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1. On the Search of a Silver Bullet for the Preparation of Bioinspired Molecular Electrets with Propensity to Transfer Holes at High Potentials

   https://doi.org/10.3390/biom11030429  

   Derr, James Bennett; Rybicka-Jasińska, Katarzyna; Espinoza, Eli Misael; Morales, Maryann; Billones, Mimi Karen; Clark, John Anthony; Vullev, Valentine Ivanov (March 2021, Biomolecules)

   null (Ed.)

   Biological structure-function relationships offer incomparable paradigms for charge-transfer (CT) science and its implementation in solar-energy engineering, organic electronics, and photonics. Electrets are systems with co-directionally oriented electric dopes with immense importance for CT science, and bioinspired molecular electrets are polyamides of anthranilic-acid derivatives with designs originating from natural biomolecular motifs. This publication focuses on the synthesis of molecular electrets with ether substituents. As important as ether electret residues are for transferring holes under relatively high potentials, the synthesis of their precursors presents formidable challenges. Each residue in the molecular electrets is introduced as its 2-nitrobenzoic acid (NBA) derivative. Hence, robust and scalable synthesis of ether derivatives of NBA is essential for making such hole-transfer molecular electrets. Purdie-Irvine alkylation, using silver oxide, produces with 90% yield the esters of the NBA building block for iso-butyl ether electrets. It warrants additional ester hydrolysis for obtaining the desired NBA precursor. Conversely, Williamson etherification selectively produces the same free-acid ether derivative in one-pot reaction, but a 40% yield. The high yields of Purdie-Irvine alkylation and the selectivity of the Williamson etherification provide important guidelines for synthesizing building blocks for bioinspired molecular electrets and a wide range of other complex ether conjugates. 

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2. A review of mathematical representations of biomolecular data

   https://doi.org/10.1039/c9cp06554g  

   Nguyen, Duc Duy; Cang, Zixuan; Wei, Guo-Wei (February 2020, Physical Chemistry Chemical Physics)

   Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on protein–ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, etc. 

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3. Integration of element specific persistent homology and machine learning for protein‐ligand binding affinity prediction

   https://doi.org/10.1002/cnm.2914  

   Cang, Zixuan; Wei, Guo‐Wei (August 2017, International Journal for Numerical Methods in Biomedical Engineering)

   Abstract Protein‐ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein‐ligand binding affinities is vital to rational drug design and the understanding of protein‐ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology‐based machine‐learning paradigm outperforms other existing methods in protein‐ligand binding affinity predictions. ESPH reveals protein‐ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein‐ligand hydrophobic interactions are extended to 40Å  away from the binding site, which has a significant ramification to drug and protein design. 

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4. Spin dynamics of [1,2- ¹³ C ₂ ]pyruvate hyperpolarization by parahydrogen in reversible exchange at micro Tesla fields

   https://doi.org/10.1039/d3cp00843f  

   Browning, Austin; Macculloch, Keilian; TomHon, Patrick; Mandzhieva, Iuliia; Chekmenev, Eduard Y.; Goodson, Boyd M.; Lehmkuhl, Sören; Theis, Thomas (June 2023, Physical Chemistry Chemical Physics)

   Hyperpolarization of 13 C-pyruvate via Signal Amplificaton By Reversibble Exchange (SABRE) is an important recent discovery because of both the relative simplicity of hyperpolarization and the central biological relevance of pyruvate as a biomolecular probe for in vitro or in vivo studies. Here, we analyze the [1,2- 13 C 2 ]pyruvate-SABRE spin system and its field dependence theoretically and experimentally. We provide first-principles analysis of the governing 4-spin dihydride- 13 C 2 Hamiltonian and numerical spin dynamics simulations of the 7-spin dihydride- 13 C 2 –CH 3 system. The analytical and the numerical results are compared to matching systematic experiments. With these methods we unravel the observed spin state mixing of singlet states and triplet states at microTesla fields and we also analyze the dynamics during transfer from micro-Tesla field to high field for detection to understand the resulting spectra from the [1,2- 13 C 2 ]pyruvate-SABRE system. 

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5. Dynamic mass spectrometry probe for electrospray ionization mass spectrometry monitoring of bioreactors for therapeutic cell manufacturing

   https://doi.org/10.1002/bit.26832  

   Chilmonczyk, Mason_A; Kottke, Peter_A; Stevens, Hazel_Y; Guldberg, Robert_E; Fedorov, Andrei_G (November 2018, Biotechnology and Bioengineering)

   Abstract Large‐scale manufacturing of therapeutic cells requires bioreactor technologies with online feedback control enabled by monitoring of secreted biomolecular critical quality attributes (CQAs). Electrospray ionization mass spectrometry (ESI‐MS) is a highly sensitive label‐free method to detect and identify biomolecules, but requires extensive sample preparation before analysis, making online application of ESI‐MS challenging. We present a microfabricated, monolithically integrated device capable of continuous sample collection, treatment, and direct infusion for ESI‐MS detection of biomolecules in high‐salt solutions. The dynamic mass spectrometry probe (DMSP) uses a microfluidic mass exchanger to rapidly condition samples for online MS analysis by removing interfering salts, while concurrently introducing MS signal enhancers to the sample for sensitive biomolecular detection. Exploiting this active conditioning capability increases MS signal intensity and signal‐to‐noise ratio. As a result, sensitivity for low‐concentration biomolecules is significantly improved, and multiple proteins can be detected from chemically complex samples. Thus, the DMSP has significant potential to serve as an enabling portion of a novel analytical tool for discovery and monitoring of CQAs relevant to therapeutic cell manufacturing. 

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