COMPUTER SIMULATION TO UNDERSTAND HOW ENZYMES WORK

Transcription

1 COMPUTER SIMULATION TO UNDERSTAND HOW ENZYMES WORK Universitat de Barcelona, ICREA & Institute of Theoretical and Computational Chemistry, Spain

2 What are enzymes? Adapted from /introduction-protein-classificationebi/protein-classification

3 What are enzymes? Most enzymes are proteins Enzymes serve as catalyst to accelerate biochemical reactions Enzymes are not changed in this process

4 DIGESTIVE ENZYMES Saliva: amylase GLYCOSIDASES Long carbohydrate molecule Stomach: proteases digestion Picture from Small intestine: lipases, amylase, lactase sugar units (monosaccharides)

5 Starch is our main source of energy Starchy foods glycosidic bonds ( 1/3 of our daily food intake)

6 The power of enzymes as catalysts t 1/2 = 1 billion years age of the earth t 1/2 = 1 million years O-glycoside hydrolysis 5-8 million years t 1/2 = 1 thousand years t 1/2 = 1 year t 1/2 = 1 day t 1/2 = 1 minute R. Wolfenden & M. J. Snider. Acc. Chem. Res. 2001, 34,

7 How enzymes work Enzymes lower the activation energy of the biochemical reaction Without enzyme With enzyme B PASS A Energy A Activation energy A B Reaction pathway B 2010 Nature Education All rights reserved.

8 How enzymes work B PASS A GLYCOSIDASE

9 Enzymes are very specific Example: α-1,4-glucosidase cuts glycosidic bonds between atoms 1 and 4 of the α-glucose units of glycogen 1 4 n OH OH OH OH...

10 Enzymes and disease Lack of lactase causes lactose intolerance GLYCOGEN GLUCOSE UNITS LACTOSE GALACTOSE GLUCOSE Lack of α-1,4-glucosidase causes accumulation of glycogen in the liver, leading to the Pompe disease

11 Knowing how enzymes work is important for drug design Synthesis of molecules that inhibit a disease-related enzyme Relenza Tamiflu

12 Computer simulation to understand how enzymes work Computer simulation provide molecular details of enzyme action Short-lived states (e.g. transition states) can be identified Important for inhibitor (drug) design A B Reaction pathway

13 Impact of High Performance Computing The large increase of computer power in recent years has contributed enormously to the progress of computer simulation of enzymes (computational enzymology) Better models and precise methods can be used Meaningful predictions can be made

14 Clarifying mechanisms of enzymes that process carbohydrates Gas2 glucosidase, an antifungal target

15 Clarifying mechanisms of enzymes that process carbohydrates Raich et al. J. Am. Chem. Soc. 138, 3325 (2016) Simulation performed in MareNostrum (BSC)

16 Molecular dynamics Motion of atoms from m i d r dt 2 i 2 = de dr i (Newton s equations) r i (t) Initial conditions: Atomic positions (e.g. crystal structure): {r i } Atomic velocities (e.g. random velocities scaled to desired T): { } v i

17 Molecular dynamics initial positions and velocities : { r } and { v } i i t = 0 compute forces F i = de dr i Solve m i d r dt 2 i 2 = de dr i new { r } and { v } i i at t = t + t single time step t 1 fs (10-15 seconds)

18 Classical MD Molecular dynamics Ab initio MD Energy from a force-field E = E bond + E ang + E dih + E vdw + E elec Computer codes: GROMACS, NAMD, AMBER, Energy from quantum mechanics E = min E ρ(r) [ ρ(r), { R }] I Density Functional Theory Computer codes: CPMD, CP2K, QUANTUM EXPRESSO, C. Rovira Methods Mol. Biol. 305, 527 (2005) Biarnés, Nieto, Planas, Rovira J. Biol. Chem. 281, (2006)

19 Classical MD Molecular dynamics Ab initio MD Energy from a force-field E = E bond + E ang + E dih + E vdw + E elec Energy from quantum mechanics E = min E ρ(r) [ ρ(r), { R }] I Density Functional Theory

20 Multiscale methods The quantum mechanics / molecular mechanics (QM/MM) approach Active center (quantum mechanics) ab initio MD Rest of the system (molecular mechanics) classical MD for the development of multiscale models for complex chemical systems

21 Carbohydrate degradation by glycosidases glycosidic bond cleavage sugar units (monosaccharides)

22 Carbohydrates are very flexible Most abundant for a free sugar Sugar shapes Chair Envelope Boat Slide from Lluís Raich (University of Barcelona)

23 Sugars change shape when they bind to enzymes Boat-like conformation O O WHY?

24 Sugars change shape when they bind to enzymes Sugar distortion facilitates the biochemical reaction O twisted boat Biarnés et al. J. Biol. Chem Biarnés et al. J. Am. Chem. Soc. 2011

25 Sugars change shape when they bind to enzymes Sugar distortion facilitates the biochemical reaction Energy A Activation energy B Reaction pathway Biarnés et al. J. Biol. Chem Biarnés et al. J. Am. Chem. Soc. 2011

26 Example: Golgi α-mannosidase II 2 mannose Cell Golgi complex

27 Golgi α-mannosidase II Mechanism of action Initial complex O boat Petersen et al. J. Am. Chem. Soc. 132, 8291 (2010)

28 Golgi α-mannosidase II Mechanism of action transition state anticancer inhibitors Petersen et al. J. Am. Chem. Soc. 132, 8291 (2010) Williams et al. Angew. Chem. Int. Ed. 53, 1087 (2014)

29 Sugar shape Important for inhibitor design BUT The structure of the enzyme for most glycosidase enzymes is not known

30 Mapping sugar shape B 2,5 O S 2 3,O B mannose Energy map 1 S 5 O H 5 3 S 1 4 H 5 O H 1 1,4 B 4 C 1 B 1,4 4 H 3 2 H 3 2 H 1 1 S 3 5 S 1 B 3,0 2 S O 2,5 B Ardèvol et al. J. Am. Chem. Soc. 2010, 132, Metadynamics Laio and Parrinello, PNAS 2002

31 Mapping sugar shape glucose mannose xylose EXP Sugar fingerprints G. J. Davies, A. Planas, C. Rovira, Acc. Chem. Res. 45, 308 (2012). Iglesias-Fernández et al. Chem. Sci. 6, 1167 (2015).

32 Structural validation wrong conformation (high energy) correct conformation (low energy) β α Privateer software: conformational validation of carbohydrate structures Agirre, Iglesias-Fernández, Rovira, Davies, Wilson, Cowtan. Nat. Struct. Mol. Biol. 22, 833 (2015)

33 Carbohydrate synthesis glycosidic bond formation

34 Sugars on the cell surface virus bacteria Outside of cell Carbohydrates and Glycobiology Cell membrane Inside of cell (cytoplasm) Special Issue.March 2001: Vol no

35 Problems associated to a failure of cell-cell communication virus bacteria Inflammation Autoinmune Outside of cell Cell membrane diseases: asthma diabetes allergies Inside of cell (cytoplasm) multiple sclerosis Infection

36 Abnormal glycosilation: cancer cancer cells

37 N-acetylgalactosaminyltransferase 2 (GalNAc-T2) GalNAc-T2 attaches one sugar (GalNAc, a galactose derivative) to proteins It initiates protein glycosylation GalNAc-T2

38 Structure of GalNAc-T2 Linker Lectin domain Catalytic domain enzyme active site Collaboration with: R. Hurtado-Guerrero (U. Zaragoza) F. Corzana (Logroño) H. Clausen (Denmark) P. Bernadó (Montpellier)

39 Structure of GalNAc-T2 protein fragment enzyme active site Collaboration with: R. Hurtado-Guerrero (U. Zaragoza) F. Corzana (Logroño) H. Clausen (Denmark) P. Bernadó (Montpellier)

40 Peptide binding to GalNAc-T2 Metadynamics simulation The lectin domain catches the peptide and brings it to the catalytic site. Lira-Navarrete et al. Nat. Commun. 2015, 6, 6937.

41 Computer simulation Atomic Force Microscopy Computational microscope Adapted from: DOI: /acs.jcim.5b00249 (A. Magistrato) Lira-Navarrete et al. Nat. Commun. 2015, 6, 6937.

42 GalNAc-T2 biochemical reaction SUGAR TRANSFER SUGAR PEPTIDE UDP Angew. Chem. Int. Ed. 2014, 53, 8206

43 Catalase enzymes catalase hydrogen peroxide (H2O2) water (H 2 O) oxygen (O 2 ) Hydrogen peroxide: biological toxin byproduct of metabolism Giorgio et al. Nature Rev. 8, (2007)

44 Catalase-peroxidase (KatG) Antitubercular target KatG mutations cause drug resistance to the INH drug Movie by Peter Loewen (Universty of Manitoba, Canada) Isoniazid (INH)

45 Catalase-peroxidase (KatG) O Fe IV Fe Movie by Peter Loewen (Universty of Manitoba, Canada) Youngblood et al. J. Am. Chem. Soc. 2009, 131, D. Balcells, Adv. Org. Chem M = Pt, Ru, Ir

46 Summary - Sugars have many shapes, and enzymes take advantage of this to accelerate catalysis. - Protein glycosylation is governed by a flexible enzyme that captures the target protein. - Enzymes can be redesigned to produce energy.

47 Acknowledgments Current and former group members Xevi Biarnés (IQS, Barcelona) Víctor Rojas-Cervellera (Barcelona) Albert Ardèvol (MPI, Frankfurt) Javier Iglesias-Fernández (KCL, London) Lluís Raich (UB, Barcelona) Mercedes Alfonso-Prieto (UB, Barcelona) Pietro Vidossich (UAB, Barcelona) Theoretical collaborators Agustí Lledós (UAB, Barcelona) Peter Reilly (Iowa State University) Experimental collaborators: Antoni Planas (IQS, Spain) Ignacio Fita (CSIC, Spain) Peter C. Loewen (U. Manitoba, Canada) Gideon J. Davies (U. York, UK) Spencer Williams (Melbourne U, Australia) Henrik Clausen (U. Copenhaguen, Denmark) David Vocadlo (SFU, Canada) Ramón Hurtado-Guerrero (BIFI, Spain)