Förster’s theory shows that the relation between FRET efficiency (E) and the distance between two chromophores (R DA ) is described by: E = 1 / [1 + (R DA /R 0 ) 6 ] (1) The Förster distance (R 0 ) is experimentally determined and is calculated from: R 0 = 9780[J(λ) 2 η -4 φ D ] 1/6 Where J(λ) is the spectral overlap integral, 2 is the orientation factor (assumed to be 2 / 3 for random orientation), η is the index of refraction of the medium, and φ D is the donor quantum yield. For the CFP-YFP pair R 0 ( 2 / 3 ) = 49.2Å. FRET efficiencies can be calculated from molecular simulations by determining the donor-acceptor distance R DA , and the orientation factor 2 . The angles θ A ,θ D , and φ calculated for each donor-acceptor pair are used to calculate the orientation factor as: 2 = (sin θ D sin θ A cos + 2 cos θ D cos θ A ) 2 (2) An adjusted R 0 based on molecular simulation is calculated from each structure as: R 0 () = R 0 ( 2 / 3 ) × ( 3 / 2 ) 1/3 (3) The 2 adjusted R 0 is then used in Eq 1 to calculate the FRET efficiency (E). This data can be used to compare with experimental results to determine whether a structural model based on a crystal structure or MD simulation is in agreement with observed data. Table 2 SERCA/SLN/PLB Experimental CFP-SERCA/ YFP-SLN Calculated CFP-SERCA/ YFP-SLN Experimental CFP-SERCA/ YFP-PLB Calculated CFP-SERCA/ YFP-PLB (T) Calculated CFP-SERCA/ YFP-PLB (R) FRET Efficiency 0.20 0.093 0.38 0.035 0.032 R DA Distance (Å) 62 81.7 53 101.1 103.4 Orientation factor ( 2 ) 0.667 0.651 0.667 0.672 0.659 (Predicted values are shown in Italics) Center-to-center distances (above) and other parameters were measured and FRET was calculated (Table 2). Simulated FRET values are significantly lower than experimentally observed, suggesting that there exists some association between the XFP’s or between the XFP’s and SERCA . The simulation protocol assumes that there are no specific interactions between proteins and that the XFP’s can move freely . A more crowded environment may cause more protein - protein association in the case of SERCA - SNL/PLB than for the CerFP - VenFP constructs . 1. Singh, D.R., Dalton, M.P., Cho, E.E., Pribadi, M.P., Zak, T.J., Seflova, J., Makarewich, C.A., Olson, E.N., and Robia, S.L. (2019) J Mol Biol 431, 4429-4443. 2. Koushik, S.V., Chen, H., Thaler, C., Puhl III, H.L., and Vogel, S.S. (2006) Biophys J 91, L99- L101. 3. Autry, J.M., Rubin, J.E., Pietrini, S.D., Winters, D.L., Robia, S.L., and Thomas, D.D. (2011) J Biol Chem 286, 31697-31706. 4. Humphrey, W., Dalke, A., and Schulten, K. (1996) J Mol Graph 14, 33-38, 27-38. 5. Winters, D.L., Autry, J.M., Svensson, B. and Thomas, D.D. (2008) Biochemistry 47, 4246–4256. 6. Svensson, B., Autry, J.M., and Thomas, D.D. (2016) Methods Mol Biol-P-type ATPases protocols 1377, 503-502. 7. Chaing, J., Li, I., Pham, E., and Truong, K. (2006) Proceedings of 28th Annual International IEEE Engineering Conference in Medicine and Biology. 8. Ansbacher, T., Srivastava, H.K. Stein, T., Baer, R., Merkx, M., and Shurki, A. (2012) Phys Chem Chem Phys 14, 4109-4117. Simulation and FRET Analyses of SERCA, Phospholamban, and Sarcolipin Complexes Bengt Svensson, Joseph M. Autry, Tory M. Schaaf, Răzvan L. Cornea, and David D. Thomas Department of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis MN, USA. We have used molecular modeling and experimental FRET constraints of fluorescent protein fusion constructs to study molecular interactions and small-molecule effects on SERCA and its transmembrane regulatory peptide phospholamban (PLB) and sarcolipin (SLN). The sarcoplasmic reticulum calcium transport ATPase (SERCA) is reversibly inhibited in heart and muscle by PLB and SLN, with phosphorylation-induced relief of inhibition. We previously used fluorescent fusion-protein constructs to quantify the interactions between SLN, PLB, and SERCA1a. Results show that SERCA has 3-fold higher affinity for SLN over PLB, even though the average maximum FRET between SERCA and PLB is twice as high as for SERCA-SLN. In order to interpret FRET results (dynamic population distributions) in a three-dimensional, structural context, we performed molecular modeling and conformational sampling simulations. Starting points for molecular simulations were x-ray crystal structures: SERCA-SLN (3W5A), and SERCA-PLB (4KYT). Simulations generated an ensemble of conformations from which the inter-probe distance (R DA ) and the orientation factor (κ 2 ) were calculated. These two simulation-based parameters were then used to calculate FRET from CFP to YFP tags, allowing quantitative comparison between structure-based simulations and experimental results. Fluorescence spectroscopy studies were carried out at the University of Minnesota Biophysical Technology Center. Computational resources were provided by the Minnesota Supercomputing Institute. This work was supported by NIH grants to DDT (GM027906, HL129814, AG026160). Conflict of Interest Disclosure: David D. Thomas and Razvan L. Cornea hold equity in and serve as executive officers for Photonic Pharma LLC, a company that owns intellectual property related to technology used in part of this research. These relationships have been reviewed and managed by the University of Minnesota in accordance with its conflict-of-interest polices. More information on this and related work can be viewed at http://biochem.umn.edu FRET Calculation From Simulations Abstract References Acknowledgments etc. Conformational Sampling of CFP-SERCA and YFP-SLN/PLB With the aim to correlate structural models with FRET results models for the SERCA-SLN/PLB fluorescent fusion proteins were constructed. For CFP-SERCA/YFP-SLN, the crystal structure (3W5A) was used. For SERCA-PLB, only the TM-helix of PLB was crystallized (4KYT). The cytoplasmic helix of PLB was modeled as either embedded in the lipid head groups, PLB(T), or extended into the cytoplasm, PLB(R). Conformational sampling of the XFPs was run on all three models. To study the interaction between SERCA and it’s regulatory peptides SLN and PLB fluorescent fusion proteins were designed. YFP and CFP were fused to the N-termini of SLN, PLB, and SERCA using recombinant DNA technology and baculovirus expression. Methods Engineering of Fluorescent Fusion Proteins : cDNA encoding SLN and SERCA were cloned from rabbit skeletal muscle and PLB from dog heart. cDNA encoding CFP and YFP were purchased from Clontech. CFP and YFP cDNA were fused to SLN, PLB, and SERCA cDNA using DNA ligation at engineered restriction sites, producing fluorescent fusion proteins with the XFP at the N-terminus [3]. Baculovirus /Insect Cell Expression : Fluorescent fusion proteins were expressed in Spodoptera frugiperda (Sf21) insect cells by infection with recombinant baculoviruses encoding fluorescent fusion proteins. Confocal microscopy and SDS-PAGE demonstrated that YFP-SLN, YFP-SLN, and CFP-SERCA are correctly targeted and efficiently expressed in endoplasmic reticulum of Sf21 cells. Fluorescence Microscopy : Fluorescence images of Sf21 cells were recorded using a Nikon TE200S microscope equipped with metal halide lamp and CCD camera. Automated filter wheels were used to acquire CFP, YFP, and FRET-selective images. Fluorescence resonance energy transfer (FRET) was measured on a cell-by-cell basis using 3-cube FRET. Protein binding curves were plotted as a function of FRET vs acceptor concentration, using hyperbolic fit to determine the maximum FRET and dissociation constant (K d ) of each protein interaction, where K d is defined as the concentration at which half-maximal FRET is observed. CFP filters: Ex = 426-446 nm, dichroic mirror = 455 nm longpass, Em = 460-500 nm. YFP filters: Ex = 490- 510 nm, dichroic = 515 nm LP, Em = 520-550 nm. FRET filters: Ex = 426-446 nm, dichroic mirror = 515 nm LP, Em = 520-550 nm. Molecular Visualization : Structure visualization and creation of figures were accomplished using VMD 1.9.3 [4]. SERCA - SLN/PLB Fusion Protein Modeling : Initial models for CFP-SERCA, YFP-SLN, and YFP-PLB were created with DS Visualizer 2019 (BIOVIA, San Diego, CA) using the CFP structure (1RM9), YFP structure (3DQO) and either SERCA-SLN (3W5A), or SERCA-PLB (4KYT) structures. Conformational sampling were performed by in a similar fashion as done previously for CFP-SERCA [5, 6] using the FPMOD modeling tools which randomly generates models by rigid body rotation [7]. In the simulation the CFP and YFP were considered to move independently. The FPMOD software discards conformations that show clashes within the linker region or between the XFP and the rest of the protein. The software was modified so that in addition to that, conformations with clashes with the membrane were discarded. The membrane is represented by a 45Å thick geometric region. For each of the SERCA structural states 1,000 conformations were generated for each XFP. All pairwise interactions were considered after excluding conformations with structure clashes between CFP and YFP. Typically about 950,000 pair- interactions were used. Calculation of FRET parameters was done by software developed in house. The transition dipole vector for the XFP chromophores was chosen in analogy with published data [8]. Cerulian - Venus modeling : The initial model was built using DS Visualizer using the CerFP structure (2WSO) and the VenFP structure (1MYW). Conformational sampling was done with FPMOD with no additional filtering generating 25,000 conformations. Calculation of FRET parameters were done as above. SR lumen cytoplasm Fluorescent Fusion Proteins FRET Measures Binding Affinity and Distance FRET between the fluorescent fusion proteins CFP and YFP was detected using acceptor photobleaching using the 3-cube method in Sf21 insect cells. FRET binding curves determine binding affinity (K d ) and intermolecular distance. Binding affinity: SLN shows ~3-fold higher association with SERCA than PLB does with SERCA. Maximum FRET: PLB shows ~2-fold higher maximum FRET with SERCA than SLN does with SERCA, indicating that the CFP-YFP distance is ~10Å closer in the SERCA-PLB regulatory complex. 0 10 20 30 40 0.0 0.1 0.2 0.3 0.4 FRET (E) [YFP] (AU) Table 1. Cer17Ven FRET Efficiency Ave. Donor-Acceptor Distance Orientation factor ( 2 ) Experiment 0.38 ± 0.03 58 0.667 Calculation 0.28 61.5 0.634 Conformational Sampling of a CerFP-VenFP FRET Standard To validate the simulation and FRET calculation results, a model was constructed for a simpler system. The CeruleanFP-VenusFP joined by a 17 residue linker has been proposed to be used as a FRET standard 2]. / / / Conformational sampling has generated 25,000 structures. VenFP was kept fixed, and CerFP was allowed to move as a rigid body at the end of the 17 residue linker. Here 15 representative conformations are shown. The center-to-center distances (above) and other parameters were measured and FRET calculated (Table 1). Calculated FRET from the simulations is slightly lower than experimentally observed FRET, suggesting that there may be some association between the XFP’s or that more extended linker conformations are favored in the simulations . Average distance = 61.5Å R DA Distance (Å) Count R DA Distance (Å) Count R DA Distance (Å) R DA Distance (Å) Ave. = 103.4Å Ave. = 101.1Å Ave. = 81.7Å (Predicted values are shown in Italics) 1000 conformations were generated. Here, 25 representative conformations are shown for CFP- SERCA/YFP-SLN (side view). 25 representative conformations are shown for CFP-SERCA/YFP-SLN, CFP-SERCA/YFP-PLB(T), and CFP-SERCA/YFP-PLB(R) (top view). Despite the quite different location in space where YFP is attached to SLN or PLB, the distributions don’t look significantly different. This may be due to the flexible C-ter of the XFP plus linker, 12-14 residues, which can span a distance of up to ~30Å. Three starting models, CFP-SERCA/YFP-SLN, CFP-SERCA/YFP-PLB(T), and CFP-SERCA/YFP-PLB(R) used for conformational sampling and FRET calculations. SERCA-PLB SERCA-SLN R DA Related to these results is a recent paper from the Olson and Robia labs [1]. FRET was measured between Cerulean labelled SERCA2a and YFP labelled SLN and PLB, showing FRET Max of 39±7 and 27±4, i.e. higher FRET for SLN than for PLB. K d values also show the reverse results with 2.9±0.8 (SLN) and 1.5±0.4 (PLB). Are these differences isoform dependent? Further studies may be needed. o Simulation of fluorescent fusion proteins using FPMOD and our analysis tools provide useful information for a more rigorous interpretation of FRET data . o Good agreement between simulated and experimental results is demonstrated for CerFP - VenFP . o Current simulations are being improved by incorporating probabilities for XFP - XFP, XFP - protein interactions and using new models for SERCA - SLN/PLB complexes .