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Amir Barati Farimani: Machine Learning Reveals Ligand-Directed

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Amir Barati Farimani: Machine Learning Reveals Ligand-Directed Machine Learning Reveals Ligand- Directed Conformational Change of μ Opioid Receptor A MIR B ARATI FARIMANI B LUE W ATERS S YMPOSIUM 2017 Significance of µOR in Drug Design Not good! § One of the oldest drug targets within the pharmacopeia: § Control of analgesia, respiration, euphoria, dysphoria, sedation and physical dependence in the CNS as well as regulation of gastrointestinal motility Nature, 485, 321–326, 2016 Why Ligand-induced Conformation Important? TRV130 ibntxA Sufentanil How can we map the slow degrees of freedom? B G Protein binding B B’’ B’ A Beta-Arrestin binding? Death Valley 315–321 Structure of µOR (Crystal Structures) Nature, 485, 321–326, 2015 Simulations and Analysis Condition Aggregate MD Aggregate MD Total Number of Simulation, Simulation, Trajectories Generation 1 Generation 2 Apo (starting from 115.839 µs 119.620 µs 512 Active state) BU72 102.705 µs 145.794 µs 512 Sufentanil 105.298 µs 166.723 µs 754 TRV130 ~250 µs ~500 IBntxA ~250 µs ~500 PZM21 ~250 µs ~500 Total 1.5 ms ~3300 47 TB of data 4 most physically important components of tICA Components of ⍵1 Components of ⍵4 Residuei Residuej Phe84 Phe338 Residuei Residuej Trp293 Ala240 His319 Gln124 Components of ⍵2 Asn332-CA Trp293-CA Residue Residue Ser329-CA Asp114-CA i j Tyr75 Ile71 Tyr128 Met281-CA Met161-CA Tyr75-CA His319-CA His319-CA Tyr75-CA Tyr326 Ile107-CA Met281-CA His319-CA Thr67-CA Phe289 Phe289 Tyr336 NPxxY Tyr75-CA Leu121-CA motif Val81-CA Ser329-CA Tyr336 Phe338 Components of ⍵3 Residuei Residuej Ile278 Leu283_CA Tyr336 Asp114 His319 Thr67-CA Phe289 Asn150 Arg280-CA Leu283-CA Using only APO dataset NPxxY (Asn322, Pro323,…, Tyr326) motif Finding Key reaction coordinates/ switches Sufentanil MSM Apo MSM BU72 MSM Two active-like states and F338 as the key switch Active (crystal) TM2 Inactive (crystal) New State (MD) Val89 Val89 Tyr336 Tyr75 His319 TM6 TM7 Phe84 Bulge Gln124 Tyr326 Phe338 Phe289 Tyr336 BU72 Tyr336 inactive; Phe338(III), TM6- facing; Phe289(III), Trp293 membrane-facing; Phe289 TM6 occluding G Protein binding TM6 to TM3 Packing region Phe289 to Asn150 TM6 Phe338 Tyr336 inactive; Phe338(III), TM6-facing; Phe289(III), membrane- facing; TM6 occluding G Protein binding region Main role-players of µOR theater of the Deactivation Gi Inactive Active Tyr336 BU72 rotates and translates toward Phe289 Phe338 rotates Trp293 toward TM1 Tyr326 stabilizes Shifts Phe289 rotation away from TM6 TM2 rotates shifts toward outward TM5 Slowest Clockwork of deactivation Tyr326 Shifts away from TM2 Tight Connection between BU72 Orientation and Receptor Activation Mediated by Tyr326 in Binding Pocket MD Simulation of µOR BU72 Inactive Active (Crystal) (Crystal) 5.8 4.3 A Tyr326 Distance (A) – BU72 Active ∆G Tyr326 Trp293 Inactive TM6 – TM3 Packing (A) Ligand-protein interaction: internal signal cascade Sufentanil versus Bu72 thienyl ring Sufentanil shows more active-like behavior compared to BU72 Transfer Entropy to map the sequence of signal and causality 10 sparse-tICA features Induce 500K features in Network Network X, Page rank Find the shortest paths, compute the overall contribution to deactivation https://github.com/msmbuilder/mdentropy https://networkx.github.io/ Using ML and MSM significant states to screen drugs Figure 1: Opioid ligands exhibit both conformational selection and a) induced fit at µOR. (a) Combined free energy landscape of receptor incorporating unliganded, BU72, and Sufentanil Table 1: Docking to both MSM states and crystal structures (Xtal+MSM) conditions.Train Circles-test representAUC AUCprojections ofWilson MSM statesstatistically used significantly for improves ability over crystals alone (Xtal) to distinguish dockingsplit onto type the landscape.(Xtal) (LargerXtal+MSM (and) greener)99% CI circlesµOR agonists are more from antagonists. Table shows median ROC Area Under the Curve MSM (AUC) performance over 1,000 train/test splits for different split and model types. State 79 importantRandom as estimated0.73 by the0.88 Random Forest(0.76, model, while MSM MSM 0.83) Differences between Xtal and Xtal+MSM methods are considered statistically State 40 State 39 smaller (and bluer) circles are less important. b) Markovsignificant State (bold values) if the lower bound of a 99% Wilson scoring confidence Model reweightedFentanyl free0.74 energy0.84 plots (kcal/(0.84,mol) of µintervalOR projected (CI) is greater than 0.5. Notably, when either fentanyl or methadone Scaffold 0.89) analogs are removed from the training set (scaffold split, cf. Methods), models 4 onto tICA coordinates ω1 and ω4 in three different conditions:remain able to distinguish fentanyl (or methadone) derivative agonists from ω from leftMethadone to right, Apo,0.67 BU72,0.84 and Sufentanil(0.90,. Noteantagonists how . This indicates that models fit in this way have the capacity to discover Scaffold 0.94) Sufentanil strongly stabilizes or completely inducesnew new opioid states scaffolds in addition to derivatives of existing ones. inaccessible in the unliganded or BU72 conditions. c-e) MSM States 79, 40, and 39, respectively, are ranked by the RF model as among the most important for opioid prediction and all display significant changes compared to either crystal structure that are presumably responsible for changes in ligand coupling. ∆G (kcal/mol) Manuscript submitted, 2017 ω1 b) BU72 Induced Active Sufentanil Induced Active Xtal Actives Active Xtal Active Xtal 4 ω Inactive Xtal Inactive Xtal Inactive Xtal BU72 Induced Inactive ∆G (kcal/mol) ∆G ∆G ω1 c) d) e) W3186.35 Y3267.43 Y3267.43 W2936.48 W2936.48 W2936.48 H2976.52 F2896.44 K2335.39 Conclusions § Large scale simulations of MOR reveal conformational dynamics and new states beyond the crystal structures § tICA and ML approach reveals how drugs influence these dynamics § BlueWaters play a fundamental role in sampling § Selected 100 significant intermediate states and docked opiates to those § We predicted the agonism/antagonism by using the MSM states Acknowledgement Vijay S. Pande Evan N. Feinberg Pande lab.
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