The Role of Configurational Entropy in Miniprotein Stability.

Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through de novo protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al. introduced a data set of more than 16,000 miniproteins spanning four structural topologies with information on stability. These structures were characterized with a set of 46 structural descriptors, with no explicit inclusion of configurational entropy (Scnf). Our work focused on creating a set of 17 descriptors intended to capture variations in Scnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The Scnf descriptors statistically discriminate between stable and unstable distributions within topologies and best describe EEHEE topology stability (where E = ß sheet and H = α helix). Between 50 and 80% of the variation in each Scnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing Scnf features as inputs to machine learning models does not improve overall performance when predicting protein stability, as the R features sufficiently capture the implicit variations.

Machine-Learning-Assisted Synthesis of Polar Racemates.

Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2'-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1̅), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal π bonding between Ti(3d - t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.

Experimental and Computational Assessment of Reactivity and Mechanism in C(sp(3))-N Bond-Forming Reductive Elimination from Palladium(IV).

This report describes a combined experimental and computational investigation of the mechanism of C(sp(3))-N bond-forming reductive elimination from sulfonamide-ligated Pd(IV) complexes. After an initial experimental assessment of reactivity, we used ZStruct, a computational combinatorial reaction finding method, to analyze a large number of multistep mechanisms for this process. This study reveals two facile isomerization pathways connecting the experimentally observed Pd(IV) isomers, along with two competing SN2 pathways for C(sp(3))-N coupling. One of these pathways involves an unanticipated oxygen-nitrogen exchange of the sulfonamide ligand prior to an inner-sphere SN2-type reductive elimination. The calculated ΔG(⧧) values for isomerization and reductive elimination with a series of sulfonamide derivatives are in good agreement with experimental data. Furthermore, the simulations predict relative reaction rates with different sulfonamides, which is successful only after considering competition between the proposed operating mechanisms. Overall, this work shows that the combination of experimental studies and new computational tools can provide fundamental mechanistic insights into complex organometallic reaction pathways.

Mechanism for Forming B,C,N,O Rings from NH3BH3 and CO2 via Reaction Discovery Computations.

This study employs computational reaction finding tools to probe the unique biphilic reactivity between ammonia-borane (AB) and CO2. The results show that sequential reactions involving multiple equivalents of AB and CO2 can lead to the formation of stable nonplanar B,C,N,O-heterocycles (Cy-BCN). Cy-BCN is shown to emerge through boron-oxygen bond formation, hydroboration, dative bond formation, and single- or double-hydrogen transfers. The most kinetically facile reactions (computed at the coupled cluster singles and doubles with perturbative triples (CCSD(T)) level of theory) result from polarized nitrogen-boron double bonds whereas thermodynamic stability results from formation of covalent boron-oxygen bonds. An important structure, HCOOBHNH2 (DHFAB), contains both of these features and is the key intermediate involved in generation of Cy-BCN. Crucially, it is shown that favorable boron-oxygen bond formation results in production of Cy-BCN species that are more stable than polyaminoboranes. These types of reaction intermediates could serve as building blocks in the formation of B,N-codoped graphene oxide (BCN).