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Diblock oligomeric peptide–polymer amphiphiles (PPAs) are biohybrid materials that offer versatile functionality by integrating the sequence-dependent properties of peptides with the synthetic versatility of polymers. Despite their potential as biocompatible materials, the rational design of PPAs for assembly into multichain nanoparticles remains challenging due to the complex intra- and intermolecular interactions emanating from the polymer and peptide segments. To systematically explore the impact of monomer composition on nanoparticle assembly, PPAs were synthesized with a random coil peptide (XTEN2) and oligomeric alkyl acrylates with different side chains: ethyl, tert-butyl, n-butyl, and cyclohexyl. Experimental characterization using electron and atomic force microscopies demonstrated that the tail hydrophobicity impacted accessible morphologies. Moreover, the characterization of different assembly protocols (i.e., bath sonication and thermal annealing) revealed that certain tail compositions provide access to kinetically trapped assemblies. All-atom molecular dynamics simulations of micelle formation unveiled key interactions and differences in core hydration, dictating the PPA assembly behavior. These findings highlight the complexity of PPA assembly dynamics and serve as valuable benchmarks to guide the design of PPAs for a variety of applications, including catalysis, mineralization, targeted sequestration, antimicrobial activity, and cargo transportation. 

Abstract Single‐stranded DNA (ssDNA) plays a pivotal role in both nanotechnology and various biological processes. Many processes and applications can be better understood with enhanced structural characterization of ssDNA; however, the dynamic nature of the molecule makes accurate characterization with atomistic resolution extremely difficult. This study uses a method that integrates experimental small‐angle X‐ray scatter (SAXS) data and molecular modeling data using a genetic algorithm (GA) to predict an all‐atom conformational ensemble of ssDNA. The results of this study also validate the performance of various AMBER force fields and implicit solvent models for ssDNA. Overall, the results are able to determine the most accurate atomistic representation of poly‐Thymine (polyT) in solution to date that closely matches the experimental SAXS observations enabling a better understanding of the behavior of ssDNA in solution. 

Adsorbents featuring high-affinity phosphate-binding proteins (PBPs) have demonstrated highly selective and rapid phosphorus removal and recovery. 

Knowledge graphs (KGs), with their flexible encoding of heterogeneous data, have been increasingly used in a variety of applications. At the same time, domain data are routinely stored in formats such as spreadsheets, text, or figures. Storing such data in KGs can open the door to more complex types of analytics, which might not be supported by the data sources taken in isolation. Giving domain experts the option to use a predefined automated workflow for integrating heterogeneous data from multiple sources into a single unified KG could significantly alleviate their data-integration time and resource burden, while potentially resulting in higher-quality KG data capable of enabling meaningful rule mining and machine learning.In this paper we introduce a domain-agnostic workflow called BUILD-KG for integrating heterogeneous scientific and experimental data from multiple sources into a single unified KG potentially enabling richer analytics. BUILD-KG is broadly applicable, accepting input data in popular structured and unstructured formats. BUILD-KG is also designed to be carried out with end users as humans-in-the-loop, which makes it domain aware. We present the workflow, report on our experiences with applying it to scientific and experimental data in the materials science domain, and provide suggestions for involving domain scientists in BUILD-KG as humans-in-the-loop.