generates output in the form of graphs and pie charts, such as amino acid composition and atomic composition. The sequence of AA from the protein database can be used to investigate protein's properties. This application is made up of three parts: a graphical user interface, data visualization, and e-mail. Python's tkinter module is used to create the user interface. There are three windows in all. The primary window is where the user enters the target sequences of proteins that need to be compared. The parameters are displayed in other sub-windows. We used visual data approaches for improved visualization so that the user can easily understand the parameter analysis. Protein thermostability and hydrophobicity are also compared by GUD-VE. The aliphatic index and the grand average of hydropathicity (GRAVY) are used to compute this. It not only provides you with the output but also allows you to preserve it for future use by providing you with the option of receiving and saving your output by email. This will aid in the identification of proteins based on their AA compositions.The GUD-VE architecture not only computes the parameters individually but also compares the user-provided sequences using data visualizations like graphs and comparison templates. This proposed work is done on the Jupyter Notebook using tkinter package on the local host, accessed by the programmer only.This proposed work's architecture is based on three domains: Graphic User Interface (GUI), Data Visualization, and E-mail. The GUI of the application is developed on the tkinter Library. The Data Visualization is developed on the matplotlib Library of Jupyter Notebook. The e-mail is developed on smtp Library of Jupyter Notebook. The flowchart is depicted in Its architecture is divided into three parts namely GUI, Data visualization, and Email. The first root window of this proposed paper work, was made using the tkinter library is designed to take user input of two protein sequences, an Email address if reports to be mailed and a user-desired path to save the plots. Buttons labeled ‘submit’, ‘reset’, ‘compute parameters’ are also there, to perform their namely functions. This proposed work allows a user to calculate the parameters for a single protein if required, keeping this in mind it is designed to open computed results in two different root windows. These input of protein sequences are used for calculating the parameters, which are elaborated in later parts of this work and used for making plots and charts for data visualization. PyPlot from Matplotlib library is used for this part along with PIL library for saving and opening created plots and charts into and from the memory location.Users can also receive the report of computed parameters in form of a text document and plots in a png format attached and sent to them via email, stmplib library is used for this, which uses stmp.google.com and its port for sending emails. So, the architecture is named GUD-VE for GUI, Data Visualization, and Email, which works on user inputs of protein sequences, recipient email address, and a memory directory path.This application uses data visualization and a graphical user interface to make the platform of the application more user-friendly. In this section, we describe the procedures of the program separated into three main components which are graphical user interface, data visualization, and email.The programming of application is based on Python programming language. The GUI framework of the application is done using the tkinter library of python. Tkinter is the standard GUI library for Python. It provides a powerful object-oriented interface to the Tk GUI toolkit. The programming of this application is done such that there are three operational root windows. The first is the main window and the remaining two windows contain the parameters of the protein sequence provided by the user namely, Protein-1 and Protein-2. This could be achieved by importing various widgets from the tkinter library, such as Labels are used to place the required text on the window. The Button widget is used to give the command to the application to perform a particular function. Entry widget used to take information from the user like email ID and path. Text Box widget used to incorporate the protein sequences whose parameters need to be determined and compared. The Labels are used to display the text that the user needs to get informed about regarding the working of the application. There were two-button widgets that were used namely, “COMPUTE PARAMETER” and “RESET” buttons. These buttons take command from the user when they have entered the text or provided the apt information as per the requirement of the application for it to compute the parameters or if the user entered the wrong protein sequence it can be reset to its original empty text box form, respectively. The entry widgets and the text box widgets are used in the program to take input from the user in a single line and multi-line format, respectively. In this application, these widgets have been used to take the input like the email address, path, and the two protein sequences from the user that needs to be computed. This application takes the amino acid sequences of two proteins labeled as Protein-1 and Protein-2. For protein analysis, information in protein databases can be used to predict certain properties about a protein, which can be useful for its empirical investigation. This is what happens when the user presses the compute parameters button. Our proposed research work makes it simpler and thus takes two protein sequences instead of one and automatically compares their parameters along with the help of graphs. Thus, reducing the manual workload of identifying and comparing the proteins. The layout of the above widgets has been designed on the tkinter canvas and frames. Following are the features of the application and an explanation of its GUI. Text boxes have been placed beneath the labels of Protein-1 and Protein-2.The next feature in this application is that it takes the path as input from the user i.e., the location where the user wants to save the images of the plots that they want to save on their personal computer. This has been obtained using the PIL Python Image Library. The images will be saved in .png format in the assigned location with the name ‘c’ and ‘p’ for comparison template and plots, respectively. The input for the path is a required entity.The next and the most salient feature of this application is that the user can send the protein parameters and compared results obtained for both the proteins via e-mail. The GUI design for this is the entry widget that takes the email ID of the user as input and the “SUBMIT” button which helps to store the email address in the program. The above-mentioned E-mail feature in the application has been implemented using python library email.The outset of the sub root windows for Protein-1 and Protein-2 is connected to the “COMPUTE PARAMETER” button attached to the main root window. In The buttons for further data visualization have been anchored to the top right corner of the windows. These buttons are labeled as Atomic Composition Plot and the AA Composition Plot. These buttons have the command that is associated with the plotting of graphs using matplotlib library which has been explained later in this report. The first Protein-1 window contains two buttons as mentioned before but the Protein-2 window contains maximum of six buttons including the comparison plot, the compare parameter template, and the rest two buttons are associated with sending mail to the user-mentioned email ID. These buttons are labeled as “Send parameter test result via e-mail” and “Send comparison images result via e-mail” which compiles the protein parameters of both the proteins into one .txt file and which attaches the comparison plots and template in .png format in the body of the email and sent to the user mentioned email address separately, respectively. Thus, providing the users the choice to enter the email ID or path.The exceptional case in which these buttons won't be visible is when the only parameter for Protein-2 has been computed and also the comparison plot buttons won't be visible since there is no Protein-1 to compare it to. Another exceptional case is if the amino acids B (Aspartic Acid or Asparagine), X (unspecified amino acid), or/and Z (Glutamic acid or glutamine), are present in the protein sequence. These amino acids are ambiguous, and their atomic acid composition cannot be estimated. So, the button for the atomic composition plot is not visible.In this proposed work, along with data output of computed parameters, visual data representation techniques are used for a better understanding and analysis of those parameters it computes. Matplotlib library of python programming language is used for this purpose. This application calculates various physicochemical properties of AA sequence provided in the first root window, is used to make plots and charts. Our proposed work takes two protein sequence inputs and then assesses them to plot two types of graphs and one pie chart. First is a Bar-plot which shows the percentage composition of amino acids. The pie-chart is used to visualize the atomic composition of that protein and an overlapping line-plot is used for a head-to-head comparison of amino acid composition. Additionally, this proposed work also compares aliphatic indexes and hydrophobic characters of two proteins which helps to determine more thermostable and hydrophobic characters having protein. These two parameters are also expressed in visual form on an image template that can be viewed and saved to a user-defined location.plot1 function is used for making a bar graph, plot2 is used for making a pie-chart of the atomic composition of that protein whose sequence the user provides. Like function plot1, function plot2 also takes one argument of list datatype with computed atomic composition from another block of code and then makes the pie-chart.While plt.pie function is used, it takes two arguments, first, one is of computed atomic composition and second for labels on pie-chart, but instead of a list, manual names are entered inside plt.pie because only five atoms are computed, the third argument is autopct='%0.1f%%' which shows the percentage on pie-chart sets the output text up to 1 decimal point. Plt.title is used to set the title of this pie-chart as “ATOMIC COMPOSITION”.From matplotlib library, Image.save and Image.open are used to save this pie-chart in .png format and to access and open that saved pie-chart respectively. And this pie-chart is also opened up in a new window for both the proteins separately.The bar-graph and pie-chart made for each protein are handy to evaluate one protein's parameters but to visualize their difference in AA composition an overlapping line-plot is used in this proposed work for base-to-base comparison.plotcom is defined for making this overlapping line-plot, and it takes two variables as arguments, these two variables are the amino acid composition of the first protein and second protein, these two parameters are required to make a comparative plot, rest part of this function uses the same methodology as of other functions, again matplotlib library is used, plt.plot function is used separately for each protein, it overlaps the line plot of second protein on the first one.Function Apart from amino acid composition and atomic composition, this proposed work also calculates thermostability of globular protein using aliphatic index and predicts more hydrophobic protein using the Grand Average of Hydropathicity (GRAVY) method.com takes 4 arguments, which are aliphatic indexes of the two proteins and GRAVY values for both. It simply compares those two parameters of two proteins and then draws either a ‘ > ‘ or a ‘ < ’ on the template for two parameters.To show which protein is more thermostable and more hydrophobic, this proposed work has one saved template, which is used to edit and is saved with the help of ImageDraw library of Python. Function send is defined for this purpose, having the email address and password of sender's email, and takes recipients email address from email.get function. Stmplib library was used for this, it is easy to use and flexible library for handling the mail- related tasks.In the proposed work the parameters are calculated via the FASTA sequence provided is visible on the output root window, but for future reference or user ease these computed parameters can be mailed via email to a user-provided email address. A report will be attached in a .TXT format file having parameters for both the proteins computed. Function set_payload and encoded with encoders.encode_base64. This encoded text file is attached and sent via stmp of the gmail i.e., stmp.google.com with port number 587, server checks login credentials and if found correct, text file is sent, and the session is ended with quit at the end of the block.The text file that has computed parameters is attached via Stmplib library is used, same as send function, send_img function have the sender email address and password for authentication, to attach the plots, a path variable path is defined which have the location of saved plots and is used to open the png format images in open function in ‘rb’ mode. Then data of these plots are attached using msg.add_attachment which takes image data, type, and subtype as arguments, for image these are defined as ‘image’ and ‘png’ in the subtype.To send plots by mail, The parameters included are described along with the calculation base for this proposed work as given below:User-provided sequence, which displays the amino acid sequence divided into 10 fragments each making it easy for the user to demarcate the position of each amino acid in the sequence. The number of AA displays the total number of AA present in the protein sequence. It is calculated by obtaining the length of the amino acid sequence. Molecular weight, which displays a mass of the protein sequence, based on 12 as the atomic number of carbon-12. It is calculated by summing the average isotopic masses of amino acids in the protein and the average isotopic mass of one water molecule.The amino acids (in three-letter format) included are: 'Ala', 'Arg', 'Asn', 'Asp', 'Cys', 'Gln', 'Glu', 'Gly', 'His', 'Ile', 'Leu', 'Lys', 'Met', 'Phe', 'Pro', 'Ser', 'Thr', 'Trp', 'Tyr' and 'Val'. Theoretical pI, which is calculated using pK values of AA, which were defined by examining polypeptide migration between pH 4.5 and 7.3 in an immobilized pH gradient gel environment with 9.2 M and 9.8 M urea at 15 °C or 25 °C. Amino acid composition, displays the name of the amino acid under the first column, the occurrence of each amino acid in the sequence under the second column, and its percentage composition in the sequence under the third column A total number of negatively charged residues, which displays the sum of the total number of Aspartic acid and Glutamic acid. The total number of positively charged residues displays the sum of the total number of Arginine and Lysine Formula, which displays the formula of the amino acid sequence using the number of carbon atoms, hydrogen, oxygen, nitrogen, and sulfur atoms data. The total number of atoms, which displays the total number of atoms in the amino acid sequence. It is calculated by summing up the number of carbon atoms, hydrogen, oxygen, nitrogen, and sulfur atoms present in the sequence.a = 2.9) and Leu/Ile-side chains (b = 3.9) to the side chain of alanine. This is one of the positive factors for the thermostability of proteins Aliphatic index, which is calculated and displayed according to the following formula given in GRAVY is calculated by summing the hydropathy values of each amino acid in the protein sequence, divided by the number of residues in the sequence This section includes the results obtained from evaluating the data inputted by the user. The result has been demonstrated separately under three headings as mentioned before in the methods section.The graphical orientation has already been explained in methodology GUI section 2.3.1 using the tkinter library. The final framework looks like (A) and (B).Third, comes the e-mail feature where the user must enter the email ID of the receiver i.e., whoever they want to send the result to. The result for the main root window has been demonstrated in the previous context. Now, the outcome of the sub root windows will be elaborated. The sub root windows get activated by pressing the “Compute Parameters” button, which has been provided for each protein i.e., Protein-1 and Protein-2 separately. The sub root windows contain the computed parameters and buttons for obtaining the image results as given in Fig. 3(A) can be accessed from the sub-root window separately for both the proteins from their respective windows. It opens in a new pop-up window using the PIL library.Explained in the previous section the number of amino acids or the amino acid composition is represented in a form of a bar graph for visual representation, three-letter abbreviations are used for amino acid names on the x-axis, and the number of repetitions on the y-axis. This bar graph given in Fig. 4(B) shows the type of pie-chart that opens when the “atomic composition plot” labelled button is pressed.The second available visual representation is a pie-chart showing the atomic composition of the protein with names of the atoms as the label and percentage composition as tags, this pie-chart is also available for both the proteins on both the sub-root windows. Fig. 4(A) can be accessed from the second sub-root window through the button labeled as “comparison plot”, which pops a new window with this image.After these two plots for a single protein, for comparison a line graph is plotted which shows one on one overlap for every single amino acid present in both the proteins. This graph is plotted after taking details about both the proteins, this plot Fig. 5(B) shows that protein 1 is more thermostable and more hydrophobic as compared to protein 2.The last button labelled as “compare parameters” opens a run-time edited image template which compares two important aspects of proteins, the first one being thermostability, calculated using the aliphatic index and hydrophobic nature, calculated using the GRAVY method, both described in the earlier section are compared for both proteins to show which protein is more thermostable and more hydrophobic. Fig. 5The parameters and comparisons done for two proteins and the plots generated can be sent to the user if he wants to receive a copy of the report and the plots and seen at the user's end. Computed parameters report is sent in .txt format and comparison report for both the proteins is sent in the form of .png format plots in the attachments to the given E-mail address and both can be downloaded and saved for future reference.Proteins physicochemical properties have a direct relation with their biological functions, especially GRAVY, thermostability, length of AA sequence, pI, and mW Unfortunately, there is a limited study done so far that provides the reasonable prediction and analysis of physicochemical properties of proteins. Cellular assays may be employed for quantifying the mutation's effect on the function and AA change's effect on protein's properties. There is another framework to integrate the protein's sequence data and variation information, its effect on physicochemical properties to interpret the information through bioinformatics-based methods. Combining these two approaches is necessary for the reliability of research and medical implementation There are many aspects of proteomic research which can be dealt using physicochemical properties of proteins. Identifying the disordered protein regions, peptide binding sites, differentiating the favorable and not favorable AA residues in water soluble and transmembrane protein like bioinformatics research is already done using physicochemical properties of AA sequences. Disordered protein region was identified with the help of biophysical properties of proteins because they mostly have polar and charged AA and their depletion takes place in hydrophobic AA. physicochemical properties of AA sequence which are hydrophobicity, volume, area, pI, and the indicator variables aliphatic, aromatic, branch and sulfur were used to predict the short peptide binding with Major Histocompatibility Complex Furthermore, this proposed work provides the user a text file that includes the number of AA, molecular weight, theoretical pI and AA composition also the information in three types of plot amino acid composition, atomic composition, and comparison between two protein sequences. Thus, allowing us the new interpretation of underlying physicochemical behavior in the query protein sequences. This proposed work's architecture not only computes the parameters individually but also compares the user-provided sequences using data visualizations like graphs and comparison templates. The most salient feature of this application is that it can send the computed data to a user-provided e-mail which makes the application more user-friendly.The first limitation of this proposed work is that it compares and analyses only two amino acid sequences. Since the calculations of the protein parameters are completely programming-based and the BioPython module is not used, the result of some exceptional protein sequences containing ambiguous amino acid sequences can be sometimes incorrectly predicted. Secondly, if, by mistake the user copies a faulty amino acid sequence, the tool will not show any error message. Moreover this program is not a server-based, thus the outcomes are limited to the data programmed in it. It is not supported by mainstream bioinformatics organizations, so the information is limited. We will further extend this proposed work to overcome these limitations and extend the physicochemical properties this proposed work can compute, compare and visualize.Not Applicable.10.13039/501100001501University Grant Commission (UGC), Ministry of Social Justice, New Delhi, India (Grant No. F1-17.1/2015-16/RGNF-2015-17-SC-HAR-20221).The present study was supported by the Ritu Chauhan: Conceptualization, Formal analysis, Supervision, Project administration, Writing – review & editing. Juhi Bhattacharya: Conceptualization, Methodology, Software, Writing – original draft. Rubi Solanki: Conceptualization, Methodology, Software, Writing – original draft. Farhan Jalees Ahmad: Conceptualization, Formal analysis, Writing – review & editing. Bhavya Alankar: Conceptualization, Formal analysis, Writing – review & editing. Harleen Kaur: Conceptualization, Formal analysis, Supervision, Project administration, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Mycobacterium spp. is known to stay alive within the macrophages of its host. Mycobacterium tuberculosis is an acid-fast bacterium that is the most common cause of tuberculosis and is considered to be the main cause of resistance of tuberculosis as a leading health issue. The genome of Mycobacterium tuberculosis contains more than 4,000 genes, of which the majority are of unknown function. An attempt has been made to computationally model and dock one of its proteins, Rv1250 (MTV006.22), which is considered as an apparent drug-transporter, integral membrane protein, and member of major facilitator superfamily (MFS). The most widely used techniques, i.e., homology modeling, molecular docking, and molecular dynamics (MD) simulation in the field of structural bioinformatics, have been used in the present work to study the behavior of Rv1250 protein from M. tuberculosis. The structure of unknown TB protein, i.e., Rv1250 was retrived using homology modeling with the help of I-TASSER server. Further, one of the sites responsible for infection was identified and docking was done by using the specific Isoniazid ligand which is an inhibitor of this protein. Finally, the stability of protein model and analysis of stable and static interaction between protein and ligand molecular dynamic simulation was performed at 100 ns The designing of novel Rv1250 enzyme inhibitors is likely achievable with the use of proposed predicted model, which could be helpful in preventing the pathogenesis caused by M. tuberculosis. Finally, the MD simulation was done to evaluate the stability of the ligand for the specific protein.Computational prediction and protein structure modeling have come to the aid of various biological problems in determining the structure of proteins. These technologies have revolutionized the biological world of research, allowing scientists and researchers to gain insights into their biological questions and design experimental research much more efficiently. Pathogenic  M. tuberculosis is a bacteria of pathogenic  species from the Mycobacteriaceae family that is known to be a causative agent of tuberculosis  which is considered as the foundation of tuberculosis chemotherapy and is used as the preventive treatment of tuberculosis. The catalase-peroxidase enzyme (KatG) is used to activate the INH, a pro-drug. Then there is a reaction of resulting active species with NAD+ (Nicotinamide adenine dinucleotide) to develop an INH-NAD adduct, which impedes the enoyl acyl carrier protein reductase (InhA), causing the inhibition of mycolic acid biosynthesis and hence, the mycobacterial cell death . Mutatioin-vitro drug discovery approaches available for the evaluation of numerous components against a pre-identified target, the process remains expensive and lengthy. Consequently, molecular docking is an alternative approach that provides every possible location of the ligand followed by the selection of the best one.Molecular docking is a computational technique that helps in prediction and conformation of a receptor-ligand complex, where ligand acts as a small molecule and the receptor can be a nucleic acid or protein. This dock system may produce several sites for the ligand in the receptor binding compartment . AlthougM. tuberculosis Rv1250 protein is a potential molecular target that can be used in the development and design of new drugs to combat tuberculosis  which is a part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It addresses a sequence of databases related to biomedicine and biotechnology and a significant source for bioinformatics tools and services. For the structure identification, SWISS-MODEL and Phyre2 servers were used which presented only 17.21% of the total predicted structure (http://zhanglab.ccmb.med.umich.edu/I-TASSER/)  which is an automated server that offers an easily accessible interface allowing the investigation of numerous proteins at the same time (http://servicesn.mbi.ucla.edu/Verify3D/), which ascertains the compatibility of an atomic model with its own amino acid sequence by allocating a structural class based on its environment and position (https://prosa.services.came.sbg.ac. at/prosa.php) to approximate deviation of Z-score from the high-resolution structures and to determine the error  . The strame time . The proame time . The modposition . Additiohe error .http://dogsite.zbh.uni-hamburg.de) . The protein model was submitted in the software using this tool and the results were generated. Further validation of the results was done by Castp . Finally, it was modeled in PyMOL software, a cross-platform molecular graphic tool that has been extensively used for 3D visualization of surfaces, proteins, small molecules, nucleic acids, etc.,  in a sdf format. This downloaded structure holds the information on the chemical properties and biological activities of small molecules. Further, sdf file was converted into pdb format by Open Babel and energy minimized by Universal Force Field (UFF) in PyRx 0.8  structure of the ligand isoniazid was retrieved from the PubChem database (PyRx 0.8 . The enehttp://www.swissdock.ch). There remains an automatic generation of the target protein structure as well as ligand structure for docking. Moreover, docking does not require computational inputs from users as all computations are made by server itself  (Molecular dynamics (MD) simulation is a technique that can be used effectively to understand macromolecular structure-to-function relationships . MD simu/prodrg) ; 2021. T/prodrg) , while f/prodrg) . The hyd/prodrg) .The Rv1250 protein sequence was obtained from the NCBI. After the sequence retrieval, BLASTp  was done for to check protein homologues of Rv1250 . Among tModel-1 was selected out of 5 models because it had the highest C-score value which signified that the model had a higher confidence .This tool also predicted the normalized B-factor, which is a denomination that indicates the degree to which protein residues/atoms have inherent thermal mobility. This value is determined in I-TASSER, by threading the template proteins from the PDB along with the sequence profiles obtained from sequence databases. The reported B-factor profile corresponds to the target protein’s normalized B-factor, designated as B=(B′-u)/s, where B′ is the raw B-factor value, u is the mean and s is the standard deviation of the raw B-factors along the sequence. The resulted graph showed that coils had more mobility than the alpha-helix because the coils had a larger peak.Ten templates for model building of Rv1250 protein were predicted . HoweverAfter building the final protein model, it was prepared for the further docking process . The SWIThe Ramachandran plot of Rv1250 generated by PROCHECK showed that 89.9% of the amino acid residues are in the most favoured region, 9.4% in the additional allowed region, 0.6% in the generously allowed region and 0.1% in disallowed region as shown in M. tuberculosis (• Residues (82.56%) had an average 3D-1D score ≥0.2• Amino acids (about 80%) scored ≥0.2 in the 3D/1D profile .Furthermore, the 3D verification showed that the predicted model was valid for the further docking process of Rv1250 protein of rculosis . FollowiIt was observed that the profile score of homology model was 82.56% which indicates that 82.56% of the amino acid residues had an average 3D-1D score of ≥0.2. Further, the model validation was done using ProSA and the z-score was obtained as −3.51 , which iFor the docking process, active site was predicted by the Dogsite scorer and Castp. These servers predicted an active site with sixteen amino acid residues  .The 3D model with an active site is shown in Docking of small molecule compounds into a receptor binding site and evaluating the binding likeness of the composite is an imperative section of the structure-based drug design technique . In thisM. tuberculosis by inhibiting the formation of mycolic acid , Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface (SASA), and formation of hydrogen bonds were analyzed during the molecular simulation .The C-alpha backbone atoms of the model protein and protein-ligand complex were examined for the dynamic stability and conformational changes during the simulation by studying the RMSD. The developed RMSD plot revealed that the model protein and protein-ligand complex developed equilibrium at 25 ns and the system was observed to be stable up to 100 ns during the molecular dynamics simulation . Ligand RMSF of the sample structure was calculated to determine the tractability of the protein backbone. RFMS signifies the changeability of C-alpha coordinates from its usual location ll through the simulation. In general, the loosely structured loops are identified with reference to the high RMSF values, whereas, less flexibility is represented by the secondary elements in protein. Here, in current study, the residue mobility for model protein and protein-ligand complex was calculated. Subsequently, the graph was outlined against the number of amino acid residues corresponding to the MD simulation curve . Table 5Radius of gyration (Rg) displayed the total compactness of the protein structure during the simulation. It is calculated as the distance between the center of mass of all atoms of protein and its terminal in a particular time interval . The dynSolvent accessible surface area (SASA) determines the area of the solute which can interact with the solvent molecule via vander Waals forces . The SASHydrogen atom covalently attached to an electronegative atom can form hydrogen bonds within molecules or with other electro-negative atoms. Hydrogen bond of the GROMACS was utilized to discover the number and distribution of hydrogen bond in the model protein and the complex with an aim to investigate the stability of system through MD simulation period of 100 ns . Intra aM. tuberculosis Rv1250 protein is a promising molecular target for the fabrication of new drugs. For this purpose, various bioinformatics tools were used to perform homology modeling and docking of this uncharacterized protein. The predicted model was energy minimized by SWISS PDB viewer. Further energy minimized model was validated by and Ramachandran plot, Verify-3D, and ProSA. Molecular docking results suggested that isoniazid interacts at the binding site and exhibits the non-covalent interactions with amino acid residues of RV1250. Further, molecular dynamics results displayed the stability of isoniazid with Rv1250.In order to fight tuberculosis,