Laying down deep roots: Molecular models of plant hormone signaling towards a detailed understanding of plant biology

Transcription

1 Laying down deep roots: Molecular models of plant hormone signaling towards a detailed understanding of plant biology Alex Moffett Center for Biophysics and Quantitative Biology PI: Diwakar Shukla Department of Chemical and Biomolecular Engineering June 15, 2016 Blue Waters Symposium 2016

2 Outline 1) Research focus overview 2) The critical role of Blue Waters 3) Plant signaling pathways: Brassinosteroid signaling Abscisic acid signaling 4) Computational methods development: Evolutionary couplings-guided molecular dynamics 1

3 1) Research focus overview: what s so great about plants? June 7, 2016 Abnormally dry Moderate Drought U.S. Percent Area Severe Drought Extreme Drought Exceptional Drought 2 droughtmonitor.unl.edu

4 What s so great about plants? 3 Long et al. Cell. 161, (2015)

5 What s so great about plants? Kinases are the transistors of the cell Humans: 518 distinct kinases Arabidopsis thaliana (mouse-ear cress): 940 distinct kinases This hints at the biochemical complexity of plant signaling Manning et al. Science 298, (2002) Zulawski et al. BMC Genom. 15 (2014) Left image: Cornelius et al. Nat. Comm. 4 (2013) Right image: edwardbosworth.com 4

6 Plant hormone signaling at the Ångström/femtosecond length/time scales Length Time pm nm µm mm fs ps ns µs ms s ks Santner and Estelle, Nature 459, (2009) Magnifying glass image: commons.wikimedia.org 5

7 2) The critical role of Blue Waters Proteins structural dynamics are critical to their function Molecular dynamics simulations allow for detailed computational study of protein dynamics Structural changes often take place over computationally vast timescales Blue Waters image: bluewaters.ncsa.illinois.edu 6 UIUC campus cluster image: campuscluster.illinois.edu

8 Blue Waters allows for rapid sampling of protein structural dynamics > 4200 XK7 nodes Tesla K20X image: anandtech.com 7

9 Markov state model (MSM) approach p(i,t + τ ) = N j=1 p( j,t)p(i,t + τ j,t) Protein conformational dynamics approximated by a Markov chain Free energy landscape from energetically unbiased, non- Boltzmann distributed simulation sets (adaptive seeding of simulations) Allows for efficient, energetically unbiased sampling of protein conformational space 8 Pande et al. Methods 52, (2010)

10 Adaptive sampling Long trajectory (Serial seeding) Adaptive seeding 9

11 MSM-weighted free energy landscape π 1 π 2 State 1 State 2 p 11 p 12 p 21 p 22 πh 1 h 1 πh 2 h 2 2 N ΔF( x)! π i h i (! x) = β 1 i=1 ln N max! x π i h i ( x)! i=1 Allows for estimation of free energy landscapes from simulation data produced using adaptive seeding methods 10

12 3) Plant signaling: Brassinosteroid (BR) signaling BR No BR or BRI1/BAK1 mutation BRI1 BAK1 BRI1 BAK1 Kinase domains Wang et al. Annu. Rev. Genet. 46, (2011) 11 Müssig and Altmann Trends Endocrin. Met. 12, (2001)

13 What are the features of an active kinase? PKA, RCSB ID: 41AC, Gerlits et al. Biochemistry 52, (2013) AMP-PCP 12 Shukla et al. Nat. Comm. 5 (2014)

14 Both BRI1 and BAK1 αc helices are disordered 13

15 Both BRI1 and BAK1 αc helices are disordered 14

16 Both BRI1 and BAK1 αc helices are disordered Folded BRI1 Unfolded Src BRI1 EGFR 15

17 Is αc helix disorder a regulatory mechanism in plant kinases? EGFR asymmetric dimer suppresses αc helix disorder Similar mechanism in BRI1/BAK1? Original tree from: Zulawski et al. BMC Genom. 15 (2014) Original tree: Zulawski et al. BMC Genom. 15 (2014) Shan et al. Cell 149, (2012) 16

18 Towards an atomistic model of BR signaling 17

19 3) Plant signaling: Abscisic acid (ABA) signaling No ABA: stomata unaffected ABA : stomata closure S S +ABA - ABA ABA S-P Activation loop 18 Soon et al. Science 335, (2012)

20 Predicting ABA binding mechanisms ABA S81 K56 19

21 4) Computational methods development: Evolutionary coupling-guided molecular dynamics Work from other groups: Direct statistical couplings in residues over families of homologous proteins predict conserved native contacts Solve an inverse Potts model to infer direct couplings between residues N P(σ ) = Z 1 exp h i (σ i ) + J ij (σ i,σ j ) i=1 1 i< j N Ekeburg et al. Phys. Rev. E 87 (2013) 20

22 Using evolutionary couplings to accelerate sampling Toy model β 2 -ΑR MSM Serial Random Directional Serial Random Directional 21

23 Using evolutionary couplings to guide association 22

24 Acknowledgments Shukla Group Prof. Diwakar Shukla Dr. Balaji Selvam Shriyaa Mittal Zahra Shamsi Saurabh Shukla Chuankai Zhao Huber group Plant Biology, UIUC Prof. Steven C. Huber Dr. Kyle Bender 23

25 BRI1 and BAK1 phosphorylation BRI1 phosphoresidue pthr1039 pser1042 pser1044 pser1049 BAK1 phosphoresidue pser290 pthr312 pthr324 pthr446 pthr449 pthr450 pthr455 24