Brenda M. Rubenstein

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

Doped lead halide perovskite nanocrystals (NCs) have garnered significant attention due to their superior optoelectronic properties. Here, we report a synthesis of Cd-doped CsPbCl3 NCs by decoupling Pb- and Cl-precursors in a hot injection method. The resulting Cd-doped perovskite NCs manifest a dual-wavelength emission profile with the first reported example of Cd-dopant emission. By controlling Cd-dopant concentration, the emission profile can be tuned with a dopant emission quantum yield of up to 8%. A new secondary emission (∼610 nm) is induced by an energy transfer process from photoexcited hosts to Cd-dopants and a subsequent electronic transition from the excited state (3Eg) to the ground state (1A1g) of [CdCl6]4– units. This electronic transition matches well with a first-principles density functional theory calculation. Further, the optical behavior of Cd-doped CsPbCl3 NCs can be altered through postsynthetic anion-exchange reactions. Our studies present a new model system for doping chemistry studies in semiconductors for various optoelectronic applications.

Formamidinium lead halide (FAPbX3, X = Cl, Br, I) perovskite materials have recently drawn an increased amount of attention owing to their superior optoelectronic properties and enhanced material stability as compared with their methylammonium-based (MA-based) analogues. Herein, we report a study of the pressure-induced structural and optical evolutions of FAPbI3 hybrid organic–inorganic perovskite nanocrystals (NCs) using a synchrotron-based X-ray scattering technique coupled to in situ absorption and photoluminescence spectroscopies. As a result of their unique structural stability and soft nature, FAPbI3 NCs exhibit a wide range of band-gap tunability (1.44–2.17 eV) as a function of pressure (0–13.4 GPa). The study presented here not only provides an efficient and chemically orthogonal means to controllably engineer the band gap of FAPbI3 NCs using pressure but more importantly sheds light on how to strategically design the band gaps of FA-based hybrid organic–inorganic perovskites for various optoelectronic applications.

Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital data is written using robotically synthesized libraries of Ugi products, and the files are read back using mass spectrometry. We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for single reads, without library purification. In addition to improved scaling of non-biological molecular data storage, these demonstrations offer an information-centric perspective on the high-throughput synthesis and screening of small-molecule libraries.