Intrinsic protein dynamics: a novel molecular dynamics data analysis method shows causality between protein residue pair mo7ons

Transcription

1 Intrinsic protein dynamics: a novel molecular dynamics data analysis method shows causality between protein residue pair mo7ons Zeynep H. Gümüş Assistant Professor of Computational Genomics Department of Genetics and Genomics & Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai Adjunct Assistant Professor of Medicine Weill Cornell Medical College Assistant Member Drug Research Center Koc University

2 Overview: K-Ras protein KRAS 674 A12G/G12A/G12C/G12D/G12F/G12R/G12S/G12V/G12Y # Mutations 0 Ras aa Top commonly mutated oncogene in all cancers. Single point muta7ons in KRAS are gene7c drivers leading to cancer. Pa7ents with mutant KRAS tumors do not respond to EGFR inhibitors. 30 years of research & s7ll no drugs that directly target it in the clinic. There is a dire need for therapies for pa7ents with KRAS mutant cancers. How do muta7ons alter its dynamics func7on?

3 Function of KRAS protein KRAS is pivotal in cellular signaling. Binds to GTP or GDP.

4 Signaling through KRAS is dependent on the bound nucleo7de: GTP- bound state is ac7ve GDP- bound state is inac7ve

5 Structure of KRAS protein P-loop, Switch I (SI) and Switch II (SII) regions make up the active site. Its well-ordered conformations allow effector protein binding for KRAS signaling Vatansever, Gumus#, Erman#, Sci Rep, 2016.

6 KRAS muta7ons drive tumorigenesis because Oncogenic muta7ons impair GTP hydrolysis & freeze KRAS in ac7ve state, causing uncontrollable cellular growth and evasion of apopto7c signals.

7 Why is understanding KRAS dynamics important? KRAS proteins are in dynamic and flexible states and their disdnct characterisdcs cannot be idendfied by structural studies alone. Dynamics of different parts of KRAS may be coupled by correlated modons. Such correlated modons are essendal for the allosteric reguladon which can render KRAS dynamics producdve.

8 How can we define correlated mo7ons? To define correlated modons, several analysis methods for MD simuladon data exist in literature. One popular method is Pearson correla7on coefficient analysis of MD trajectories of residue pairs. Correlated MoDons and Residual FrustraDon in Thrombin JPCB 2013

9 Pearson coefficient: quandfies the correladon between pairs of residues that indicates allosteric coupling of their modons (based on MD trajectories) Disadvantage: coefficients are symmetric. We cannot idendfy which residue drives and which residue follows within a correlated residue pair.

10 Why do we want to reveal causality? Correlated modons of residues oyen have a direcdon or causal reladonship. Knowing the causality of allosterically coupled residues is not only of theoredcal interest but also can provide guidance in drug design. OR?

11 Short- term goals: analyze causality of correlated modons in KRAS compare acdve and inacdve WT KRAS to idendfy acdvadon mechanism compare WT and mutant KRAS to idendfy effects of mutadons Long- term goal: UDlize dynamics informadon in future drug design studies WT- GTP vs. WT- GDP WT vs. Oncogenic Drug design

12 METHOD

13 MD SIMULATIONS Valuable for understanding the dynamic behavior of proteins What are the advantages of MD? Detailed informadon Processes (Dme- dependence) IdenDfy interacdons Effects of the modificadons/ mutadons to the system or enviroment - Easy to introduce

14 MD simulations performed at constant temperature (310K) and pressure (1atm). Calculated fluctuations of each protein residue during each simulation. Fluctuation of residue i at timestep t is represented by ΔR i (t). We record ΔR i (t) values of each residue i at every timestep t of the simulation as time-series. mean t 1 t 2 t 3 t We use fluctuation time-series in 4 causality analysis.

15 Here, we introduce a new method for the analysis of MD simuladon data with condi7onal 7me- delayed correla7on (CTC) approach. For correlated residues, CTC helps us iden7fy if one residue drives the mo7on of another.

16 Condi7onal Time- delayed Correla7on (CTC) Method can idendfy causadon between Dme- series variables. Based on calculadon of 7me- delayed correla7ons. Extensively used in causality analyses in economics, leading to the Nobel prize, and in other fields: neuroscience, cell biology. To our knowledge, no applicadons to MD simuladon data have been reported.

17 For every residue pair (residue i and residue j): STEP I CORRELATION 0 0 C ij Calculate correlation between residue i residue j (C ij ) C ij : Correlations of ΔR j with ΔR i values (C ij ( ΔR i. ΔR j ) ) Correlated residue pair? YES CTC z STEP II STEP III C ij > 0.5? or C ij < -0.5? t-τ t-τ C ij ( τ ) C ji ( τ ) Select only correlated residue pairs C ij varies between -1 and 1 If i and j motions are independent: C ij = 0 If they are positively correlated: 0.5<C ij 1 If they are negatively correlated:-1 C ij <-0.5 Calculate CTC of residues i and j (C ij (τ)) C ij (τ) : Correlations of ΔR i at time t+τ with earlier ΔR i values at time t (C ij ( ΔR i (t), ΔR j (t+τ )) ) CTC vs. TIME- DELAY MAP STEP IV CTC 1 C ij (τ) Draw C TC vs. time-delay map of residues i and j to asses the decay of their correlations by time-delay, τ C ij (τ) > C ji (τ): Residue i at time t drives residue j motions at time t+τ. 0 C ji (τ) time-delay (τ) Asymmetric decay curves? YES RESIDUE i DRIVES RESIDUE j STEP V Determine if decay curves of C ij ( τ ) and C ji ( τ ) are asymmetric & C ij ( τ ) > C ji ( τ ) C ij ( τ ) is greater than C ji ( τ ) for all τ for all τ values? values STEP VI Identify that residue i motions drive residue j motions

18 RESULTS

19 CAUSALITY RELATIONS IN ACTIVE KRAS MOTIONS SII drives SI α3 - Loop 7 drives SI & SII β2 and β3 both drive and follow residue mo7ons

20 SII mo7ons drive SI: 1 C ( τ ) CTC C (τ) 0 τ Red curve shows that fluctuadons of residue 68 (SII) at Dme t drive fluctuadons of residue 29 (SI) at a later Dme t+τ. All correladons are normalized with respect their value at zero.

21 α3 and Loop 7 (L7) mo7ons drive switch regions (SI & SII): 1 CTC 0 C (τ) C (τ) τ 1 CTC 0 C (τ) C (τ) τ FluctuaDons of residues 98 and 102 (α3) at Dme t drive fluctuadons of residues 66 (SII) and 32 (SI) at Dme t+τ, respecdvely.

22 CONCLUSIONS Applied a novel method CTC to WT K-Ras dynamics and identified causality between correlated residue pairs: Switch II drives SI while both are driven by α3-loop 7 Results in good agreement with literature (Kapoor et al, Proteins, 2015; Raimondi et al Plos Computational Biology 2011; Diaz et al Biochemistry 1995) WT K-Ras results serve as a reference point for mutant K-Ras analysis We are currently applying CTC to mutant K-Ras (G12D) & comparing with WT K-Ras. Comparison will identify how causality between residue pairs is changed by mutation Identification of affected residue pairs can help to predict drug target sites of mutant K-Ras We will determine novel drug target sites based on CTC results and discover drug molecules that bind to them by using rational drug design strategies.

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24 Khurana et al & Mark Gerstein, Science, 2013 Our immersive 3D network visualiza7on tool, icave is in press in Gigascience. Freely available at: hdp://research.mssm.edu/gumuslab/sofware.html

25 Frohmer et al, Nature Neuroscience, 2016 Our immersive 3D network visualiza7on tool, icave is in press in Gigascience. Freely available at: hdp://research.mssm.edu/gumuslab/sofware.html

26 Acknowledgements Icahn School of Medicine at Mount Sinai Sezen Vatansever Koc University, Turkey Burak Erman

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33 Gumus Lab Web: hlp://research.mssm.edu/gumuslab/ Postdoctoral Projects: Data VisualizaDon ComputaDonal Genomics Lung Cancer; Colon Cancer Computer ScienDsts; ComputaDonal Genomicists

34 THANK YOU!