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Computational Modeling of Gpcrs: Towards Understanding Receptor Signaling for Biology and Drug Discovery

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Computational Modeling of Gpcrs: Towards Understanding Receptor Signaling for Biology and Drug Discovery Computational Modeling of GPCRs: Towards Understanding Receptor Signaling for Biology and Drug Discovery Vsevolod (Seva) Katritch JCIMPT-ComplexesBarcelona, | January Jul 13 7,th 2011, 2018 1 GPCRs in Human Biology & Pharmacology Nat Rev Drug Disc 5:993 (2006) >30% of all drugs GPCR targets GPCR Targets: >120 established > 800 potential 800 GPCR are receptors for: Disorders Targeted Clinically Neurotransmitters ( adrenaline, dopamine, Psychiatric, Learning & Memory, histamine, acetylcholine, adenosine, Mood, Sleep, Drug Addiction, Stress, serotonin, glutamate, anandamide, GABA) Anxiety, Pain, Social Behavior… Hormones & Neuropeptides (opioid, neurotensin, glucagon, CRF, Cardiovascular, Endocrine, Obesity, galanin, orexin, oxytocin, neuromedin, Immune, HIV, Reproductive … melanocortins, somatostatin, ghrelin, TRH, TSH, GnRH, , PTH, THS, LH… >30 total ) Neurodegenerative and Autoimmune Disease … Immune system (chemokine, sphingosine 1 phosphate) Brain Development, Regenerative Med. Development (Frizzled, Adhesion) Sensory Cancer Light, Taste, Olfactory (388) GPCR Structure and Function Thousands of Ligands - Chemical Diversity >800 Different Human Receptors (largest family in human genome) Share 7TM Fold But Diverse We use structure Structural and modeling Features to learn: • Molecular Recognition • Signaling Mechanisms and to design: Dozens of Effectors New tool compounds GPCR New receptor properties Consortium Katritch et al 2013 Annu Rev Pharm. Tox. 53, 531-556 Stevens et al. 2013, Nat Rev Drug Discov, 12: 25–34 Outline ➢Rational prediction of stabilizing mutations: CompoMug ➢New insights into GPCR function and allosteric mechanisms ➢Structure-Based ligand discovery for GPCRs 5 Why Stabilizing GPCRs? ➢ Crystallography: ➢ Synergistic with fusions and truncations ➢ Reduces heterogeneity ➢ Allows stabilization of active or inactive states ➢ Allows co-crystallization with low-affinity ligands ➢ Biophysical characterization ➢ SPR ➢ NMR ➢ Drug discovery ➢ More robust assays for HTS Navratilova et al. 2006 A. Biochem. 355 (2006) 132–139 ➢ NMR-based ligand screening ➢ Ligand soaking for large-scale SBDD GPCR Structure and Function • > 250 structures • > 55 unique GPCRs All required some protein engineering: ➢ 47 with fusion proteins ➢ 36 with mutations ➢ 25 with both ➢ All have truncations GPCR Consortium Katritch et al 2013 Annu Rev Pharm. Tox. 53, 531-556 Stevens et al. 2013, Nat Rev Drug Discov, 12: 25–34 GPCR Network Structure-Function Pipeline Multistage iterative process * Human Cell Signaling Stevens et al. 2013, Nat Rev Drug Discov, 12: 25–34 GPCR Network Structure-Function Pipeline New Rationally Designed Protein Fusions * * N-term: NOP, d-OR (1.8Å), GCGR, SMOH, mGluR1, AT1 … BRIL Chun et al. 2012, Structure 20: 967 * R ICL3 A2A (1.8Å), 5HT1B, 5HT2B, P2Y12 , LPA1, … Human Cell ❖ Higher success and rational design predictability Signaling ❖ Improved resolutions – as high as 1.8 Å ! Rubredoxin ❖ Less invasive fusion (especially N-terminal) Flavodoxin Initial Constructs: GPCR-826 project Surface Expression >30%: • 80% (718) of Nt_BRIL 1.19* • 60% (480) of ICL3_BRIL 7.78 6.25** 5.69 * Extended if structured (Cys bonds, ECD etc) **For shorter ICL3 residues are added ➢ 615 GPCRs purified ➢ 120 have strong monomer fractions Xuechen Lv et al, Protein & cell (2016) 10 GPCR Network Structure-Function Process CompoMug: Rationally Designed Mutations GPCR Consortium * * Rationally designed mutations for GPCR crystallization Peng et al., 2018, Cell 172,* 719–730; Popov et al, 2018, in revision Human Cell Signaling Stabilizing mutations: Experimental approaches Alanine/Leucine scanning Number of mutants: 300-2000 - b1AR (Serrano-Vega et al., 2008, Warne et al., 2009) - Adenosine A2A receptor (Magnani et al., 2008, Lebon et al., 2011, Robertson et al., 2011, Dore et al., 2011) - Neurotensin NTSR1 (Shibata et al., 2009, 2013) - mGLUR5 (Dore et al., 2014) - CRFR1 (Hollenstein et al., 2013) - FFAR1 (Hirozane et al., 2014) Directed evolution Number of mutants: > 1,000,000 - NTSR1 (Sarkar et al., 2008, Dodevsky et al., 2011) - a1AR, a1BR (Dodevsky, Plückthun, 2011) - Tachykinin receptor NK1 (Dodevsky, Plückthun, 2011) One might hope that in the future it might be possible to design thermostabilizing mutations, computationally predict them or transfer them from other receptors … Heydenreich et al., 2015, Frontiers in Pharmacology ComPOMuG: Computational Predictions of Optimizing Mutations in GPCRs Machine learning Sequence- Structure- based based Knowledge-based Popov et al, 2018, eLife 10.7554/eLife.34729 Knowledge Based (Class A only) ➢ 2 or more structures with this mutant ➢ Known transferable position ➢ Most of them destroy/modify a conserved functional site Positio Mutation Role Receptor (PDB ID) n 3.41 X->W stabilization of TM3 - TM5 5HT2B (4IB4), 5HT1B (4IAR), ADRB1 interface (5A8E), ADRB2 (3NY8), CXCR4 (3ODU), DRD3 (3PBL) 2.50 D->N Sodium pocket AA2AR (5WF5) 3.39 S->A Sodium pocket AA2AR (5WF5) 7.49 D->N Sodium pocket P2RY1 (4XNV), P2Y12 (4PXZ) 3.40 I->V, A P-I-F microswitch motif ADRB1 (4BVN), APJ (5VBL) 3.49 D,G->A DRY motif FFAR1 (5TZR), NTR1 (4XES) 5.58 Y-> A Conserved activation FFAR1 (5TZR), ADRB1 (4BVN) microswitch 6.37 L->A Interferes with DRY motif AA2AR (5IU4), NTR1 (4GRV) function X3.41W – “Roth” Mutation W3.41 P5.50 ➢ Stabilizing or neutral in most Class A GPCRs ➢ Helped to solve >6 receptors, including 2 in active-like state Roth CB et al, (2008) J Mol Biol. 376:1305-19. Activation Related Changes in Microswitches Removing switches can 3.40 I->V, A P-I-F microswitch motif decouple from agonist and 3.49 D,G->A DRY motif limit natural motions of receptor 6.37 L->A Interferes with DRY motif function AT2R AT1R β2AR-G* F265 P5.50-I3.40-F6.44 P223 Y318 I132 II III I IV R142 VII V NP7.50xxY VI DR3.50Y VIII Zhang H et al, Nature. 2017;544:327-32. Stabilizing mutations in conserved Na+ site APO TM NECA Theophylline ZM241385 O C TM TM Tm WT 47 53 53 62 D2.50A 47 58 54 60 S3.39A 50 61 55 63 N7.49A 54 64 56 64 D2.50N 58 64 59 66 7804 D2.50N 100 7804 APO 7804 NECA 7804 Theophylline 7804 ZM 50 % Signal Total 0 0 20 40 60 80 100 Temp (C) 7804 APO 7804 NECA 7804 Theophylline 7804 ZM V50 58.35 64.18 58.58 65.87 Slope 5.561 2.547 4.243 2.572 Katritch et al 2014, TiBS, 39, 233 White et al 2018, Structure 26, 259 Sequence-Based ➢ Goal: identify (and replace) “outliers”: residues which are rarely observed at certain position ➢ Some positions are highly variable – take into account relative conservation ➢ Results depend on the GPCR set – use 3 levels of sequence clustering: ➢ GPCR Class or branch (SI ≥ 25%) ➢ GPCR family (SI ≥ 30%) GPCR ID 1 2 3 4 5 ➢ GPCR subfamily (SI ≥ 35%) GPCR 1 L V L P W GPCR 2 L L L P W GPCR 3 L A L P W GPCR 4 L T L P W ... ... ... ... ... ... Target GPCR L S L S W Sequence-Based: Building Scoring Matrix Sequence-Based: Building Scoring Matrix Composite Scores Structure-based: Design of Disulfide Bridges ➢ Find a pair of residues with permissive geometry ➢ Full energy optimization and scoring energy strain Structure-based: Design of ionic locks ➢ Find a pair of residues with permissive geometry ➢ Full energy optimization and scoring energy strain Machine Learning Prospective Application to Mutant Discovery: 5HT2C structures 6BQG 6BQH Peng Y et al. (2018) 5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology. Cell 172(4):719-30 Mutation CompoMug Module aSEC* Tm (oC) ΔTm (C) quality ±SEM WT 50.4 ± 0.8 0.0 CompoMug prospective I621.41V Seq-based ~ -0.7 G691.48A Seq-based - -1.4 screening: 5HT D992.50N Knowledge - - 2C H8512.51E Struct-based N/A - G1032.54A Seq-based - -4.4 Y1253.23K Seq-based - -2.0 Y1253.23V Seq-based ~ -0.7 ➢ 40 mutations tested M1433.41W Knowledge - 0.6 R1573.55T ML & Seq-based - -1.8 ➢ 10 improved both Tm & SEC (~25%) R1573.55Q Seq-based - -2.0 ➢ Best shift DTm ~ 9°C T1694.40K Seq-based + 0.2 A1714.42L ML ~ 52.3 ± 1.2 1.9 ➢ 3 components contributed, but I1724.43A Seq-based - 1.1 I1724.43F Seq-based ~ 0.6 not Knowledge-based G1844.55A ML + 51.9 ± 0.1 1.5 N203ECL2D Struct-based - -2.6 F2205.45I ML ~ 0.0 10 ■ Knowledge based F2245.48Y ML &Seq-based - -3.3 C3607.45N C2355.59F Seq-based ~ 0.1 9 ■ Sequence based L2365.60R ML &Seq-based N/A - ■ Structure based V2405.64A Seq-based + 52.4 ± 0.5 2.0 8 V2405.64S Seq-based + 0.3 7 ■ Machine Learning G3146.38A ML - -4.0 6.57 L333 V ML &Seq-based + 53.7 ± 0.6 3.3 C 6 K3487.32A Seq-based - -4.4 O A982.49C C3607.45N Seq-based + 59.2 ± 0.5 8.8 5 3.38 G3627.47L Seq-based + 52.3 ± 0.7 1.9 6.57 A140 C Tm, Tm, L333 V G3627.47A Seq-based + 54.3 ± 0.7 3.9 4 D 7.55 8.49 L370 D Struct-based - -2.3 3 I374 D K3738.48E Struct-based - -0.4 I3748.49D Struct-based + 53.9 ± 0.8 3.5 2 I3748.49T Seq-based + 54.1 ± 0.9 3.7 Y3758.50F Seq-based - -2.4 1 N3818.56R Sequence-based ~ 0.6 0 T671.46C/G1032.54C Struct-based - - 1 2 3 4 5 6 7 8 9 10 V741.53C/A962.47C Struct-based - - A872.38C/A1714.42C Struct-based ~ - Mutations A982.49C/A1403.38C Struct-based ~ 52.8 ± 1.0 2.4 T3697.54C/Y3758.50C Struct-based N/A - 5HT2C: Mutations in the structural model 5HT2C: Combined mutations in complex with ligands 2 combo 3 combo 2 combo +Antagonist O DTm = 12 CO DTm = 13 CO DTm = 21 C 5-HT2C: Crystals and structures ➢ Combo mutations yield first 5-HT2C crystals with antagonists ➢ Single C3607.45N allowed crystallization of both agonist and antagonists ➢ Structures for agonist Ergotamine (3.0 Å) and antagonist Ritanserin (2.7 Å) Peng Y et al.
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