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1 1 Supporting Information Hydrothermal Decomposition of Amino Acids and Origins of Prebiotic Meteoritic Organic Compounds Fabio Pietrucci 1, José C. Aponte 2,3,*, Richard Starr 2,4, Andrea Pérez-Villa 1, Jamie E. Elsila 2, Jason P. Dworkin 2, A. Marco Saitta 1,* Sorbonne Université, Muséum National d Histoire Naturelle, UMR CNRS 7590, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, F-75005, Paris, France 2 The Goddard Center for Astrobiology and Solar System Exploration Division, Code 691, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, Maryland 20771, United States of America 3 Department of Chemistry, The Catholic University of America, 620 Michigan Ave. NE, Washington, DC 20064, United States of America 4 Physics Department, The Catholic University of America, 620 Michigan Ave. NE, Washington, DC 20064, United States of America * To whom correspondence may be addressed. jose.c.aponte@nasa.gov (tel.: ) or marco.saitta@sorbonne-universite.fr (tel.: )

2 Born-Oppenheimer molecular dynamics (BOMD) Ab initio calculations were based on the Density Functional Theory with the exchange-correlation Perdew-Burke-Ernzerhof functional [1], including Grimme s dispersion corrections [2,3] and the Martins- Troullier pseudopotentials [4] for C, N, O, and H atoms, as implemented in the code CPMD 4.1 [5]. BOMD simulations were performed in the canonical ensemble (NVT) at a temperature of 373 K, with a time step of 0.48 fs, with electronic wavefunction plane-wave cutoff of 70 Ry and convergence threshold of 10-5, in a periodically repeated cubic box of 13.2x13.1x13.8 Å for glycine (247 atoms) and 13.4x13.3x13.8 Å for isovaline (256 atoms), including 1 amino acid and 79 water molecules (density 1.03 g/cm 3 ). The temperature was controlled with a massive Nose-Hoover thermostat [6,7] with a chain length of 4 and with a frequency of 3000 cm Enhanced sampling simulations BOMD simulations were coupled with enhanced sampling techniques [8], according to the following protocol: In the first place, we performed metadynamics simulations [9, 10], employing the path collective variables s and z as the set of reaction coordinates [11]. s indicates the progress along a putative pathway composed by a discrete sequence of atomic configurations, while z represents the distance from the putative pathway. The path collective variables are defined by the following functional form: where D(R, R k ) corresponds to to the distance between the current atomic configuration and the k-th reference configuration: in this work we employed only two references: the reactants and the putative products, thus avoiding to bias the results with hypotheses on the reaction mechanism. The parameter λ is chosen such that λd(r k, R k+1 ) 2.3, conveniently localizing the free energy basins of the reference states around s 1.1 and z 1.9, respectively, and leading to smooth pathways and landscapes. A much larger λ would produce very irregular and discontinuous pathways, while a much smaller one would hamper the resolution of different free energy minima. We adopted a topological metric suited to chemical reactions in solution, introduced in Ref. [12], that tracks changes in chemical bond networks passing from reactants, through intermediates / transition states, until the products: 48

3 Atomic structures are therefore compared based on the difference between the coordination numbers (C IS (t), defined smoothly as in the formula above) of a specific atom I with all atoms of species S. Parameters r 0 SS, n, and m are related to the typical bond distances between two atomic species. We adopted n = 8, m = 14, and r 0 SS = 1.8 and 1.5 Å for X-X and X-H pairs respectively, with X = C, N, O. Reference coordination patterns for reactants and products were obtained as averages from equilibrium BOMD simulations of 10 ps. The patterns are reported in the following tables: Glycine deamination: {C} {O} {N} {H} Ref. 1 C OO C α N Ref. 2 C OO C α N Glycine decarboxylation: {C} {O} {N} {H} Ref. 1 C OO C α N Ref. 2 C OO C α N Isovaline deamination: {C} {O} {N} {H} Ref. 1 C OO C α N Ref. 2 C OO C α N

4 Isovaline decarboxylation: {C} {O} {N} {H} Ref. 1 C OO C α N Ref. 2 C OO C α N Ref. 3 C OO C α N Ref. 4 C OO C α N Ref. 5 C OO C α N Angelic acid decarboxylation: {C} {O} {N} {H} Ref. 1 C OO C α C =Cα N Ref. 2 C OO C α C =Cα N In a first stage, we performed metadynamics simulations [10] for the different reaction channels: a history-dependent bias potential is added to the potential energy of the system, defined as a sum of Gaussian functions of the collective variables. The procedure allows to quickly overcome barriers and explore the relevant free energy landscape. The Gaussian widths for the path CVs were set at σ s = and σ z =0.2, with a height of 3.1 kcal/mol and a deposition interval of 24 fs. The typical duration of each simulation was between 15 and 30 ps. In a second stage, to identify the transition state of each reaction and to get more insight about its mechanism, we carried out a commitor analysis [13]. For each reaction, we extracted a set of up to 20 atomic configurations close to the saddle point in the free energy landscape from reactive metadynamics (and umbrella sampling, see below) trajectories, and we performed 10 independent unbiased BOMD trajectories of length between 0.24 and 0.72 ps, starting from each configuration, verifying whether they fell into reactants or products basins. If the 10 trajectories split between the two basins, additional trajectories were generated until a total of 100. A transition state configuration is

5 identified as committed to both basins with a probability of 50±10%. Note that isovaline deamination (Fig. 3a in the main text) leads to a three-way transition state: we explicitly verified that the corresponding configurations relax to each of the three basins with similar probability. Overall, committor analysis relaxation trajectories are free from bias potentials, hence represent realistic reactive trajectories of the system and were analyzed to understand the reaction mechanisms. In a third stage, a series of umbrella sampling [14] simulations were performed, systematically restraining the s coordinate at different locations along the reaction pathway obtained by commitor analysis (or metadynamics) using a harmonic bias potential in the form 0.5 k (s-s 0 ) 2. For each reaction we adopted between 25 and 60 equally-spaced windows, with force constant set to k = k B T /(Δs/ 2.5 ) 2 with Δs the spacing between windows. We performed for every window a trajectory of duration between 5 and 10 ps. The weighted histogram analysis method (WHAM)[15] finally yielded the free energy profile of the reaction, with a statistical error bar of about ±1 kcal/mol. The error is estimated (in a conservative way) by comparing the profiles obtained using data in different intervals: [1, 10] ps, [1, 5.5] ps, and [5.5, 10] ps. For comparison, the autocorrelation time of the collective variables is less than 0.1 ps. The umbrella sampling simulation of isovaline decarboxylation to sec-butylamine (Fig. 3c in the main text) employed 5 reference configurations instead of only 2 in the definition of the path CV, to improve the resolution of the free energy landscape. The 3 additional intermediate reference configurations were obtained from preliminary metadynamics and umbrella sampling simulations based on 2 reference configurations only. For isovaline deamination an additional series of umbrella sampling simulations were performed along the z coordinate, obtaining through the WHAM the two-dimensional free energy landscape shown in Fig. 3a in the main text. All enhanced sampling simulations employed a modified version of the plugin Plumed 1.3 [16]. A guide to the use of the coordination pattern-based path variables employed in this work, as well as source code and example input files for Plumed 1.3 and Plumed 2.x can be freely downloaded from

6 Table S1: Summary of simulated amino acid decomposition reactions, with their corresponding free energy barriers and reaction free energy differences (values in kcal/mol) Reactant Product(s) Mechanism ΔF ǂ ΔF Glycine (1) Glycolic acid (2) + Ammonia Deamination Glycine (1) Glycolate (3) + Ammonium Deamination Glycine (1) Methylammonium (4) Decarboxylation Carbon dioxide + Hydroxide Isovaline (5) 2,2-Ethyl-methyl-α-acetolactone (6) + Ammonia Deamination Isovaline (5) cis-2-methyl-2-butenoate (7) Deamination cis-2-methyl-2- butenoate (7) + Ammonium Butene (8) + Carbon dioxide + Ammonia Decarboxylation Isovaline (5) Sec-butylamine (9) Decarboxylation

7 Figure S1. Schematic summary of glycine decomposition mechanisms. Note that the arrow convention, representing the displacement of electron pairs, provides a convenient schematization of the mechanisms, however for a precise description of the latter (that are typically concerted and may involve proton transfer through long solvent chains) we refer to the main text, as well as to Fig. 4 and 5 and to the Supplementary Movies

8 Figure S2. Schematic summary of isovaline decomposition mechanisms. The same remarks apply as for Fig. S

9 Movie S1. Deamination of (1), formation of (2) and ammonia. Movie S2. Decarboxylation of (1), formation of (4), ammonia and hydroxide. Movie S3. Deamination of (5), formation of (6) and ammonia. Movie S4. Deamination of (5), formation of (7). Movie S5. Decarboxylation of (5), formation of (9). Movie S6. Decarboxylation of (7), formation of (8), ammonia and carbon dioxide.

10 References [1] J. P. Perdew, K. Burke, and M. Ernzerhof. Generalized gradient approximation made simple. Phys. Rev. Lett., 77: , Oct [2] S. Grimme. Semiempirical gga-type density functional constructed with a long-range dispersion correction. J. Comput. Chem., 27(15): , [3] V. Barone, M. Casarin, D. Forrer, M. Pavone, M. Sambi, and A. Vittadini. Role and effective treatment of dispersive forces in materials: Polyethylene and graphite crystals as test cases. J. Comput. Chem., 30(6): , [4] N. Troullier and J. L. Martins. Efficient pseudopotentials for plane-wave calculations. Phys. Rev. B, 43: , Jan [5] CPMD Copyright IBM Corp. and by Max Planck Institute Stuttgart [6] S. Nosé. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys., 81(1): , [7] W. G. Hoover. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A, 31: , Mar [8] F. Pietrucci. Strategies for the exploration of free energy landscapes: unity in diversity and challenges ahead. Rev. Phys., 2:32 45, [9] A. Laio and M. Parrinello. Escaping free-energy minima. Proc. Natl. Acad. Sci. U.S.A., 99(20): , [10] A. Laio and F. L. Gervasio. Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys., 71(12):126601, [11] D. Branduardi, F. L. Gervasio, and M. Parrinello. From a to b in free energy space. J. Chem. Phys.,126(5):054103, [12] F. Pietrucci and A. M. Saitta. Formamide reaction network in gas phase and solution via a unified theoretical approach: Toward a reconciliation of different prebiotic scenarios. Proc. Natl. Acad. Sci. U.S.A., 112(49): , [13] P. G. Bolhuis, D. Chandler, C. Dellago, and P. L. Geissler. Transition path sampling: Throwing ropes over rough mountain passes, in the dark. Annu. Rev. Phys. Chem., 53(1): , [14] G.M. Torrie and J.P. Valleau. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J. Comput. Phys., 23(2): , 1977.

11 [15] S. Kumar, J. M. Rosenberg, D. Bouzida, R. H. Swendsen, and P. A. Kollman. The weighted histogram analysis method for free-energy calculations on biomolecules. i. the method. J. Comput. Chem., 13(8): , [16] M. Bonomi, D. Branduardi, G. Bussi, C. Camilloni, D. Provasi, P. Raiteri, D. Donadio, F. Marinelli, F. Pietrucci, R. A. Broglia, and M. Parrinello. Plumed: A portable plugin for free-energy calculations with molecular dynamics. Comput. Phys. Commun., 180(10): , 2009.

Supporting Information Synthesis of (D)-erythrose from glycolaldehyde aqueous solutions under electric field

Electronic Supplementary Material (ESI) for ChemComm. This journal is The Royal Society of Chemistry 2018 Supporting Information Synthesis of (D)-erythrose from glycolaldehyde aqueous solutions under electric