Insights into the amazing world of RNA catalysis from hybrid QM/MM simulations

Darrin M. York

http://theory.rutgers.edu RNA catalysis is important from a fundamental biological and evolutionary perspective

1989 Nobel Prize in Chemistry:

Thomas R. Cech and Sidney Altman for their "discovery of catalytic properties of RNA”.

The 2009 Nobel Prize in Chemistry:

Venkatraman Ramakrishnan, Thomas A. Steitz and Ada E. Yonath for “studies of the structure and function of the ribosome”. RNA catalysis is chemically interesting!

Unlike protein enzymes, the (4.2) repertoire of building blocks and chemical functional groups available for catalysis in RNA is (9.2) limited:

• 4 relatively inert nucleobases • Stable sugar-phosphate backbone (3.5) • Solution pKa values shifted relatively far from neutrality • Each phosphate carries -1 charge (no other charged (9.5) groups at neutral pH)

Fundamental question as to how RNA molecules can adopt 3D structures that confer enzyme activity at all. RNA catalysis is chemically interesting!

Naturally Occurring Ribozymes

 Site-specific phosphoryl transfer (O2’-transphosphorylation) - Hammerhead, HDV, Hairpin, VS, glmS, Twister, TS, pistol, RNase P…  Site-specific intermolecular phosphoryl transfer (exogenous O3’ attack) - Group I and II intron  Peptidyl/Amino-acyl transfer - Ribosome (RNA active site core)

In vitro evolved/synthetic ribozymes

 Diels-Alder cycloaddition  Michael addition  Aldol condensation  RNA polymerization - class I RNA ligase (e.g., L1 ligase) RNA catalysis has had impact on biotechnology

• Biotechnology Tools − gene expression − medical diagnostics − biosensors − drug discovery Ribozymes are less efficient than protein enzymes – but that might just be their biggest advantage…

• Whereas the overall rate enhancement for the fastest protein enzymes exceed that of RNA enzymes, there is growing precedent that the intrinsic catalytic rate may be more similar

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• Formation of a catalytically active state in RNA is often an improbable event that is highly sensitive control factors (conformational state, metal ion/cofactor binding, pH, etc…) – although this limits efficiency, it is a tremendous design advantage!

Key Concepts

Garcia-Viloca et al. Science, 303, 184 (2004) Why is free energy and sampling important in the study of RNA catalysis? • To characterize the catalytically active state in solution

• To explore the free energy landscape of the chemical reaction

• To aid in the interpretation of experiments and make predictions

• The goal is not to (just) predict rate or even pathway – it is to gain a predictive understanding of mechanism in order to guide design Complexity of the “Problem Space” for RNA Catalysis

RNA catalysis departs from a active state defined by:

1) Conformation (ζ∗) 2) Metal ion binding mode (M*) 3) Protonation state (p*)

These elements are intimately coupled, creating a highly complex “problem space” that must be addressed prior to any investigation of the chemical steps of the catalytic mechanism. Typically the only part that QM/MM studies address Computational RNA Enzymology What is a quantum mechanical force field (QMFF)?

• Quantum mechanical force fields (QMFFs) are a specific class of linear-scaling quantum mechanical (LSQM) methods that replace elements of theoretical rigor by introducing parametrically fitted functions to achieve higher accuracy and computational.

• QMFFs use a fragmentation approach and empirical density- functional interactions that avoid explicit inter-fragment orbital coupling.

• QMFFs leverage the benefits provided by the LSQM and QM/MM approaches to produce a fully QM method that is able to simultaneously achieve very high accuracy and efficiency.

Acc. Chem. Res. (2014) 47, 2812–2820. J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). J. Phys. Condens. Matter (Topical Review) 29, 383002 (2017). QMFF Framework - Algorithm Differences

Acc. Chem. Res. (2014) 47, 2812–2820. J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). J. Phys. Condens. Matter (Topical Review) 29, 383002 (2017). Electrostatic Potential Difference Maps (Reference: B3LYP/6-311++G**)

DFTB3 Electrostatic Potential Difference Maps (Reference: B3LYP/6-311++G**)

mDC

DFTB3 Unlike DFTB3 mDC uses multipoles for improved electrostatics and hydrogen bonding.

Errors in non-bonded interactions are better than DFTB3 or dispersion-corrected DFT functionals tested, and give excellent results in condensed phase simulations so far… Next-generation QMFFs

Protein-protein interactions, simulations in the condensed phase (solution and crystals). Next-generation QMFFs

Acc. Chem. Res. (2014) 47, 2812–2820. J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). Next-generation QMFFs

J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). Next-generation QMFFs

J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). Next-generation QMFFs

Reference data:

J. Chem. Theory Comput. 5, 1016 (2009).

Acc. Chem. Res. 47, 2812 (2014).

J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). DFTB3: SCF procedure with 60X speed up (1 sec = 1 min) mDC: SCF procedure in real time PROBLEM: QMFF & QM/MM Ewald sums that are stable with minimal basis sets blow up periodically with medium to large basis sets (e.g., 6-31G*). Total disaster. Sad!

QM/MM Electrostatics: Background

Ambient-Potential Composite Ewald (cEw) method

Giese and York, J. Chem. Theory Comput. 12, 2611 (2016)

Example mDC simulation of dimethyl glycine crystals (18-unit supercell, 288 residues, 4608 atoms, 10 ns)

Framework is general for other QM methods (or mixed methods).

Moving toward applications to kinases/phosphatases (including membranes), spectroscopy (IR, Raman, NMR) and new tools to facilitate drug discovery and process pipelines.

Giese et al., J. Chem. Theory Comput. 11, 436 (2015); 11, 451 (2015). pmemdGTI: a GPU-accelerated free energy module in AMBER TI results are the L51a to L51bt mutation in Factor Xa in explicit solvent (41,563 total atoms) in the NVT ensemble using a 1 fs time step and PME electrostatics with the whole ligand defined as the TI region.

T.-S. Lee et al. J Chem. Theory Comput. 13, 3077 (2017). Cleavage Transesterification Catalyzed by Twister Ribozyme

• small nucleolytic ribozyme • What is the catalytically active state • mutational studies suggest that G33 in solution? and A1 may play the roles of a • What residues act as general acid general base and acid, respectively and base? • N3 position rather than N1 of A1 • How does the active site provide implicated in acid step electrostatic stabilization?

:B

AH+ Crystallographic Structures Represent Inactive States

Comparison of Crystal Structures - Active Site

4OJI 4QJH 4RGE 5DUN

Liu, Y. et. al. Nat. Eiler, DR. et. al. Proc. Ren, A. et. al. Nat. Kosutic, M. et. al. Chem. Biol., 2014, Natl. Acad. Sci., 2014, Commun., 2014, v5, Angew. Chem. Int. Ed. v10, p739. v111, p13028. p5534. Engl., 2015, v54, p15128.

• Current crystallographic structures represent static pictures of inactive states of the ribozyme in a crystal, obfuscating their functional interpretation. • There is need for a structural (and dynamical) model of the active states in solution that is consistent with the current body of experimental data. Crystal Simulation Captures both Structure and Dynamics

● Average MD1 and crystal2 monomers are almost identical (0.8 Å heavy-atom RMSD) ● MD fluctuations agree closely with experimental B-factors (R=0.90) ● MD explains small fluctuations of U-1 (re. 6) and disordered of residues 17-18.

1. Gaines CS and York DM J. Am. Chem. Soc., 2016, v138, p 3058. 2. Liu, Y. et. al. Nat. Chem. Biol., 2014, v10, p739.

Crystal Structure is in an Inactive State

● 4OJI lacks the 2’OH nucleophile and has U-1 extruded from the active site. ● MD simulations suggest U-1 is trapped in an INACTIVE state by a crystal packing contact that leads to disruption of the scissile phosphate center.

● Nucleophile-phosphate distance (Dinl) and inline attack angle (θinl) have poor

inline fitness (Dinl > 3.5 Å and θinl ~90 ).

Gaines CS and York DM J. Am. Chem. Soc., 2016, v138, p 3058. Solution Simulations Reveal Active Dynamical Picture

U-1 Conformational States in Solution

Without the crystal packing contact, U-1 can rotate from an “extruded” state to be “stacked” under G33 or form a base “triple” with A34 and A19.

The “stacked” and “triple” states represent plausible ACTIVE states in solution. Stacking State Impacts In-line Fitness and Key Hydrogen Bonds Transition State Mimic Simulations Give Insight into General Acid Catalysis

Stacked TS mimic ● “Stacked” and “triple” states preserve hydrogen bond scaffold

● Interactions involving A1:N3, G33, and the scissile phosphate support roles as

general acid and base Triple TS mimic ● Hydrogen bond between G33:N2 and non-bridging phosphoryl oxygen is consistent thio substitution data

Free Energy Simulations provide Insight into the Origin of pKa Shifts of Nucleobase Residues

Bevilacqua, Biochemistry, 42, 2259 (2003) Dissanyake, Biochemistry, 54, 1307 (2015)

Gaines CS, York DM J. Am. Chem. Soc., 2016, v138, p 3058. Intrinsic Rate of Reaction

PBE0/6-31G*, 70 QM atoms, ~44,000 MM atoms, 2.8 ps/day (30s/MD step) on 96 cores ~ 5 ns of cumulative sampling for a full 2D profile

General Base - Phosphoryl Transfer Intrinsic Rate of Reaction

PBE0/6-31G*, 70 QM atoms, ~44,000 MM atoms, 2.8 ps/day (30s/MD step) on 96 cores ~ 5 ns of cumulative sampling for a full 2D profile

Phosphoryl Transfer – General Acid Kinetic Isotope Effects Survey: identification of transition states from kinetic isotope effects

RNase A active site model

2.31

2.29

Angew. Chem. Int. Ed. 51, 647 (2012) PNAS 110, 13002 (2013) Curr. Opin. Chem. Biol. 21C, 96 (2014) Verification of the Transition State by KIE Predictions

Non-enz: O2’: 0.997 O5’: 1.034

RNase A: O2’: 0.994 O5’: 1.014

Twister*: O2’: 0.997 O5’: 1.004

Role of Adenine:

• Embedded in an electronegative pocket that shifts pKa values. • Act as a general acid: − at the N1 position (hairpin & VS) − at the N3 position (twister) – a feature thus far novel to the twister ribozyme. Common L-platform and L-anchor architecture Hammerhead Ribozyme Revisited: Cooperative divalent metal binding/general base activation

• A divalent metal ion was found to bind to G12 at high pH

• Might play the role to down-shift the pKa of G12-H1 in order to activate the general base and nucleophile deprotonation

• Experimental pKa of base in hammerhead: 8.0, so, a pKa shift of -1.2 of G12 is the most straight forward interpretation

Mir et al. (Golden lab), Biochemistry, 54, 6369 (2015); Biochemistry 55, 633 (2016).

Chen et al., Biochemistry, 56, 2985 (2017). G-site Mg2+ Gets the pKa Right

pKa shift System 2+ (TI) • With the G-site Mg , pKa of G12 in HHR is right on Guanine (ref.) 0.0 the spot HHR:G12 3.7 ± 0.2 HHR.Mg2+:G12 -1.2 ± 0.4 • Without the G-site Mg2+, -1.2 pKa of G12 in HHR is HHR Experiment higher than guanine, agrees with previous prediction that HHR has a negatively charged active site (Lee et al., J. Mol. Biol., 2009)

• Mg2+ shift the pKa of G12 down by 5 pKa units, similar as the QM results

Chen Chenet al., etBiochemistry al., Biochemistry, 56, 2985, submitted (2017) . Overall Catalytic Effect of G-site Mg2+

• G-site Mg2+ increases the probability of G12 being deprotonated, but also makes G12 a worse proton acceptor for the subsequent general base proton transfer (GBPT) step

• Free energy profile of the GBPT step is predicted using ab initio QM/MM umbrella sampling

• Overall, G-site Mg2+ has a catalytic effect of 5.6 kcal/mol

System ΔGDEPR ΔGGBPT ΔGTOTAL HHR:G12 6.7 ± 0.6 6.3 ± 0.2 13.0 ± 0.6 HHR•Mg2+:G12 0.0 ± 0.0 7.4 ± 0.2 7.4 ± 0.2 Role of Guanine: • Act as a general base • Increase the acidity of the 2’OH nucleophile (similar to Lys41 in RNase A) • Prolong the lifetime of the activated precursor

pKa > 9.2 H+ Role of Guanine: • Act as a general base • Increase the acidity of the 2’OH nucleophile (similar to Lys41 in RNase A) • Prolong the lifetime of the activated precursor • Activated by metal ion interactions at N7/O6 pKa ~8 H+ Role of Ions:

• Do not appear to play a direct role in catalysis in twister (also HPR, VS), but do in some other ribozymes (HHR, HDV, TS, PR). • Important for electrostatic stabilization, and formation of inline conformations (predicted for HHR).

Lee et al., J. Mol. Biol. 388, 195 (2009);Prog. Mol. Biol. Trans. Sci. 120, 25 (2014)

We aim to advance the forefront of biomolecular simulation research through innovative, high-throughput multiscale methods that enable discovery. http:/ / LBSR.rutgers.edu Acknowledgements

• Haoyuan Chen • Dr. Timothy Wilson • Prof. William Scott (UCSC) • Colin Gaines • Dr. Tim Giese • Prof. Michael Harris (Case) • Ken Kostenbader • Dr. Tai-Sung Lee • Prof. Joseph Piccirilli (U. Chicago) • Emily Buginsky • Dr. Ming Huang • Prof. Joe Wedekind (U. Rochester) • Dr. George Giambasu • Prof. David Lilley (U. Dundee) • Prof. Dan Herschlag (Stanford) • Prof. David Case (Rutgers) • Prof. Shantenu Jha (Rutgers) • Prof. Phil Bevilacqua (Penn. State) • Prof. Barbara Golden (Purdue)

Funding/Resources: • NIH, NSF, IBM, Merck • XSEDE, Blue Waters sustained- petascale computing project Key Advances in Multiscale Modeling Tools

• Multiscale quantum models; e.g., ab initio quantum mechanical/molecular mechanical methods with rigorous treatment of long-range electrostatics

J Chem. Theory Comput. 11, 436 (2015); 12, 2611 (2016);

• Enhanced sampling and analysis methods for characterization of free energy landscapes

J Chem. Theory Comput., 9, 153 (2013); 10, 24 (2014); 11, 373 (2015); 13, 3077 (2017) (GPU-accelerated TI method in AMBER)

• New molecular simulation methods that integrate experimental conditions and observables; e.g., constant pH, ionic strength, experimental restraints

Biochemistry 54, 1307 (2015); Acta Crystallogr. D Struct Biol. 72, 1062 (2016); J. Comput. Aided Mol. Des. 30, 533 (2016)

• Fully quantum mechanical force fields

Acc. Chem. Res. 47, 2812 (2014); J Chem. Theory Comput. 10, 1086 (2014); 11, 451 (2015); J. Cond. Mat. Phys. 29, 383002 (2017).