News Article | May 10, 2017
No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. All the crystals of Ca2+-ATPase were prepared by microdialysis as described4, 14, 20, 22. Solvent was exchanged by placing the dialysis buttons into buffers containing appropriate amounts of contrast medium. The density of the buffer was also measured directly. For higher (>40%) concentrations of iohexol (available as Histodenz from Sigma-Aldrich; also called Nycodenz), dialysis was prolonged for at least 48 h. Crystals were picked up by nylon loops and flash frozen with cold nitrogen gas in a cold room. They were stored in liquid nitrogen until use. All the data were collected at BL41XU, SPring-8, with optics optimized for small-angle X-ray diffraction. Specimens were cooled to 100 K or 40 K at a later stage. The detectors used were R-AXIS V imaging plate detector with 400 × 400 mm plate size (RIGAKU), a Q315 CCD detector (ADSC) and a PILATUS3 6M detector (DECTRIS). The camera distance was fixed to 600 mm, with a He path of 450 mm. A beam stop of 1 mm diameter was placed on the polyimide film at the downstream end of the He path. As a result, reflections with 200 Å to 3.2 Å Bragg spacing can be recorded (Extended Data Fig. 1e). Even with a dynamic range of 20 bits, at least two exposures were necessary to record the strongest reflections without saturating the detector with high-resolution reflections. For collecting data, a wavelength of 1 Å or 1.5 Å was used. The iodine atom in iohexol is expected to have an f ″ of 3.3 (at 1 Å) or 6.5 e− (at 1.5 Å), giving rise to a useful anomalous signal. Diffraction intensities were integrated and merged with Denzo and Scalepack33. Statistics for the merged datasets are listed in Supplementary Tables 3–7. The starting point for solvent contrast modulation is a set of crystals soaked in buffers containing different concentrations (ξ %) of contrast modulation medium. In these crystals, the electron density ρ at a point (x, y, z) can be separated into two parts: a constant part (the protein and the bilayer) and a variable part (solvent), the electron density of which changes linearly with ξ. The variable part can be expressed using solvent exchange probability P (x, y, z) and the mean solvent density , resulting in where indicates the normalized structure factor for the solvent with unit electron density (that is, 1 e− per Å3; Extended Data Fig. 1f). What we can measure for the nth dataset with an iohexol concentration of ξ is where Δθ (h, k, l) is the angle between the vectors representing |F (h, k, l)| and (Extended Data Fig. 1f). Thus, for any reflection, diffraction intensity should vary as a quadratic function of solvent electron density34(Extended Data Fig. 2a), and we can use , the best estimate of in a least squares sense, for further calculation instead of . As a special case, the amplitude of any centric reflection should vary linearly with solvent electron density (Extended Data Fig. 2b). As this is a linear equation, the amplitudes of the centric reflections can be used for refining the average solvent density . In any case, as is known, at least three measurements of diffraction intensities at different ξ are required to obtain |F (h, k, l)|, and Δθ (h, k, l). Nevertheless, as Δθ is not directly related to F , F or (Extended Data Fig. 1f), we need an initial set of phases for F (or, in reality, initial model for ρ )35. Yet, once density maps at two different ξ (usually 0% and maximum concentration) are generated, phases can be substantially refined by posing restraints on P (Extended Data Fig. 1h). Consistency among datasets at different ξ are maximized both in real and reciprocal space. In reciprocal space, as already described, should change as a quadratic function of ξ (equation (2); Extended Data Fig. 2a), if scaled properly. Systematic errors can arise from errors in abscissa (solvent electron density ) and ordinate (scaling factor K and overall temperature factor B). Initially, K and B of each dataset (at ξ ) are determined by rigid body refinement of the atomic model treating the entire molecule as one segment. They are applied to raw diffraction amplitudes to place on an absolute scale, as in K and ξ can be optimized in the subsequent steps by minimizing the residual R (Extended Data Fig. 2c) Nevertheless, restraint in real space through P (x, y, z) is much more direct and powerful. Therefore, we first calculate (6)and uses only the phase part of this to calculate as As the average of ρ , , must be zero if F (0, 0, 0) is not incorporated, has to be obtained separately, by bringing the density inside the protein ρ (x, y, z) to that expected from the atomic model. Therefore, is calculated as in which the summation is taken over all grid points (x, y, z) within the van der Waals radius of a protein atom and N is the total number of grid points. It is then added to ρ at each point in the electron density map. P (x, y, z) and ρ (x, y, z), that is, ρ (x, y, z) at zero solvent density, can be obtained by linear least squares method, as at point (x, y, z) is a linear function of (Fig. 1a). Because the solvent density can actually be changed only within a narrow range (0.35–0.45 e− per Å3) and far from 0 (Fig. 1a), the error in ρ (x, y, z) could be substantial. Yet, as strong restraints can be placed on ρ (x, y, z) and P (x, y, z) (Extended Data Fig. 1h), these parameters can be largely improved. First, P (x, y, z) is smoothed as in conventional solvent flattening (smoothing radius is set equal to the resolution of the diffraction data). Then (Extended Data Fig. 1h), (i) for any point in the solvent region, that is, >40 Å from the bilayer centre and >5.2 Å from the protein surface, P (x, y, z) is set to 1 and ρ (x, y, z) to zero (solvent flattening); the same rule applies to any point with P (x, y, z) ≥ 1. (ii) For any point within the van der Waals radius from a protein atom, P (x, y, z) is set to 0 and ρ (x, y, z) to the density calculated from the atomic model (protein flattening). (iii) For any other points in the interface area (outside of the solvent and more distant from a protein atom than the van der Waals radius; light green area in Extended Data Fig. 1h): if P (x, y, z) ≥ 1, P (x, y, z) is set to 1 and ρ (x, y, z) to zero; if P (x, y, z) ≤ 0, P (x, y, z) is set to 0, and if 0 < P (x, y, z) < 1, P (x, y, z) is not modified. Then, ρ (x, y, z) is updated as in where N is the number of datasets. As a result, equation (1) no longer holds. The parameter should be updated first, as it must be constant independent of (x, y, z). For this purpose, a weighted average residual for was calculated over points that satisfy 0.1 < P (x, y, z) < 1.0, as in Then we calculated again, as in equation (6), using updated P (x, y, z), ρ (x, y, z) and . For calculating new density maps, the phase part of and the amplitude part of were used after a proper scaling of . For this purpose, the scaling parameters (that is, K and B in equation (4)) were refined by minimizing the residual as defined by After ten inner cycles of refinement essentially in real space, we went back to reciprocal space to refine with updated . For this purpose, K and B in equation (4) were refined (using instead of ) by minimizing the residual as defined by with new . We then repeated these refinement cycles until R no longer decreased (30 outer cycles; altogether 300 cycles of refinement; Extended Data Fig. 2d, e). The protein in the same state is assumed to have an identical structure irrespective of the iohexol concentration. In reality, as the unit cell dimensions vary depending on the concentration of the contrast medium, the protein structure may also change accordingly. In fact, the diagonal of the unit cell changed from 261.7 Å (0% iohexol) to 267.8 Å (80%) for the E1⋅AlF −⋅ADP⋅2Ca2+ crystals. In other crystal forms, they changed from 247.6 Å (0%) to 248.5 Å (70%) for the E1⋅2Ca2+ crystal, 272.7 Å (0%) to 276.1 Å (70%) for the E2⋅AlF −(TG) crystals of C2 symmetry, 204.0 Å (0%) to 204.5 Å (70%) for the E2⋅AlF −(TG) crystals of P2 2 2 symmetry, and 600.8 Å (0%) to 595.5 Å (75%) for the E2(TG) crystals. To find an atomic model that best fits all datasets of different contrast medium concentrations, rigid body refinement treating the whole ATPase molecule as one segment was done exhaustively using reflections from 15 Å to the highest resolution. First, the starting protein atomic model was fully refined (up to ‘grouped B-factor’) using CNS24 for a particular crystal at ξ% iohexol. Then, rigid body refinement was carried out, using this atomic model as the template, against all the other diffraction datasets in the same state but at different concentrations of iohexol. The starting atomic models used were: 1SU4 (E1⋅2Ca2+), 2ZBD (E1⋅AlF −⋅ADP⋅2Ca2+), 2ZBG (E2⋅AlF −(TG) C2 symmetry), 1XP5 (E2⋅AlF −(TG) P4 2 2 symmetry) and 2AGV (E2(TG)) (Supplementary Table 1). An average of the R-factors taken over all iohexol concentrations was assigned as the ‘average’ R-factor of that particular crystal. Finally, the atomic model of the smallest ‘average R-factor’ was chosen for the common protein atomic model (Supplementary Tables 3–7). In all cases, the atomic model at an intermediate concentration of iohexol yielded the best results. Any diffraction dataset, whose R-factor exceeded 0.35 in the individual rigid body refinement was discarded at this stage. The electron density profile of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) bilayer from small-angle X-ray scattering32 was used as an initial model for the lipid bilayer (Extended Data Fig. 8a). That is, the peak height was set to 0.44 e− per Å3 and that of the acyl chain to 0.23 e− per Å3; the solvent part was set to that calculated from the composition of the solvent (0.35 to 0.45 e− per Å3). We examined the effects of errors in peak-to-peak distances and peak heights on the final refined structure. In addition to the standard 40 Å, the peak-to-peak distance was varied between 30 and 50 Å (Extended Data Fig. 8b). The profiles of the final models for the bilayer after refinements were very much the same, converging to a unique structure (Extended Data Fig. 8c). Also the height of the density peak was changed to 0.40 e− per Å3 from 0.44 e− per Å3, the figure obtained by small-angle X-ray scattering32, but the final profile of the bilayer was virtually the same. In the crystals of C2 symmetry, because of the presence of a twofold axis (that is, b-axis) that goes through the centre of the bilayer, the lipid bilayer has to be parallel to the ab plane of the crystal. However, in the case of the E2⋅AlF −(TG) crystal of P2 2 2 symmetry and E2(TG) crystals of P4 2 2 symmetry, a slanted bilayer is allowed. Due to crystal symmetry, such a plane can be represented by an equation c = (a + b)tan(θ), where θ is the angle between the plane and the crystal ab plane. At first, we calculated a plane that approximates positions of the residues that may anchor phospholipids (or more precisely, the Trp NE1, Tyr OH, Lys NZ and Arg NH1 atoms) for each leaflet, and the plane running through the middle of the two planes was defined as the centre of the lipid bilayer. Such calculation showed that the bilayer plane is inclined by θ = 11.3° (E2⋅AlF −(TG) or 2.9° (E2(TG)), which indeed yielded the smallest R-factors (Extended Data Fig. 8d). The angle of the bilayer plane calculated from the positions of the phosphate in the contrast modulation map after refinement was 12.4° for the E2⋅AlF −(TG) crystals, irrespective of the angle assumed in the initial one-dimensional model, provided that it was less than 16.7°. Nevertheless, as described, the R-factor varied considerably, and each leaflet tended to split into two if started from 0°. As seen from the formulation, phase information obtained by contrast modulation (that is, Δθ ) is used only for separating solvent and protein + lipid parts (that is, F and F ; equation (2)) and does not appear explicitly afterwards. Once a real space model is obtained, however, the consistency between the calculated and the experimentally obtained phase (that is, ΔΔθ ) must be examined and may be used for map calculation as a weighting factor. For this purpose, we define a kind of figure of merit (FOM) (Extended Data Fig. 2g) as Although the anomalous signal from heavy atoms in the solvent contains phase information, it cannot be used for obtaining phases in a form suitable for calculation of electron density35, 36. Yet, at least in principle, it provides a useful means for cross-validation of the phase information. For this purpose, R (lack of closure between the sum of ) defined as where the Bijvoet difference ΔF represents |F (h, k, l) − F (−h, −k, −l)|, was calculated (Extended Data Fig. 2f). As the anomalous signal is multiplied by |F |, R is a useful measure only when the content of contrast medium is very high (>60%). This is because the anomalous signal is so weak, as heavy atoms are scattered in a large solvent volume, not localized to well defined positions in the crystal lattice. Atomic models of a part of phospholipid (from the head to the carbonyl groups) were refined by simulated annealing with CNS24 based on the electron density maps calculated using structure factors obtained by contrast modulation (that is, ) after phase combination with those derived from atomic models. This was necessary to avoid problems arising from the CNS mask37 that defines the boundary between the protein (+ lipid) and solvent. The weights for the phases derived from contrast modulation were chosen so that it is 1 for the reflections at the Bragg spacing (d) of 15 Å or larger, and 0.0112e0.3d for those at d < 15 Å (0.05 at d = 5 Å). This is because phase difference rapidly increases from d = 10 Å towards a larger spacing (Extended Data Fig. 2h), whereas the contribution from the bilayer becomes very small at d = 5 Å (approximately 5% of the total amplitude; Extended Data Fig. 1g). The phases at 15 Å or lower resolution were derived entirely from contrast modulation. The atomic model was finally refined with CNS24 and Phenix38. Structure figures were prepared with Pymol (Pymol version1.7 Schrodinger LLC) and videos were prepared with Molscript39. To evaluate the impact on the R-factors of the atomic model for the bilayer in the crystal (Extended Data Fig. 2i), atomic models for the acyl chains are needed. They were taken from those in 100-ns molecular dynamics simulations, as they could not be built into the contrast modulation electron density maps. Here the molecular dynamics model was aligned using that of Ca2+-ATPase and the portions of the acyl chains that do not overlap with the protein or head groups were cut out and incorporated into the model of the crystal structure. The temperature factors of the acyl chain atoms were set to 999, which is the largest number allowed in a PDB file. Here, in addition to the hard mask that CNS24 generates for non-solvent part, a soft mask similar to that used in solvent flattening40 was also tried (Extended Data Fig. 2i). In the best case, that is, E2(TG), a combined atomic model decreased the R-factor nearly 60% in the lowest resolution region (Extended Data Fig. 2i). Starting atomic models were derived from the PDB entries 1SU4 (E1⋅2Ca2+), 2ZBD (E1~P⋅ADP⋅2Ca2+), 2ZBG (E2~P) and 2AGV (E2). The inhibitor (thapsigargin (TG)) in the entry was removed and the first-layer lipids modelled in this study were incorporated. Ca2+-ATPase was embedded in a fully hydrated DOPC bilayer, solvated and ionized using VMD41. The four protein structures were oriented in the membrane so that the planes that approximate the positions of the phosphorous atoms become horizontal in each crystal form as in Fig. 3. The positions and the orientations of Ca2+-ATPase were slightly different from those predicted27. All-atom molecular dynamics simulations of the Ca2+-ATPase in a box of 136 × 136 × 180 Å with explicit solvent and 471–475 phospholipids were performed for 100 ns in the NPT ensemble as described previously42 using NAMD2.8 (ref. 43). Protein atoms were restrained with a harmonic potential of 10.0 kcal mol−1 Å−2. CHARMM36 (ref. 44) force-field parameters were used for phospholipids and CHARMM27 (ref. 45) for the protein and ions, and TIP3P for water. The number of molecules and other parameters are listed in Supplementary Table 8. Atomic coordinates and structure factors for the reported crystal structures are deposited in the Protein Data Bank under accession numbers 5XA7 (E1⋅2Ca2+), 5XA8 (E1⋅AlF −⋅ADP⋅2Ca2+), 5XA9 (E2⋅AlF −(TG); C2 symmetry), 5XAA (E2⋅AlF −(TG); P2 2 2 symmetry), and 5XAB (E2(TG)).
News Article | May 15, 2017
Researchers from North Carolina State University have demonstrated that molecular dynamics simulations and machine learning techniques could be integrated to create more accurate computer prediction models. These "hyper-predictive" models could be used to quickly predict which new chemical compounds could be promising drug candidates. Drug development is a costly and time-consuming process. To narrow down the number of chemical compounds that could be potential drug candidates, scientists utilize computer models that can predict how a particular chemical compound might interact with a biological target of interest - for example, a key protein that might be involved with a disease process. Traditionally, this is done via quantitative structure-activity relationship (QSAR) modeling and molecular docking, which rely on 2- and 3-D information about those chemicals. Denis Fourches, assistant professor of computational chemistry, wanted to improve upon the accuracy of these QSAR models. "When you're screening a set of 30 million compounds, you don't necessarily need a very high reliability with your model - you're just getting a ballpark idea about the top 5 or 10 percent of that virtual library. But if you're attempting to narrow a field of 200 analogues down to 10, which is more commonly the case in drug development, your modeling technique must be extremely accurate. Current techniques are definitely not reliable enough." Fourches and Jeremy Ash, a graduate student in bioinformatics, decided to incorporate the results of molecular dynamics calculations - all-atom simulations of how a particular compound moves in the binding pocket of a protein - into prediction models based on machine learning. "Most models only use the two-dimensional structures of molecules," Fourches says. "But in reality, chemicals are complex three-dimensional objects that move, vibrate and have dynamic intermolecular interactions with the protein once docked in its binding site. You cannot see that if you just look at the 2-D or 3-D structure of a given molecule." In a proof-of-concept study, Fourches and Ash looked at the ERK2 kinase - an enzyme associated with several types of cancer - and a group of 87 known ERK2 inhibitors, ranging from very active to inactive. They ran independent molecular dynamics (MD) simulations for each of those 87 compounds and computed critical information about the flexibility of each compound once in the ERK2 pocket. Then they analyzed the MD descriptors using cheminformatics techniques and machine learning. The MD descriptors were able to accurately distinguish active ERK2 inhibitors from weakly actives and inactives, which was not the case when the models used only 2-D and 3-D structural information. "We already had data about these 87 molecules and their activity at ERK2," Fourches says. "So we tested to see if our model was able to reliably find the most active compounds. Indeed, it accurately distinguished between strong and weak ERK2 inhibitors, and because MD descriptors encoded the interactions those compounds create in the pocket of ERK2, it also gave us more insight into why the strong inhibitors worked well. "Before computing advances allowed us to simulate this kind of data, it would have taken us six months to simulate one single molecule in the pocket of ERK2. Thanks to GPU acceleration, now it only takes three hours. That is a game changer. I'm hopeful that incorporating data extracted from molecular dynamics into QSAR models will enable a new generation of hyper-predictive models that will help bringing novel, effective drugs onto the market even faster. It's artificial intelligence working for us to discover the drugs of tomorrow." The work appears in the Journal of Chemical Information and Modeling. Ash is first author of the paper and is funded by an NIEHS grant (T32ES007329). Other funding was provided by the NC State Chancellor's Faculty Excellence Program. Note to editors: An abstract of the paper follows. "Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories" Quantitative Structure-Activity Relationship (QSAR) models typically rely on 2D and 3D molecular descriptors to characterize chemicals and forecast their experimental activities. Previously, we showed that even the most reliable 2D QSAR models and structure-based 3D molecular docking techniques were not capable of accurately ranking a set of known inhibitors for the ERK2 kinase, a key player in various types of cancer. Herein, we calculated and analyzed a series of chemical descriptors computed from the molecular dynamics (MD) trajectories of ERK2-ligand complexes. First, the docking of 87 ERK2 ligands with known binding affinities was accomplished using Schrodinger's Glide software; then, solvent-explicit MD simulations (20 ns, NPT, 300K, TIP3P, 1fs) were performed using the GPU-accelerated Desmond program. Second, we calculated a series of MD descriptors based on the distributions of 3D descriptors computed for representative samples of the ligand's conformations over the MD simulations. Third, we analyzed the dataset of 87 inhibitors in the MD chemical descriptor space. We showed that MD descriptors (i) had little correlation with conventionally used 2D/3D descriptors, (ii) were able to distinguish the most active ERK2 inhibitors from the moderate/weak actives and inactives, and (iii) provided key and complementary information about the unique characteristics of active ligands. This study represents the largest attempt to utilize MD-extracted chemical descriptors to characterize and model a series of bioactive molecules. MD descriptors could enable the next generation of hyper-predictive MD-QSAR models for computer-aided lead optimization and analogue prioritization.
News Article | May 15, 2017
Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives. Before Sunday's NBA playoff game between his San Antonio Spurs and the Golden State Warriors, Spurs coach Gregg Popovich told the press: "To this day I feel like there's a cloud, a pall, over the whole country, in a paranoid, surreal sort of way that's got nothing to do with the Democrats losing the election." John Oliver saw that surreal paranoia and raised it some barely controlled vitriol and wide-eyed astonishment in a segment on Sunday night of his show "Last Week Tonight." "President Trump," he began, "two words that continue to sound revolting together like 'viscous discharge' and 'moist stockings'." Oliver, like the writers on SNL, addressed the firing of FBI Director James Comey. He compared the letter the president sent to Comey -- in which he said Comey had told him three times he wasn't under investigation -- to breaking up, oh so awkwardly, via text message. "That is the equivalent of a breakup text reading, 'While I greatly appreciate you informing me on three separate occasions that I was the most enthusiastic, dexterous and intuitive lover you ever had, I nevertheless must terminate your position as my girlfriend'. Eggplant emoji,'" he said. Oliver questioned Trump's intelligence, as well as America's. He said of the president: "Trump has somehow managed to be both a terrible and amazing liar and I don't know how that's physically possible." He then turned to the Trump tweet that threatened Comey with the possible existence of tapes of their conversations. "Tweets like that are actually really difficult to parse, because they are somehow both a borderline obstruction of justice and the meaningless rantings of a confused old idiot," he said. Oliver then described the president as a "Schrodinger's a***hole." He ended with a desperate appeal to those responsible for the US government's checks and balances to start checking and balancing before it's too late. Watching this and SNL on Saturday, it seems that much comedic criticism has slipped from sarcastic carping to unconcealed contempt. Should you believe the country is on the right track, this must make for confusing and even annoying viewing. Should you believe that things are getting very sticky, it must offer a queasy reassurance that you haven't yet broken up with your senses. Technically Incorrect: Bringing you a fresh and irreverent take on tech.
News Article | May 8, 2017
BWS category director Drew Tiffin said there was “sea-change” in the retailer’s approach to its beer aisles, and an exciting move that would tap into the opportunity of a category that is seeing triple digit growth. He admitted that until now, Asda had not been “the most advanced” when it came to craft beer when compared to the other multiple retailers, but the move would change this. “We hope we will become a real destination for craft beer drinkers,” he told db. “We want to build the most credible range in the multiples, so we are putting in 80 new lines – well over 100 if you include the regional beers.” “You can’t argue with the growth of the category, it is coming through and we want to embrace it.” Are craft breweries selling out the indies in supermarket move? The new lines coming into store this week are being adding as a result of stripped out duplication in the beer range, which includes some more mainstream lines, although Tiffin was keen to point out that this was “not at the expense of choice”. “We have a lot of bog-standard high volume lager that we sell a huge amount of, week in, week out, but we also know customers are happy to trade in between the various formats and pack sizes, and it is quite easy to consolidate that slightly without compromising on a sales perspective or a customer perspective,” he said. “Through that we can create space, so you’ll now see three dedicated bays to craft beer.” Ale and craft beer buyer Hywel Evans said the store was making a “big commitment” to the category that would give it greater credibility. Around 10% of the beer aisles will now be created to craft beers, he said, and it will be running a promotion shortly after launch selling 4 beers for £6. New beers added to the range include Brewgooder Clean Water (RRP: £5.50 per 4-pack) which is brewed by BrewDog on behalf of a not-for-profit foundation that supplies clean water projects to disadvantaged areas across the world, a bitter from Hull-based brewery Atom Brewery called Schrodinger’s Cat, (RRP: 1.60), a dry- hopped Bad Co’s Pale Aura (RRP: £1.70), Brew York’s Little Eagle session (RRP: 1.79) and a selection of American beers, including Stone IPA from the West Coast (RRP: £2.00) and Harpoon IPA from the East Coast (RRP:2.00). It will also continue its rotation of seasonal beers, with BrewDog Hop Fiction next in store. Tiffin said the retailer will concentrate on specific craft beer “hotspots” were these styles of beers have been outperforming most strongly – namely in the Scotland, Manchester, Brighton and the South Coast, Bristol and London – and making more regional beers available outside their local area. “It is all about choice – regionality doesn’t mean it has to be a local brew, we know that craft beer drinkers want to explore.” wine buying manager Ed Betts added. Tiffin said the real challenge was in getting the logistics right. ““The real challenge will be in getting this right, and we’re going to learn a lot. We’re going in big and bold but in some store we won’t be bold enough, in others we might give them more than they want or need – but that’s always the way.” The retailed has also slashed around 25% of its wine range to rebalance the portfolio and add in more higher-priced and premium wines, and boosted it range of craft spirits. As part of the move it has revamped the Wine Atlas range, and adding nine new wines to its premium own-label Extra Special wine range. Asda’s move follows announcement from Waitrose, Tesco and The Coop which have also boosted their range of speciality, craft and international beer. Waitrose added around 27% to bring its total craft beer range to 95 beers, while Tesco boostings its range by a third to 70 skus across its 400 high street stores.
News Article | June 19, 2017
Schrödinger is a leading provider of advanced molecular simulations and enterprise software solutions and services for its clients, which include all major pharmaceutical and biotechnology companies worldwide, as well as leading materials science researchers. Schrödinger also establishes deep partnerships and collaborations with companies in such fields as biotechnology, pharmaceuticals, chemicals, and electronics, and helped found the biotech companies Nimbus Therapeutics, Morphic Therapeutic, and Relay Therapeutics. Schrödinger's investors include David E. Shaw and Bill Gates. Through significant long-term investments in basic research, Schrödinger has made scientific breakthroughs across many areas of drug discovery and materials science. Hundreds of peer-reviewed scientific publications by Schrödinger scientists are frequently among the most heavily cited in their fields. Founded in 1990, Schrödinger has over 300 employees and operations in the United States, Europe, Japan, and India, as well as business partners in China and Korea. For more information, please visit www.schrodinger.com. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/schrodinger-to-participate-in-2nd-annual-cowen-and-company-futurehealth-conference-300475482.html
News Article | July 20, 2017
OSAKA, Japan & NEW YORK--(BUSINESS WIRE)--Takeda Pharmaceutical Company Limited (TSE: 4502) and Schrödinger Inc., a privately-held company dedicated to revolutionizing drug discovery through advanced computational methods, today announced the formation of a multi-target research collaboration directed to diseases that align with Takeda’s core therapeutic areas of interest. The unique collaboration will combine Schrödinger’s in silico platform-driven drug discovery capabilities with Takeda’s deep therapeutic area knowledge and expertise in structural biology. With a focus on simplicity, speed and agility, Schrödinger will lead the multi-target discovery effort with Takeda providing protein crystal structures to aid Schrödinger in using its computational platform to guide the design of new chemical entities. Schrödinger will be responsible for its discovery costs. “Schrödinger’s agility, drug discovery track record and revolutionary computational chemistry platform are key strengths that attracted Takeda to Schrödinger as a strategic partner,” said Stephen Hitchcock, Ph.D., Head of Research for Takeda. “Takeda’s business model is focused on creating mutually beneficial partnerships that capitalize on the complementary capabilities of both parties in order to accelerate the discovery of new therapies for patients. In this case, Schrödinger is taking the lead role in discovery, with Takeda playing a supporting role, leveraging its in-house structural biology team and therapeutic area expertise.” Under the terms of the collaboration, Takeda will have the option to exclusively license the programs from Schrödinger later in discovery at established economic terms, including pre-clinical, clinical, and commercial milestones of up to $170M per program, as well as royalties on future sales. Additional financial details are not being disclosed. “We are very much looking forward to partnering with Takeda and marshalling its biology and development expertise with our world class computational chemistry and drug discovery capabilities to advance these programs,” said Ramy Farid, Ph.D., Schrödinger’s President and Chief Executive Officer. “This alliance allows us the opportunity to fully exploit the power of our platform to accelerate the delivery of novel therapeutics to patients.” Takeda Pharmaceutical Company Limited is a global, research and development-driven pharmaceutical company committed to bringing better health and a brighter future to patients by translating science into life-changing medicines. Takeda focuses its R&D efforts on oncology, gastroenterology and central nervous system therapeutic areas plus vaccines. Takeda conducts R＆D both internally and with partners to stay at the leading edge of innovation. New innovative products, especially in oncology and gastroenterology, as well as Takeda’s presence in Emerging Markets, are currently fueling the growth of Takeda. More than 30,000 Takeda employees are committed to improving quality of life for patients, working with Takeda’s partners in health care in more than 70 countries. For more information, visit www.takeda.com/news. Schrödinger is a leading provider of advanced molecular simulations and enterprise software solutions and services to accelerate and increase the efficiency of drug discovery for its clients, which include all major pharmaceutical and biotechnology companies worldwide, as well as leading materials science researchers. Schrödinger also establishes deep partnerships and collaborations with companies in such fields as biotechnology, pharmaceuticals, chemicals, and electronics, and was a cofounder of leading biotech companies including Nimbus Therapeutics, Morphic Therapeutic, and Relay Therapeutics. Schrödinger’s investors include David E. Shaw and Bill Gates. Through significant long-term investments in basic research, Schrödinger has made scientific breakthroughs across many areas of drug discovery and materials science. Hundreds of peer-reviewed scientific publications by Schrödinger scientists are frequently among the most heavily cited in their fields. Founded in 1990, Schrödinger has over 300 employees and operations in the United States, Europe, Japan, and India, as well as business partners in China and Korea. For more information, please visit www.schrodinger.com. 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Theoretical Chemistry Accounts | Year: 2011
Relativistic basis sets of double-zeta, triple-zeta, and quadruple-zeta quality have been optimized for the 6d elements Rf-Cn. The basis sets include SCF exponents for the occupied spinors and for the 7p shell; exponents of correlating functions for the valence shell, the 6s and 6p shells, and the 5f shell; and exponents of functions for dipole polarization of the 6d shell. A finite nuclear size was used in all optimizations. Prescriptions are given for constructing contracted basis sets. The use of the basis sets is demonstrated for some atomic and molecular systems. The basis sets are available as an Internet archive and from the Dirac program Web site, http://dirac.chem.sdu.dk. © 2011 Springer-Verlag.
Schrodinger | Date: 2013-12-23
A method and system for calculating the free energy difference between a target state and a reference state. The method includes determining one or more intermediate states using a coupling parameter, performing molecular simulations to obtain ensembles of micro-states for each of the system states, and calculating the free energy difference by an analysis of the ensembles of micro-states of the system states. The method can be particularly suited for calculating physical or non-physical transformation of molecular systems such as ring-opening, ring-closing, and other transformations involving bond breaking and/or formation. A soft bond potential dependent on a bond stretching component of the coupling parameter and different from the conventional harmonic potential is used in the molecular simulations of the system states for the bond being broken or formed during the transformation.
Schrodinger | Date: 2015-04-29
Methods and systems for drug discovery collaboration provide collaborative drug discovery electronic workplaces simultaneously accessible by multiple user computing devices. In certain embodiments, a server computer running a server side application communicates with multiple user computing devices. The server side application communicates with electronic databases that define the parameters of each electronic workplace. Each workplace includes an indication of one or more items, such as compounds, and data pertaining to such items, such as computational and experimental data. Updates to a workplace made by one user may be saved to the workplace definition and propagated and displayed to other users. New items of interest may be added to a workplace. A new item added to a workplace may also be saved to the database and registered with the system for use by other users and in connection with other workplaces.
Schrodinger | Date: 2013-03-15
Methods for assessing the consistency and reliability of the calculations using cycle closures in relative binding free energy calculation paths. The methods are used for determining relative strength of binding between a receptor and individual members of a set ligands to form complexes between individual ligand set members and the receptor, in which the binding free energy difference with the lowest error is determined by probabilistic determination of the free energy differences and error distributions about those differences along each of the legs of the closed thermodynamic cycle that probabilistically lead to the hysteresis(es) value(s) observed for each closed of the closed thermodynamic cycle.