Electro‐responsive functional materials can play a critical role in selective metal recovery and recycling due to the need for molecular differentiation between transition metals in complex mixtures. Redox‐active metallopolymers are a promising platform for electrochemical separations, offering versatile structural tuning and fast electron transfer. First, through a judicious selection of polymer structure between a main‐chain metallopolymer (polyferrocenylsilane) and a pendant‐group metallopolymer (polyvinylferrocene), charge‐transfer interactions and binding strength toward competing metal ions are tuned, which as a result, dictate selectivity. For example, almost an order of magnitude increase in separation factor between chromate and meta‐vanadate can be achieved, depending on polymer structure. Second, these metallopolymer electrodes exhibit potential‐dependent selectivity that can even flip ion preference, based solely on electrical means—indicating a control parameter that is orthogonal to structural modifications. Finally, this work presents a framework for evaluating electrochemical separations in multicomponent ion mixtures and elucidates the underlying charge‐transfer mechanisms resulting in molecular selectivity through a combination of spectroscopy and electronic structure calculations. The findings demonstrate the applicability of redox‐metallopolymers in tailored electrochemical separations for environmental remediation, value‐added metal recovery, waste recycling, and even mining processing.
The present work examines an in-situ NMR technique for the copolymerization of methyl methacrylate (MMA) and diallyl dimethylammonium chloride (DADMAC). The copolymer poly(MMA-co-DADMAC) contains monomers of significantly different polarities, making it challenging to measure DADMAC incorporation. The conventional technique for accurate measurement of monomer reactivity ratios in copolymers is time consuming, requiring a large number of experiments, in addition to being challenging for isolation/analysis of formed copolymers from this heterogeneous polymerization. The results show that the reactivity ratios were insensitive to temperatures between 60 and 80 °C, while the reactivity ratios of MMA were found to be much higher than that of DADMAC. The results were comparable to those obtained by the conventional technique. Due to the heterogeneous nature, the measured reactivity ratios are considered as apparent reactivity ratios but important for modifying poly(methyl methacrylate) with DADMAC by copolymerization, enabling design and development of copolymers with desired composition for specific end-use applications.
Understanding of protein conformational dynamics is essential for elucidating molecular origins of protein structure-function relationship. Traditionally, reaction coordinates, i.e., some functions of protein atom positions and velocities have been used to interpret the complex dynamics of proteins obtained from experimental and computational approaches such as molecular dynamics simulations. However, it is nontrivial to identify the reaction coordinates a priori even for small proteins. Here, we evaluate the power of evolutionary couplings (ECs) to capture protein dynamics by exploring their use as reaction coordinates, which can efficiently guide the sampling of a conformational free energy landscape. We have analyzed 10 diverse proteins and shown that a few ECs are sufficient to characterize complex conformational dynamics of proteins involved in folding and conformational change processes. With the rapid strides in sequencing technology, we expect that ECs could help identify reaction coordinates a priori and enhance the sampling of the slow dynamical process associated with protein folding and conformational change.
Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e. spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residueresidue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints, and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts.We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.
Markov State Models (MSMs) are a powerful framework to reproduce the long-time conformational dynamics of biomolecules using a set of short Molecular Dynamics (MD) simulations. However, precise kinetics predictions of MSMs heavily rely on the features selected to describe the system. Despite the importance of feature selection for large system, determining an optimal set of features remains a difficult unsolved problem. Here, we introduce an automatic approach to optimize feature selection based on genetic algorithms (GA), which adaptively evolves the most fitted solution according to natural selection laws. The power of the GA-based method is illustrated on long atomistic folding simulations of four proteins, varying in length from 28 to 80 residues. Due to the diversity of tested proteins, we expect that our method will be extensible to other proteins and drive MSM building to a more objective protocol.Additional Key Words and Phrases: Genetic algorithm, feature selection, markov state model, molecular dynamics simulation, generalized matrix Rayleigh quotient 1 arXiv:1806.09723v1 [q-bio.BM]
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