ACS Physical Chemistry Au最新文献

{"title":"Quantum Molecular Devices","authors":"Ronnie Kosloff*,&nbsp;","doi":"10.1021/acsphyschemau.3c00077","DOIUrl":"10.1021/acsphyschemau.3c00077","url":null,"abstract":"<p >Miniaturization has been the driving force in contemporary technologies. However, two main obstacles limit further progress: additional reduction in size has reached its quantum limit, and lithography has reached its threshold. Future progress requires tackling three challenges: chemical synthesis of a complete device, active cooling for exploiting quantum characteristics, and quantum coherent control for operation. Chemical synthesis replaces the current top-bottom approach to manufacturing with bottom-up synthesis from elementary building blocks. New ultracold synthetic methods should be developed. An additional challenge is the active cooling of molecules, where the bottleneck is entropy removal. Notably, the current solution, namely, diffusion, is too slow. A coherent approach offers a possible solution; specifically, quantum coherent control is the method of choice for manipulating ultracold matter. Finally, the many degrees of freedom of molecules should be an asset that allows the design and implementation of complex tasks such as sensing communication and computing.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raman Spectroscopic Insights of Phase-Separated Insulin Aggregates","authors":"Sandip Dolui,&nbsp;Anupam Roy,&nbsp;Uttam Pal,&nbsp;Shubham Kundu,&nbsp;Esha Pandit,&nbsp;Bhisma N Ratha,&nbsp;Ranit Pariary,&nbsp;Achintya Saha,&nbsp;Anirban Bhunia and Nakul C. Maiti*,&nbsp;","doi":"10.1021/acsphyschemau.3c00065","DOIUrl":"10.1021/acsphyschemau.3c00065","url":null,"abstract":"<p >Phase-separated protein accumulation through the formation of several aggregate species is linked to the pathology of several human disorders and diseases. Our current investigation envisaged detailed Raman signature and structural intricacy of bovine insulin in its various forms of aggregates produced in situ at an elevated temperature (60 °C). The amide I band in the Raman spectrum of the protein in its native-like conformation appeared at 1655 cm^–1 and indicated the presence of a high content of α-helical structure as prepared freshly in acidic pH. The disorder content (turn and coils) also was predominately present in both the monomeric and oligomeric states and was confirmed by the presence shoulder amide I maker band at ∼1680 cm^–1. However, the band shifted to ∼1671 cm^–1 upon the transformation of the protein solution into fibrillar aggregates as produced for a longer time of incubation. The protein, however, maintained most of its helical conformation in the oligomeric phase; the low-frequency backbone α-helical conformation signal at ∼935 cm^–1 was similar to that of freshly prepared aqueous protein solution enriched in helical conformation. The peak intensity was significantly weak in the fibrillar aggregates, and it appeared as a good Raman signature to follow the phase separation and the aggregation behavior of insulin and similar other proteins. Tyrosine phenoxy moieties in the protein may maintained its H-bond donor–acceptor integrity throughout the course of fibril formation; however, it entered in more hydrophobic environment in its journey of fibril formation. In addition, it was noticed that oligomeric bovine insulin maintained the orientation/conformation of the disulfide bonds. However, in the fibrillar state, the disulfide linkages became more strained and preferred to maintain a single conformation state.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Chemistry Models for Chemical Research in the XXth and XXIst Centuries","authors":"Josep M. Ribó*,&nbsp; and ,&nbsp;David Hochberg*,&nbsp;","doi":"10.1021/acsphyschemau.3c00057","DOIUrl":"10.1021/acsphyschemau.3c00057","url":null,"abstract":"<p >Thermodynamic hypotheses and models are the touchstone for chemical results, but the actual models based on time-invariance, which have performed efficiently in the development of chemistry, are nowadays invalid for the interpretation of the behavior of complex systems exhibiting nonlinear kinetics and with matter and energy exchange flows with the surroundings. Such fields of research will necessarily foment and drive the use of thermodynamic models based on the description of irreversibility at the macroscopic level, instead of the current models which are strongly anchored in microreversibility.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Chemistry Models for Chemical Research in the XXth and XXIst Centuries","authors":"J. Ribó, D. Hochberg","doi":"10.1021/acsphyschemau.3c00057","DOIUrl":"https://doi.org/10.1021/acsphyschemau.3c00057","url":null,"abstract":"","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Vision for Physical Chemistry: An Inclusive Future for all Abilities","authors":"Juliana I. Bonilla-Lugo*,&nbsp; and ,&nbsp;Sarah E. Wolf*,&nbsp;","doi":"10.1021/acsphyschemau.3c00069","DOIUrl":"10.1021/acsphyschemau.3c00069","url":null,"abstract":"","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters","authors":"Guilian Luchini,&nbsp; and ,&nbsp;Robert S. Paton*,&nbsp;","doi":"10.1021/acsphyschemau.3c00045","DOIUrl":"10.1021/acsphyschemau.3c00045","url":null,"abstract":"<p >The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of “top-down” macroscopic observables, such as Hammett parameters using a “bottom-up” computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σ_m predictions fare much worse than σ_p. As a result, the choice of where to compute these descriptors─for the ring carbons or the attached H or other substituent atoms─affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"My Vision of Electric-Field-Aided Chemistry in 2050","authors":"Sason Shaik*,&nbsp;","doi":"10.1021/acsphyschemau.3c00064","DOIUrl":"10.1021/acsphyschemau.3c00064","url":null,"abstract":"<p >This manuscript outlines my outlook on the development of electric-field (EF)-mediated-chemistry and the vision of its state by 2050. I discuss applications of oriented-external electric-fields (OEEFs) on chemical reactions and proceed with relevant experimental verifications. Subsequently, the Perspective outlines other ways of generating EFs, e.g., by use of pH-switchable charges, ionic additives, water droplets, and so on. A special section summarizes conceptual principles for understanding and predicting OEEF effects, e.g., the “reaction-axis rule”, the capability of OEEFs to act as tweezers that orient reactants and accelerate their reaction, etc. Finally, I discuss applications of OEEFs in continuous-flow setups, which may, in principle, scale-up to molar concentrations. The Perspective ends with the vision that by 2050, OEEF usage will change chemical education, if not also the art of making new molecules.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139663220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydration Waters Make Up for the Missing Third Hydrogen Bond in the A·T Base Pair","authors":"Chi H. Mak*,&nbsp;","doi":"10.1021/acsphyschemau.3c00058","DOIUrl":"10.1021/acsphyschemau.3c00058","url":null,"abstract":"<p >Base pairing complementarity is central to DNA function. G·C and A·T pair specificity is thought to originate from the different number of hydrogen bonds the pairs make. Quantifying how many hydrogen bonds exist can be difficult because water molecules in the surrounding can make up for or disrupt direct hydrogen bonds, and the hydration structures around A·T and G·C pairs on duplex DNA are distinct. Large-scale computer simulations have been used here to create a detailed map for the hydration structure on A·T and G·C base pairs in water. The contributions of specific hydration waters to the free energy of each of the hydrogen bonds in the A·T and G·C pairs were computed. Using the equilibrium fractions of hydrated versus unhydrated states from the hydration profiles, the impact of specific bound waters on each hydrogen bond can be uniquely quantified using a thermodynamic construction. The findings suggest that hydration water in the minor groove of an A·T pair can provide up to about 2 kcal/mol of free energy advantage, effectively making up for the missing third hydrogen bond in the A·T pair compared to G·C, rendering the intrinsic thermodynamic stability of the A·T pair almost synonymous with G·C.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139551978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harvesting Chemical Understanding with Machine Learning and Quantum Computers","authors":"Shubin Liu*,&nbsp;","doi":"10.1021/acsphyschemau.3c00067","DOIUrl":"10.1021/acsphyschemau.3c00067","url":null,"abstract":"<p >It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of <i>in silico</i> simulations in the next few decades.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating the Membrane Binding Process of a Disordered Protein: Dynamic Interplay of Anionic Lipids and the Polybasic Region","authors":"Azadeh Alavizargar*,&nbsp;Maximilian Gass,&nbsp;Michael P. Krahn and Andreas Heuer,&nbsp;","doi":"10.1021/acsphyschemau.3c00051","DOIUrl":"10.1021/acsphyschemau.3c00051","url":null,"abstract":"<p >Intrinsically disordered regions of proteins are responsible for many biological processes such as in the case of liver kinase B1 (LKB1)─a serine/threonine kinase relevant for cell proliferation and cell polarity. LKB1 becomes fully activated upon recruitment to the plasma membrane by binding of its disordered C-terminal polybasic motif consisting of eight lysines/arginines to phospholipids. Here, we present extensive molecular dynamics (MD) simulations of the polybasic motif interacting with a model membrane composed of 1-palmitoyl-2-oleoyl-<i>sn</i>-glycero-3-phosphocholine (POPC) and 1-palmitoyl-2-oleyl phosphatidic acid (PA) and cell culture experiments. Protein–membrane binding effects are due to the electrostatic interactions between the polybasic amino acids and PAs. For significant binding, the first three lysines turn out to be dispensable, which was also recapitulated in cell culture using transfected GFP-LKB1 variants. LKB1–membrane binding results in nonmonotonous changes in the structure of the protein as well as the membrane, in particular, accumulation of PAs and reduced thickness at the protein–membrane contact area. The protein–lipid binding turns out to be highly dynamic due to an interplay of PA–PA repulsion and protein–PA attraction. The thermodynamics of this interplay is captured by a statistical fluctuation model, which allows the estimation of both energies. Quantification of the significance of each polar amino acid in the polybasic provides detailed insights into the molecular mechanism of protein–membrane binding of LKB1. These results can likely be transferred to other proteins, which interact by intrinsically disordered polybasic regions with anionic membranes.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}