Faraday Discussions最新文献

{"title":"Ion concentration polarization causes a nearly pore-length-independent conductance of nanopores†","authors":"DaVante Cain, Ethan Cao, Ivan Vlassiouk, Tilman E. Schäffer and Zuzanna S. Siwy","doi":"10.1039/D4FD00148F","DOIUrl":"10.1039/D4FD00148F","url":null,"abstract":"<p >There has been a great amount of interest in nanopores as the basis for sensors and templates for preparation of biomimetic channels as well as model systems to understand transport properties at the nanoscale. The presence of surface charges on the pore walls has been shown to induce ion selectivity as well as enhance ionic conductance compared to uncharged pores. Here, using three-dimensional continuum modeling, we examine the role of the length of charged nanopores as well as applied voltage for controlling ion selectivity and ionic conductance of single nanopores and small nanopore arrays. First, we present conditions where the ion current and ion selectivity of nanopores with homogeneous surface charges remain unchanged, even if the pore length decreases by a factor of 6. This length-independent conductance is explained through the effect of ion concentration polarization (ICP), which modifies local ionic concentrations, not only at the pore entrances but also in the pore in a voltage-dependent manner. We describe how voltage controls the ion selectivity of nanopores with different lengths and present the conditions when charged nanopores conduct less current than uncharged pores of the same geometrical characteristics. The manuscript provides different measures of the extent of the depletion zone induced by ICP in single pores and nanopore arrays, including systems with ionic diodes. The modeling shown here will help design selective nanopores for a variety of applications where single nanopores and nanopore arrays are used.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"257 ","pages":" 344-359"},"PeriodicalIF":3.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scattering of larger molecules – part 1: general discussion","authors":"Dmitri Babikov, Nadia Balucani, Astrid Bergeat, Mark Brouard, David W. Chandler, Matthew L. Costen, Michal Fárník, Hua Guo, Tibor Győri, Dwayne Heard, David Heathcote, Nils Hertl, Pablo G. Jambrina, Nathanael M. Kidwell, O. A. Krohn, Viet Le Duc, Jérôme Loreau, Stuart R. Mackenzie, Max McCrea, Kenneth G. McKendrick, Jennifer Meyer, Daniel R. Moon, Amy S. Mullin, Gilbert S. Nathanson, Daniel M. Neumark, Kang-Kuen Ni, Martin J. Paterson, Eva Pluhařová, Patrick Robertson, Christopher Reilly, George C. Schatz, Chris Sparling, Arthur G. Suits, Peter D. Watson, Roland Wester, Stefan Willitsch and Alec M. Wodtke","doi":"10.1039/D4FD90019G","DOIUrl":"10.1039/D4FD90019G","url":null,"abstract":"","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"251 ","pages":" 313-341"},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond theory-driven discovery: introducing hot random search and datum-derived structures","authors":"Chris J. Pickard","doi":"10.1039/D4FD00134F","DOIUrl":"10.1039/D4FD00134F","url":null,"abstract":"<p >Data-driven methods have transformed the prospects of the computational chemical sciences, with machine-learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory-driven, as opposed to data-driven, discovery based on <em>ab initio</em> random structure searching (AIRSS), and then introduce two new methods that exploit machine-learning acceleration. I show how long high-throughput anneals, between direct structural relaxation, enabled by ephemeral data-derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low-energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low-energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg<small>₃</small>Al<small>₂</small>(SiO<small>₄</small>)<small>₃</small>, garnet structure is recovered from a low-energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion-model-based generative methods is discussed.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"256 ","pages":" 61-84"},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/fd/d4fd00134f?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re-evaluating retrosynthesis algorithms with Syntheseus†","authors":"Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl and Marwin H. S. Segler","doi":"10.1039/D4FD00093E","DOIUrl":"10.1039/D4FD00093E","url":null,"abstract":"<p >Automated synthesis planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques, and unnecessarily hamper progress. To remedy this, we present a synthesis planning library with an extensive benchmarking framework, called SYNTHESEUS, which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step synthesis planning algorithms. We demonstrate the capabilities of SYNTHESEUS by re-evaluating several previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes in controlled evaluation experiments. We end with guidance for future works in this area, and call on the community to engage in the discussion on how to improve benchmarks for synthesis planning.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"256 ","pages":" 568-586"},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling ligand exchange in metal complexes with machine learning potentials†","authors":"Veronika Juraskova, Gers Tusha, Hanwen Zhang, Lars V. Schäfer and Fernanda Duarte","doi":"10.1039/D4FD00140K","DOIUrl":"10.1039/D4FD00140K","url":null,"abstract":"<p >Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and <em>ab initio</em> methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal–ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg<small>²⁺</small> in water and Pd<small>²⁺</small> in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"256 ","pages":" 156-176"},"PeriodicalIF":3.4,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/fd/d4fd00140k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning†","authors":"Arya Changiarath, Aayush Arya, Vasileios A. Xenidis, Jan Padeken and Lukas S. Stelzl","doi":"10.1039/D4FD00099D","DOIUrl":"10.1039/D4FD00099D","url":null,"abstract":"<p >Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an “active learning” scheme introduced by Yang <em>et al.</em> (<em>bioRxiv</em>, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network to establish sequence–property relationships for disordered proteins and their self-interactions and their interactions in phase-separated condensates. We show how iterative training with second virial coefficients derived from the simulations of disordered protein sequences leads to a rapid improvement in predicting peptide self-interactions. We employ this Bayesian approach to efficiently search for new sequences that bind to condensates of the disordered C-terminal domain (CTD) of RNA Polymerase II, by simulating molecular recognition of peptides to phase-separated condensates in coarse-grained molecular dynamics. By searching for protein sequences which prefer to self-interact rather than interact with another protein sequence we are able to shape the morphology of protein condensates and design multiphasic protein condensates.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"256 ","pages":" 235-254"},"PeriodicalIF":3.4,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/fd/d4fd00099d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular sandwich-based DNAzyme catalytic reaction towards transducing efficient nanopore electrical detection of antigen proteins†","authors":"Lebing Wang, Shuo Zhou, Yunjiao Wang, Yan Wang, Jing Li, Xiaohan Chen, Daming Zhou, Liyuan Liang, Bohua Yin, Youwen Zhang and Liang Wang","doi":"10.1039/D4FD00146J","DOIUrl":"10.1039/D4FD00146J","url":null,"abstract":"<p >Despite significant advances in nanopore nucleic acid sequencing and sensing, protein detection remains challenging due to the inherent complexity of protein molecular properties (<em>i.e.</em>, net charges, polarity, molecular conformation &amp; dimension) and sophisticated environmental parameters (<em>i.e.</em>, biofluids), resulting in unsatisfactory electrical signal resolution for protein detection such as poor accessibility, selectivity and sensitivity. The selection of an appropriate electroanalytical approach is strongly desired which should be capable of offering easily detectable and readable signals regarding proteins particularly depending on the practical application. Herein, a molecular sandwich-based cooperative DNAzyme catalytic reaction nanopore detecting approach was designed. Specifically, this approach uses Mg<small>²⁺</small> catalyzed DNAzyme (10–23) toward nucleic acids digestion for efficient antigen protein examination. The proposed strategy operates by initial formation of a molecular sandwich containing capture antibody–antigen–detection antibody for efficient entrapment of target proteins (herein taking the HIV p24 antigen for example) and immobilization on magnetic beads surfaces. After that, the DNAzyme was linked to the detection antibody <em>via</em> a biotin–streptavidin interaction. In the presence of Mg<small>²⁺</small>, the DNAzyme catalytic reaction was triggered to digest nucleic acid substrates and release unique cleavage fragments as reporters capable of transducing more easily detectable nucleic acids as a substitute for the complicated and hard to yield protein signals, in a nanopore. Notably, experimental validation confirms the detecting stability and sensitivity for the target antigen referenced with other antigen proteins, meanwhile it demonstrates a detection efficacy in a human serum environment at very low concentration (LoD ∼1.24 pM). This cooperative DNAzyme nanopore electroanalytical approach denotes an advance in protein examination, and may benefit <em>in vitro</em> testing of proteinic biomarkers for disease diagnosis and prognosis assessment.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"257 ","pages":" 60-72"},"PeriodicalIF":3.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemiluminescence microscopy for the investigation of peptide interactions within planar lipid membranes†","authors":"Kaoru Hiramoto, Kosuke Ino, Ibuki Takahashi, Ayumi Hirano-Iwata and Hitoshi Shiku","doi":"10.1039/D4FD00137K","DOIUrl":"10.1039/D4FD00137K","url":null,"abstract":"<p >Understanding the interactions between lipid membranes and peptides is crucial for controlling bacterial and viral infections, and developing effective drugs. In this study, we proposed the use of electrochemiluminescence (ECL) microscopy in a solution of [Ru(bpy)<small>₃</small>]<small>²⁺</small> and tri-<em>n</em>-propylamine to monitor alterations in the lipid membranes due to peptide action. A planar artificial lipid membrane served as a model platform, and its surface was observed using ECL microscopy during exposure to melittin, a representative membrane lytic peptide. Upon exposure to melittin, the light-emitting process of the [Ru(bpy)<small>₃</small>]<small>²⁺</small>/tri-<em>n</em>-propylamine system through the lipid membrane exhibited complex changes, suggesting that stepwise peptide actions can be monitored through the system. Furthermore, wide-field imaging with ECL microscopy provided an effective means of elucidating the membrane surface at the submicron level and revealing heterogeneous changes upon exposure to melittin. This complemented the spatiotemporal information that could not be obtained using conventional electrochemical measurements.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"257 ","pages":" 137-150"},"PeriodicalIF":3.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Restoring translational symmetry in periodic all-orbital dynamical mean-field theory simulations","authors":"Jiachen Li and Tianyu Zhu","doi":"10.1039/D4FD00068D","DOIUrl":"10.1039/D4FD00068D","url":null,"abstract":"<p >Dynamical mean-field theory (DMFT) and its cluster extensions provide an efficient Green’s function formalism to simulate spectral properties of periodic systems at the quantum many-body level. However, traditional cluster DMFT breaks translational invariance in solid-state materials, and the best strategy to capture non-local correlation effects within cluster DMFT remains elusive. In this work, we investigate the use of overlapping atom-centered impurity fragments in recently-developed <em>ab initio</em> all-orbital DMFT, where all local orbitals within the impurity are treated with high-level quantum chemistry impurity solvers. We demonstrate how the translational symmetry of the lattice self-energy can be restored by designing symmetry-adapted embedding problems, which results in an improved description of spectral functions in two-dimensional boron nitride monolayers and graphene at the levels of many-body perturbation theory (GW) and coupled-cluster theory. Furthermore, we study the convergence of self-energy and density of states as the embedding size is systematically expanded in one-shot and self-consistent DMFT calculations.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"254 ","pages":" 641-652"},"PeriodicalIF":3.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/fd/d4fd00068d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enzyme-modified Pt nanoelectrodes for glutamate detection†","authors":"Peibo Xu, Henry David Jetmore, Ran Chen and Mei Shen","doi":"10.1039/D4FD00138A","DOIUrl":"10.1039/D4FD00138A","url":null,"abstract":"<p >We present here a glutamate oxidase (GluOx)-modified platinum (Pt) nanoelectrode with a planar geometry for glutamate detection. The Pt nanoelectrode was characterized using electrochemistry and scanning electron microscopy (SEM). The radius of the Pt nanoelectrode measured using SEM is ∼210 nm. GluOx-modified Pt nanoelectrodes were generated by dip coating GluOx on the Pt nanoelectrode in a solution of 0.9% (wt%) bovine serum albumin (BSA), 0.126% (wt%) glutaraldehyde, and 100 U mL<small>⁻¹</small> GluOx. An increase in current was observed at +0.7 V <em>vs.</em> Ag/AgCl/1 M KCl with adding increasing concentrations of glutamate. Two-sample <em>t</em>-test results showed that there is a significant difference for current at +0.7 V between the blank and the added lowest glutamate concentration, as well as between adjacent glutamate concentrations, confirming that the increase in current is related to the increased glutamate concentration. The experimental current–concentration curve of glutamate detection fitted well to the theoretical Michaelis–Menten curve. At the low concentration range (50 μM to 200 μM), a linear relationship between the current and glutamate concentration was observed. The Michaelis–Menten constants of <em>I</em><small>_max</small> and <em>K</em><small>_m</small> were calculated to be 1.093 pA and 0.227 mM, respectively. Biosensor efficiency (the ratio of glutamate sensitivity to H<small>₂</small>O<small>₂</small> sensitivity) is calculated to be 57.9%. Enz<small>_act</small> (<em>I</em><small>_max</small>/H<small>₂</small>O<small>₂</small> sensitivity, an indicator of the amount of enzyme loaded on the electrode) of the GluOx-modified Pt nanoelectrode is 0.243 mM. We further compared the sensitivity of a GluOx-modified Pt nanoelectrode with a GluOx-modified carbon fiber microelectrode (7 μm diameter and a sensing length of ∼350 μm). Glutamate detection on the GluOx-modified carbon fiber microelectrode fitted well to a Michaelis–Menten like response. Based on the fitting, the GluOx-modified carbon fiber microelectrode exhibited an <em>I</em><small>_max</small> of 0.689 nA and a <em>K</em><small>_m</small> of 301.2 μM towards glutamate detection. The best linear range of glutamate detection on the GluOx-modified carbon fiber microelectrode is from 50 μM to 150 μM glutamate. The GluOx-modified carbon fiber microelectrode exhibited a higher potential requirement for glutamate detection compared to the GluOx-modified Pt nanoelectrode.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"257 ","pages":" 165-181"},"PeriodicalIF":3.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}