1
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Hoffmann M, Poschenrieder J, Incudini M, Baier S, Fritz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl M, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward M, Jankowski J, Lee H, Tsoy O, Wenke N, Pedersen A, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth P, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal D. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. Nucleic Acids Res 2024; 52:10144-10160. [PMID: 39175109 PMCID: PMC11417373 DOI: 10.1093/nar/gkae697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 07/12/2024] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Julian M Poschenrieder
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit‘a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Sylvie Baier
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Amelie Fritz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Maximilian V Harl
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Ingmar Lesch
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hunor Frey
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Simon Kayser
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Paul Wissenberg
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Schwartz
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Hafner
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | | | - Jakub Jankowski
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Hye Kyung Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva1211, Switzerland
| | - Priscilla A Furth
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit‘a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
| | - Markus List
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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2
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Zhang H, Fichthorn KA. Structural classification of Ag and Cu nanocrystals with machine learning. NANOSCALE 2024; 16:17154-17164. [PMID: 39192812 DOI: 10.1039/d4nr02531h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
We use machine learning (ML) to classify the structures of mono-metallic Cu and Ag nanoparticles. Our datasets comprise a broad range of structures - both crystalline and amorphous - derived from parallel-tempering molecular dynamics simulations of nanoparticles in the 100-200 atom size range. We construct nanoparticle features using common neighbor analysis (CNA) signatures, and we utilize principal component analysis to reduce the dimensionality of the CNA feature set. To sort the nanoparticles into structural classes, we employed both K-means clustering and the Gaussian mixture model (GMM). We evaluated the performance of the clustering algorithms through the gap statistic and silhouette score, as well as by analysis of the CNA signatures. For Ag, we found five structural classes, with 14 detailed sub-classes, while for Cu, we found two broad classes (crystalline and amorphous), with the same five classes as for Ag, and 15 detailed sub-classes. Our results demonstrate that these ML methods are effective in identifying and categorizing nanoparticle structures to different levels of complexity, enabling us to classify nanoparticles into distinct and physically relevant structural classes with high accuracy. This capability is important for understanding nanoparticle properties and potential applications.
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Affiliation(s)
- Huaizhong Zhang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Kristen A Fichthorn
- Department of Chemical Engineering and Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
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3
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Naskar P, Talukder S. Energetics and spectroscopic studies of CNO (-) (H 2 O) n $$ {\mathbf{CNO}}^{\left(\hbox{-} \right)}{\left({\mathbf{H}}_{\mathbf{2}}\mathbf{O}\right)}_{\mathbf{n}} $$ clusters and the temperature dependencies of the isomers: An approach based on a combined recipe of parallel tempering and quantum chemical methods. J Comput Chem 2024. [PMID: 39151062 DOI: 10.1002/jcc.27480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/22/2024] [Accepted: 07/30/2024] [Indexed: 08/18/2024]
Abstract
A system associated with several number of weak interactions supports numerous number of stable structures within a narrow range of energy. Often, a deterministic search method fails to locate the global minimum geometry as well as important local minimum isomers for such systems. Therefore, in this work, the stochastic search technique, namely parallel tempering, has been executed on the quantum chemical surface of theCNO (-) (H 2 O) n $$ {\mathrm{CNO}}^{\left(\hbox{-} \right)}{\left({\mathrm{H}}_2\mathrm{O}\right)}_n $$ system forn = 1 $$ n=1 $$ -8 to generate global minimum as well as several number of local minimum isomers. IR spectrum can act as the fingerprint property for such system to be identified. Thus, IR spectroscopic features have also been included in this work. Vertical detachment energy has also been calculated to obtain clear information about number of water molecules in several spheres around the central anion. In addition, in a real experimental scenario, not only the global but also the local minimum isomers play an important role in determining the average value of a particular physically observable property. Therefore, the relative conformational populations have been determined for all the evaluated structures for the temperature range between 20K and 400K. Further to understand the phase change behavior, the configurational heat capacities have also been calculated for different sizes.
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Affiliation(s)
- Pulak Naskar
- Department of Chemistry, Mrinalini Datta Mahavidyapith, Kolkata, India
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4
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Aupič J, Pokorná P, Ruthstein S, Magistrato A. Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning. J Phys Chem Lett 2024; 15:8177-8186. [PMID: 39093570 DOI: 10.1021/acs.jpclett.4c01544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously adapt upon binding with specific interaction partners and/or certain alterations in environmental conditions. This malleability is foundational for their role as tunable interaction hubs in core cellular processes such as signaling, transcription, and translation. Tracing the conformational ensemble of an IDP/IDR and its perturbation in response to regulatory cues is thus paramount for illuminating its function. However, the conformational heterogeneity of IDP/IDRs poses several challenges. Here, we review experimental and computational methods devised to disentangle the conformational landscape of IDP/IDRs, highlighting recent computational advances that permit proteome-wide scans of IDP/IDRs conformations. We briefly evaluate selected computational methods using the disordered N-terminal of the human copper transporter 1 as a test case and outline further challenges in IDP/IDRs ensemble prediction.
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Affiliation(s)
- Jana Aupič
- CNR-IOM at International School for Advanced Studies (SISSA/ISAS), via Bonomea 265, 34136 Trieste, Italy
| | - Pavlína Pokorná
- CNR-IOM at International School for Advanced Studies (SISSA/ISAS), via Bonomea 265, 34136 Trieste, Italy
| | - Sharon Ruthstein
- Department of Chemistry, Faculty of Exact Sciences and the Institute for Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Alessandra Magistrato
- CNR-IOM at International School for Advanced Studies (SISSA/ISAS), via Bonomea 265, 34136 Trieste, Italy
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5
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Schön JC. Energy landscapes-Past, present, and future: A perspective. J Chem Phys 2024; 161:050901. [PMID: 39101536 DOI: 10.1063/5.0212867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/17/2024] [Indexed: 08/06/2024] Open
Abstract
Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics.
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Affiliation(s)
- J C Schön
- Max-Planck-Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
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6
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Murphy M, Greenhouse B. MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.03.560769. [PMID: 37873322 PMCID: PMC10592951 DOI: 10.1101/2023.10.03.560769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE's adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.
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7
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Cheung DL. Surface Hydrophobicity Strongly Influences Adsorption and Conformation of Amyloid Beta Derived Peptides. Molecules 2024; 29:3634. [PMID: 39125038 PMCID: PMC11314246 DOI: 10.3390/molecules29153634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/19/2024] [Accepted: 07/25/2024] [Indexed: 08/12/2024] Open
Abstract
The formation of amyloid fibrils is a common feature of many protein systems. It has implications in both health, as amyloid fibrils are implicated in over 30 degenerative diseases, and in the biological functions of proteins. Surfaces have long been known to affect the formation of fibrils but the specific effect depends on the details of both the surface and protein. Fully understanding the role of surfaces in fibrillization requires microscopic information on protein conformation on surfaces. In this paper replica exchange molecular dynamics simulation is used to investigate the model fibril forming protein, Aβ(10-40) (a 31-residue segment of the amyloid-beta protein) on surfaces of different hydrophobicity. Similar to other proteins Aβ(10-40) is found to adsorb strongly onto hydrophobic surfaces. It also adopts significantly different sets of conformations on hydrophobic and polar surfaces, as well as in bulk solution. On hydrophobic surfaces, it adopts partially helical structures, with the helices overlapping with beta-strand regions in the mature fibril. These may be helical intermediates on the fibril formation pathway, suggesting a mechanism for the enhanced fibril formation seen on hydrophobic surfaces.
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Affiliation(s)
- David L Cheung
- School of Biological and Chemical Sciences, University of Galway, University Road, H91 TK33 Galway, Ireland
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8
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Moraca F, Vespoli I, Mastroianni D, Piscopo V, Gaglione R, Arciello A, De Nisco M, Pacifico S, Catalanotti B, Pedatella S. Synthesis, biological evaluation and metadynamics simulations of novel N-methyl β-sheet breaker peptides as inhibitors of Alzheimer's β-amyloid fibrillogenesis. RSC Med Chem 2024; 15:2286-2299. [PMID: 39026638 PMCID: PMC11253850 DOI: 10.1039/d4md00057a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/07/2024] [Indexed: 07/20/2024] Open
Abstract
Several scientific evidences report that a central role in the pathogenesis of Alzheimer's disease is played by the deposition of insoluble aggregates of β-amyloid proteins in the brain. Because Aβ is self-assembling, one possible design strategy is to inhibit the aggregation of Aβ peptides using short peptide fragments homologous to the full-length wild-type Aβ protein. In the past years, several studies have reported on the synthesis of some short synthetic peptides called β-sheet breaker peptides (BSBPs). Herein, we present the synthesis of novel (cell-permeable) N-methyl BSBPs, designed based on literature information on the structural key features of BSBPs. Three-dimensional GRID-based pharmacophore peptide screening combined with PT-WTE metadynamics was performed to support the results of the design and microwave-assisted synthesis of peptides 2 and 3 prepared and analyzed for their fibrillogenesis inhibition activity and cytotoxicity. An HR-MS-based cell metabolomic approach highlighted their cell permeability properties.
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Affiliation(s)
- Federica Moraca
- Department of Pharmacy, University of Napoli Federico II Via Domenico Montesano 49 I-80131 Napoli Italy
- Net4Science Academic Spin-Off, University "Magna Græcia" of Catanzaro Viale Europa 88100 Catanzaro Italy
| | - Ilaria Vespoli
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Flemingovo náměstí 542/2 CZ-16610 Prague Czech Republic
| | - Domenico Mastroianni
- Department of Chemical Sciences, University of Napoli Federico II Via Cintia 4 I-80126 Napoli Italy
| | - Vincenzo Piscopo
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli" Viale Abramo Lincoln 5 I-81100 Caserta Italy
| | - Rosa Gaglione
- Department of Chemical Sciences, University of Napoli Federico II Via Cintia 4 I-80126 Napoli Italy
- Istituto Nazionale di Biostrutture e Biosistemi (INBB) Viale delle Medaglie d'Oro 305 I-80145 Roma Italy
| | - Angela Arciello
- Department of Chemical Sciences, University of Napoli Federico II Via Cintia 4 I-80126 Napoli Italy
- Istituto Nazionale di Biostrutture e Biosistemi (INBB) Viale delle Medaglie d'Oro 305 I-80145 Roma Italy
| | - Mauro De Nisco
- Department of Sciences, University of Basilicata Viale dell'Ateneo Lucano I-85100 Potenza Italy
| | - Severina Pacifico
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli" Viale Abramo Lincoln 5 I-81100 Caserta Italy
| | - Bruno Catalanotti
- Department of Pharmacy, University of Napoli Federico II Via Domenico Montesano 49 I-80131 Napoli Italy
| | - Silvana Pedatella
- Department of Chemical Sciences, University of Napoli Federico II Via Cintia 4 I-80126 Napoli Italy
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9
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Bjola A, Salvalaglio M. Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration. J Chem Theory Comput 2024; 20:5418-5427. [PMID: 38913384 PMCID: PMC11238544 DOI: 10.1021/acs.jctc.4c00091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 06/25/2024]
Abstract
Addressing the sampling problem is central to obtaining quantitative insight from molecular dynamics simulations. Adaptive biased sampling methods, such as metadynamics, tackle this issue by perturbing the Hamiltonian of a system with a history-dependent bias potential, enhancing the exploration of the ensemble of configurations and estimating the corresponding free energy surface (FES). Nevertheless, efficiently assessing and systematically improving their convergence remains an open problem. Here, building on mean force integration (MFI), we develop and test a metric for estimating the convergence of FESs obtained by combining asynchronous, independent simulations subject to diverse biasing protocols, including static biases, different variants of metadynamics, and various combinations of static and history-dependent biases. The developed metric and the ability to combine independent simulations granted by MFI enable us to devise strategies to systematically improve the quality of FES estimates. We demonstrate our approach by computing FES and assessing the convergence of a range of systems of increasing complexity, including one- and two-dimensional analytical FESs, alanine dipeptide, a Lennard-Jones supersaturated vapor undergoing liquid droplet nucleation, and the model of a colloidal system crystallizing via a two-step mechanism. The methods presented here can be generally applied to biased simulations and are implemented in pyMFI, a publicly accessible, open-source Python library.
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Affiliation(s)
- Antoniu Bjola
- Thomas Young Centre and Department
of Chemical Engineering, University College
London, London WC1E 7JE, U.K.
| | - Matteo Salvalaglio
- Thomas Young Centre and Department
of Chemical Engineering, University College
London, London WC1E 7JE, U.K.
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10
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Saurabh A, Brown PT, Bryan JS, Fox ZR, Kruithoff R, Thompson C, Kural C, Shepherd DP, Pressé S. Approaching Maximum Resolution in Structured Illumination Microscopy via Accurate Noise Modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570701. [PMID: 38106139 PMCID: PMC10723446 DOI: 10.1101/2023.12.07.570701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.
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Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Peter T. Brown
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - J. Shepard Bryan
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Zachary R. Fox
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Rory Kruithoff
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | | | - Comert Kural
- Department of Physics, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Douglas P. Shepherd
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
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11
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Fram B, Su Y, Truebridge I, Riesselman AJ, Ingraham JB, Passera A, Napier E, Thadani NN, Lim S, Roberts K, Kaur G, Stiffler MA, Marks DS, Bahl CD, Khan AR, Sander C, Gauthier NP. Simultaneous enhancement of multiple functional properties using evolution-informed protein design. Nat Commun 2024; 15:5141. [PMID: 38902262 PMCID: PMC11190266 DOI: 10.1038/s41467-024-49119-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 05/24/2024] [Indexed: 06/22/2024] Open
Abstract
A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.
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Affiliation(s)
- Benjamin Fram
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Yang Su
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ian Truebridge
- Institute for Protein Innovation, Boston, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- AI Proteins, Boston, MA, USA
| | - Adam J Riesselman
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Program in Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - John B Ingraham
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Alessandro Passera
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030, Vienna, Austria
| | - Eve Napier
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
| | - Nicole N Thadani
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Apriori Bio, Cambridge, MA, USA
| | - Samuel Lim
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Kristen Roberts
- Selux Diagnostics Inc., 56 Roland Street, Charlestown, MA, USA
| | - Gurleen Kaur
- Selux Diagnostics Inc., 56 Roland Street, Charlestown, MA, USA
| | - Michael A Stiffler
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Dyno Therapeutics, 343 Arsenal Street, Watertown, MA, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- AI Proteins, Boston, MA, USA
| | - Amir R Khan
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas P Gauthier
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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12
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Kallabis C, Beyerlein P, Lisdat F. Quantitative determination of dopamine in the presence of interfering substances supported by machine learning tools. Bioelectrochemistry 2024; 157:108667. [PMID: 38377891 DOI: 10.1016/j.bioelechem.2024.108667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/10/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
In the field of neuroscience as well as in the clinical setting, the neurotransmitter dopamine (DA) is an analyte which is important for research as well as medical purposes. There are plenty of methods available to measure dopamine quantitatively, with voltammetric ones such as differential pulse voltammetry (DPV) being among the most convenient and simple ones. However, dopamine often occurs, either naturally or because of the requirements of involved enzymatic systems, alongside substances that can influence the signal it produces upon electrochemical conversion. An example for such substances is the magnesium ion, which itself is not electrochemically active in the potential range needed for DA oxidation, but influences the dopamine signal. We have characterized the properties of DPV signals subject to the interaction between DA and Mg2+ and show that, although these properties are changing in a nonlinear fashion when both concentrations are varying, relatively simple linear mathematical models can be used to determine dopamine concentrations quantitatively in the presence of magnesium ions. The focus of this study is thus, the mathematical treatment of experimental data in order to overcome an analytical problem and not the investigation of the chemical background of DA-Mg2+ interaction.
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Affiliation(s)
- C Kallabis
- Biosystems Technology, Institute of Life Sciences and Biomedical Technologies, Technical University Wildau, Hochschulring 1, 15745 Wildau, Germany.
| | - P Beyerlein
- ibiomics UG, Kamerunerstrasse 9, 15711 Königswusterhausen, Germany
| | - F Lisdat
- Biosystems Technology, Institute of Life Sciences and Biomedical Technologies, Technical University Wildau, Hochschulring 1, 15745 Wildau, Germany.
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13
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Riveros II, Yildirim I. Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops. J Chem Theory Comput 2024; 20:4363-4376. [PMID: 38728627 DOI: 10.1021/acs.jctc.4c00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.
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Affiliation(s)
- Ivan Isaac Riveros
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States
| | - Ilyas Yildirim
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States
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14
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Sepali C, Gómez S, Grifoni E, Giovannini T, Cappelli C. Computational Spectroscopy of Aqueous Solutions: The Underlying Role of Conformational Sampling. J Phys Chem B 2024; 128:5083-5091. [PMID: 38733374 DOI: 10.1021/acs.jpcb.4c01443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
Fully atomistic multiscale polarizable quantum mechanics (QM)/molecular mechanics (MM) approaches, combined with techniques to sample the solute-solvent phase space, constitute the most accurate method to compute spectral signals in aqueous solution. Conventional sampling strategies, such as classical molecular dynamics (MD), may encounter drawbacks when the conformational space is particularly complex, and transition barriers between conformers are high. This can lead to inaccurate sampling, which can potentially impact the accuracy of spectral calculations. For this reason, in this work, we compare classical MD with enhanced sampling techniques, i.e., replica exchange MD and metadynamics. In particular, we show how the different sampling techniques affect computed UV, electronic circular dichroism, nuclear magnetic resonance shielding, and optical rotatory dispersion of N-acetylproline-amide in aqueous solution. Such a system is a model peptide characterized by complex conformational variability. Calculated values suggest that spectral properties are influenced by solute conformers, relative population, and solvent effects; therefore, particular care needs to be paid for when choosing the sampling technique.
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Affiliation(s)
- Chiara Sepali
- Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Sara Gómez
- Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Emanuele Grifoni
- Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | | | - Chiara Cappelli
- Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
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15
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Zuckerman DM, George A. Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo. J Chem Theory Comput 2024; 20:2971-2984. [PMID: 38603773 PMCID: PMC11089648 DOI: 10.1021/acs.jctc.4c00014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.
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Affiliation(s)
- Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239, United States
| | - August George
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239, United States
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16
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Sadeghi M, Rosenberger D. Dynamic framework for large-scale modeling of membranes and peripheral proteins. Methods Enzymol 2024; 701:457-514. [PMID: 39025579 DOI: 10.1016/bs.mie.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
In this chapter, we present a novel computational framework to study the dynamic behavior of extensive membrane systems, potentially in interaction with peripheral proteins, as an alternative to conventional simulation methods. The framework effectively describes the complex dynamics in protein-membrane systems in a mesoscopic particle-based setup. Furthermore, leveraging the hydrodynamic coupling between the membrane and its surrounding solvent, the coarse-grained model grounds its dynamics in macroscopic kinetic properties such as viscosity and diffusion coefficients, marrying the advantages of continuum- and particle-based approaches. We introduce the theoretical background and the parameter-space optimization method in a step-by-step fashion, present the hydrodynamic coupling method in detail, and demonstrate the application of the model at each stage through illuminating examples. We believe this modeling framework to hold great potential for simulating membrane and protein systems at biological spatiotemporal scales, and offer substantial flexibility for further development and parametrization.
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Affiliation(s)
- Mohsen Sadeghi
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
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17
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Trubey P, Sansó B. Bayesian Non-Parametric Inference for Multivariate Peaks-over-Threshold Models. ENTROPY (BASEL, SWITZERLAND) 2024; 26:335. [PMID: 38667889 PMCID: PMC11049620 DOI: 10.3390/e26040335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
We consider a constructive definition of the multivariate Pareto that factorizes the random vector into a radial component and an independent angular component. The former follows a univariate Pareto distribution, and the latter is defined on the surface of the positive orthant of the infinity norm unit hypercube. We propose a method for inferring the distribution of the angular component by identifying its support as the limit of the positive orthant of the unit p-norm spheres and introduce a projected gamma family of distributions defined through the normalization of a vector of independent random gammas to the space. This serves to construct a flexible family of distributions obtained as a Dirichlet process mixture of projected gammas. For model assessment, we discuss scoring methods appropriate to distributions on the unit hypercube. In particular, working with the energy score criterion, we develop a kernel metric that produces a proper scoring rule and presents a simulation study to compare different modeling choices using the proposed metric. Using our approach, we describe the dependence structure of extreme values in the integrated vapor transport (IVT), data describing the flow of atmospheric moisture along the coast of California. We find clear but heterogeneous geographical dependence.
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Affiliation(s)
- Peter Trubey
- Department of Statistics, University of California, Santa Cruz, CA 95064, USA;
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18
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Pirnia A, Maqdisi R, Mittal S, Sener M, Singharoy A. Perspective on Integrative Simulations of Bioenergetic Domains. J Phys Chem B 2024; 128:3302-3319. [PMID: 38562105 DOI: 10.1021/acs.jpcb.3c07335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.
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Affiliation(s)
- Adam Pirnia
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Ranel Maqdisi
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Sumit Mittal
- VIT Bhopal University, Sehore 466114, Madhya Pradesh, India
| | - Melih Sener
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
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19
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Werner P, Hartmann AK. Optimized finite-time work protocols for the Higgs RNA model with external force. Phys Rev E 2024; 109:044127. [PMID: 38755889 DOI: 10.1103/physreve.109.044127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 02/27/2024] [Indexed: 05/18/2024]
Abstract
The Higgs RNA model with an added term for a coupling to an external force is studied in regard to finite-time force-driving protocols with a minimal-work requirement. In this paper, RNA sequences which at low temperature exhibit hairpins are considered, which are often cited as typical template systems in stochastic thermodynamics. The optimized work protocols for this glassy many-particle system are determined numerically using the parallel tempering method. The protocols show distinct jumps at the beginning and end, which have been observed for single-particle systems and are proven to be optimal in the fast protocol limit generally. Optimality seems to be achieved by staying close to the equilibrium unfolding transition point, in agreement with experimental and theoretical observations. The change of work distributions, compared to those resulting from a naive linear driving protocol, are discussed generally and in terms of free energy estimation as well as the effect of optimized protocols on rare work process starting conditions.
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Affiliation(s)
- Peter Werner
- Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
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20
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Wang Y, Zhang YE, Pan F, Zhang P. Tensor Network Message Passing. PHYSICAL REVIEW LETTERS 2024; 132:117401. [PMID: 38563954 DOI: 10.1103/physrevlett.132.117401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 11/20/2023] [Accepted: 02/06/2024] [Indexed: 04/04/2024]
Abstract
When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops, while tensor network methods can have exponential computational complexity in large graphs due to long loops. In this Letter, we propose a new method called "tensor network message passing." This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small subgraphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally treelike and locally dense-connected when the dense local graphs have a limited tree width. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, and have demonstrated the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.
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Affiliation(s)
- Yijia Wang
- CAS Key Laboratory for Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuwen Ebony Zhang
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Feng Pan
- CAS Key Laboratory for Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Pan Zhang
- CAS Key Laboratory for Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
- School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- Hefei National Laboratory, Hefei 230088, China
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21
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Bonnel J, Dosso SE, Hodgkiss WS, Ballard MS, Garcia DD, Lee KM, McNeese AR, Wilson PS. Trans-dimensional inversion for seafloor properties for three mud depocenters on the New England shelf under dynamical oceanographic conditionsa). THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2024; 155:1825-1839. [PMID: 38445985 DOI: 10.1121/10.0025176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 02/17/2024] [Indexed: 03/07/2024]
Abstract
This paper presents inversion results for three datasets collected on three spatially separated mud depocenters (hereafter called mud ponds) during the 2022 Seabed Characterization Experiment (SBCEX). The data considered here represent modal time-frequency (TF) dispersion as estimated from a single hydrophone. Inversion is performed using a trans-dimensional (trans-D) Bayesian inference method that jointly estimates water-column and seabed properties along with associated uncertainties. This enables successful estimation of the seafloor properties, consistent with in situ acoustic core measurements, even when the water column is dynamical and mostly unknown. A quantitative analysis is performed to (1) compare results with previous modal TF trans-D studies for one mud pond but under different oceanographic condition, and (2) inter-compare the new SBCEX22 results for the three mud ponds. Overall, the estimated mud geoacoustic properties show no significant temporal variability. Further, no significant spatial variability is found between two of the mud ponds while the estimated geoacoustic properties of the third are different. Two hypotheses, considered to be equally likely, are explored to explain this apparent spatial variability: it may be the result of actual differences in the mud properties, or the mud properties may be similar but the inversion results are driven by difference in data information content.
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Affiliation(s)
- Julien Bonnel
- Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02540, USA
| | - Stan E Dosso
- School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W2Y2, Canada
| | - William S Hodgkiss
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA
| | - Megan S Ballard
- Walker Department of Mechanical Engineering and Applied Research Laboratories, University of Texas at Austin, Austin, Texas 78712, USA
| | - Dante D Garcia
- Walker Department of Mechanical Engineering and Applied Research Laboratories, University of Texas at Austin, Austin, Texas 78712, USA
| | - Kevin M Lee
- Walker Department of Mechanical Engineering and Applied Research Laboratories, University of Texas at Austin, Austin, Texas 78712, USA
| | - Andrew R McNeese
- Walker Department of Mechanical Engineering and Applied Research Laboratories, University of Texas at Austin, Austin, Texas 78712, USA
| | - Preston S Wilson
- Walker Department of Mechanical Engineering and Applied Research Laboratories, University of Texas at Austin, Austin, Texas 78712, USA
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22
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Salvadori G, Mazzeo P, Accomasso D, Cupellini L, Mennucci B. Deciphering Photoreceptors Through Atomistic Modeling from Light Absorption to Conformational Response. J Mol Biol 2024; 436:168358. [PMID: 37944793 DOI: 10.1016/j.jmb.2023.168358] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/28/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
In this review, we discuss the successes and challenges of the atomistic modeling of photoreceptors. Throughout our presentation, we integrate explanations of the primary methodological approaches, ranging from quantum mechanical descriptions to classical enhanced sampling methods, all while providing illustrative examples of their practical application to specific systems. To enhance the effectiveness of our analysis, our primary focus has been directed towards the examination of applications across three distinct photoreceptors. These include an example of Blue Light-Using Flavin (BLUF) domains, a bacteriophytochrome, and the orange carotenoid protein (OCP) employed by cyanobacteria for photoprotection. Particular emphasis will be placed on the pivotal role played by the protein matrix in fine-tuning the initial photochemical event within the embedded chromophore. Furthermore, we will investigate how this localized perturbation initiates a cascade of events propagating from the binding pocket throughout the entire protein structure, thanks to the intricate network of interactions between the chromophore and the protein.
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Affiliation(s)
- Giacomo Salvadori
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Patrizia Mazzeo
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Davide Accomasso
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Lorenzo Cupellini
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
| | - Benedetta Mennucci
- Department of Chemistry and Industrial Chemistry, University of Pisa, 56124 Pisa, Italy
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23
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Wang Y, Stebe KJ, de la Fuente-Nunez C, Radhakrishnan R. Computational Design of Peptides for Biomaterials Applications. ACS APPLIED BIO MATERIALS 2024; 7:617-625. [PMID: 36971822 PMCID: PMC11190638 DOI: 10.1021/acsabm.2c01023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.
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Affiliation(s)
- Yiming Wang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Kathleen J Stebe
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Cesar de la Fuente-Nunez
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Ravi Radhakrishnan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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24
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Hayton JA, Davies MB, Whale TF, Michaelides A, Cox SJ. The limit of macroscopic homogeneous ice nucleation at the nanoscale. Faraday Discuss 2024; 249:210-228. [PMID: 37791990 DOI: 10.1039/d3fd00099k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Nucleation in small volumes of water has garnered renewed interest due to the relevance of pore condensation and freezing under conditions of low partial pressures of water, such as in the upper troposphere. Molecular simulations can in principle provide insight on this process at the molecular scale that is challenging to achieve experimentally. However, there are discrepancies in the literature as to whether the rate in confined systems is enhanced or suppressed relative to bulk water at the same temperature and pressure. In this study, we investigate the extent to which the size of the critical nucleus and the rate at which it grows in thin films of water are affected by the thickness of the film. Our results suggest that nucleation remains bulk-like in films that are barely large enough accommodate a critical nucleus. This conclusion seems robust to the presence of physical confining boundaries. We also discuss the difficulties in unambiguously determining homogeneous nucleation rates in nanoscale systems, owing to the challenges in defining the volume. Our results suggest any impact on a film's thickness on the rate is largely inconsequential for present day experiments.
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Affiliation(s)
- John A Hayton
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Michael B Davies
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
- Department of Physics and Astronomy, University College London, London WC1E 6BT, UK
| | - Thomas F Whale
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
- School of Earth and Environment, University of Leeds, Leeds, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Stephen J Cox
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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25
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Onizawa N, Hanyu T. Enhanced convergence in p-bit based simulated annealing with partial deactivation for large-scale combinatorial optimization problems. Sci Rep 2024; 14:1339. [PMID: 38228712 DOI: 10.1038/s41598-024-51639-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/08/2024] [Indexed: 01/18/2024] Open
Abstract
This article critically investigates the limitations of the simulated annealing algorithm using probabilistic bits (pSA) in solving large-scale combinatorial optimization problems. The study begins with an in-depth analysis of the pSA process, focusing on the issues resulting from unexpected oscillations among p-bits. These oscillations hinder the energy reduction of the Ising model and thus obstruct the successful execution of pSA in complex tasks. Through detailed simulations, we unravel the root cause of this energy stagnation, identifying the feedback mechanism inherent to the pSA operation as the primary contributor to these disruptive oscillations. To address this challenge, we propose two novel algorithms, time average pSA (TApSA) and stalled pSA (SpSA). These algorithms are designed based on partial deactivation of p-bits and are thoroughly tested using Python simulations on maximum cut benchmarks that are typical combinatorial optimization problems. On the 16 benchmarks from 800 to 5000 nodes, the proposed methods improve the normalized cut value from 0.8 to 98.4% on average in comparison with the conventional pSA.
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Affiliation(s)
- Naoya Onizawa
- Research Institute of Electrical Communication, Tohoku University, Sendai, 980-8577, Japan.
| | - Takahiro Hanyu
- Research Institute of Electrical Communication, Tohoku University, Sendai, 980-8577, Japan
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26
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Giacalone G, Nijs G, van der Schee W. Determination of the Neutron Skin of ^{208}Pb from Ultrarelativistic Nuclear Collisions. PHYSICAL REVIEW LETTERS 2023; 131:202302. [PMID: 38039448 DOI: 10.1103/physrevlett.131.202302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/29/2023] [Indexed: 12/03/2023]
Abstract
Emergent bulk properties of matter governed by the strong nuclear force give rise to physical phenomena across vastly different scales, ranging from the shape of atomic nuclei to the masses and radii of neutron stars. They can be accessed on Earth by measuring the spatial extent of the outer skin made of neutrons that characterizes the surface of heavy nuclei. The isotope ^{208}Pb, owing to its simple structure and neutron excess, has been in this context the target of many dedicated efforts. Here, we determine the neutron skin from measurements of particle distributions and their collective flow in ^{208}Pb+^{208}Pb collisions at ultrarelativistic energy performed at the Large Hadron Collider, which are mediated by interactions of gluons and thus sensitive to the overall size of the colliding ^{208}Pb ions. By means of state-of-the-art global analysis tools within the hydrodynamic model of heavy-ion collisions, we infer a neutron skin Δr_{np}=0.217±0.058 fm, consistent with nuclear theory predictions, and competitive in accuracy with a recent determination from parity-violating asymmetries in polarized electron scattering. We establish thus a new experimental method to systematically measure neutron distributions in the ground state of atomic nuclei.
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Affiliation(s)
- Giuliano Giacalone
- Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany
| | - Govert Nijs
- Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Wilke van der Schee
- Theoretical Physics Department, CERN, CH-1211 Genève 23, Switzerland
- Institute for Theoretical Physics, Utrecht University, 3584 CC Utrecht, The Netherlands
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27
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Aierken D, Bachmann M. Secondary-structure phase formation for semiflexible polymers by bifurcation in hyperphase space. Phys Chem Chem Phys 2023; 25:30246-30258. [PMID: 37921656 DOI: 10.1039/d3cp02815a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Canonical analysis has long been the primary analysis method for studies of phase transitions. However, this approach is not sensitive enough if transition signals are too close in temperature space. The recently introduced generalized microcanonical inflection-point analysis method not only enables the systematic identification and classification of transitions in systems of any size, but it can also distinguish transitions that standard canonical analysis cannot resolve. By applying this method to a generic coarse-grained model for semiflexible polymers, we identify a mixed structural phase dominated by secondary structures such as hairpins and loops that originates from a bifurcation in the hyperspace spanned by inverse temperature and bending stiffness. This intermediate phase, which is embraced by the well-known random-coil and toroidal phases, is testimony to the necessity of balancing entropic variability and energetic stability in functional macromolecules under physiological conditions.
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Affiliation(s)
- Dilimulati Aierken
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
- Omenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ 08540, USA.
- Soft Matter Systems Research Group, Center for Simulational Physics, Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Michael Bachmann
- Soft Matter Systems Research Group, Center for Simulational Physics, Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
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28
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Patil K, Wang Y, Chen Z, Suresh K, Radhakrishnan R. Activating mutations drive human MEK1 kinase using a gear-shifting mechanism. Biochem J 2023; 480:1733-1751. [PMID: 37869794 PMCID: PMC10872882 DOI: 10.1042/bcj20230281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/30/2023] [Accepted: 10/20/2023] [Indexed: 10/24/2023]
Abstract
There is an unmet need to classify cancer-promoting kinase mutations in a mechanistically cognizant way. The challenge is to understand how mutations stabilize different kinase configurations to alter function, and how this influences pathogenic potential of the kinase and its responses to therapeutic inhibitors. This goal is made more challenging by the complexity of the mutational landscape of diseases, and is further compounded by the conformational plasticity of each variant where multiple conformations coexist. We focus here on the human MEK1 kinase, a vital component of the RAS/MAPK pathway in which mutations cause cancers and developmental disorders called RASopathies. We sought to explore how these mutations alter the human MEK1 kinase at atomic resolution by utilizing enhanced sampling simulations and free energy calculations. We computationally mapped the different conformational stabilities of individual mutated systems by delineating the free energy landscapes, and showed how this relates directly to experimentally quantified developmental transformation potentials of the mutations. We conclude that mutations leverage variations in the hydrogen bonding network associated with the conformational plasticity to progressively stabilize the active-like conformational state of the kinase while destabilizing the inactive-like state. The mutations alter residue-level internal molecular correlations by differentially prioritizing different conformational states, delineating the various modes of MEK1 activation reminiscent of a gear-shifting mechanism. We define the molecular basis of conversion of this kinase from its inactive to its active state, connecting structure, dynamics, and function by delineating the energy landscape and conformational plasticity, thus augmenting our understanding of MEK1 regulation.
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Affiliation(s)
- Keshav Patil
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Yiming Wang
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Zhangtao Chen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Krishna Suresh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, U.S.A
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, U.S.A
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29
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Darkins R, Duffy DM, Ford IJ. Accelerating Solvent Dynamics with Replica Exchange for Improved Free Energy Sampling. J Chem Theory Comput 2023; 19:7527-7532. [PMID: 37864561 PMCID: PMC10653078 DOI: 10.1021/acs.jctc.3c00786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/23/2023]
Abstract
Molecular reactions in solution typically involve solvent exchange; for example, a surface must partly desolvate for a molecule to adsorb onto it. When these reactions are simulated, slow solvent dynamics can limit the sampling of configurations and reduce the accuracy of free energy estimates. Here, we combine Hamiltonian replica exchange (HREX) with well-tempered metadynamics (WTMD) to accelerate the sampling of solvent configurations orthogonal to the collective variable space. We compute the formation free energy of a carbonate vacancy in the calcite-water interface and find that the combination of WTMD with HREX significantly improves the sampling relative to WTMD without HREX.
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Affiliation(s)
- Robert Darkins
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K.
| | - Dorothy M. Duffy
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K.
| | - Ian J. Ford
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K.
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30
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Luo Y, Zhen YZ, Liu X, Ebler D, Dahlsten O. General limit to thermodynamic annealing performance. Phys Rev E 2023; 108:L052105. [PMID: 38115520 DOI: 10.1103/physreve.108.l052105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 10/13/2023] [Indexed: 12/21/2023]
Abstract
Annealing has proven highly successful in finding minima in a cost landscape. Yet, depending on the landscape, systems often converge towards local minima rather than global ones. In this Letter, we analyze the conditions for which annealing is approximately successful in finite time. We connect annealing to stochastic thermodynamics to derive a general bound on the distance between the system state at the end of the annealing and the ground state of the landscape. This distance depends on the amount of state updates of the system and the accumulation of nonequilibrium energy, two protocol and energy landscape-dependent quantities which we show are in a trade-off relation. We describe how to bound the two quantities both analytically and physically. This offers a general approach to assess the performance of annealing from accessible parameters, both for simulated and physical implementations.
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Affiliation(s)
- Yutong Luo
- Blackett Laboratory, Imperial College London, London SW7 2AZ, United Kingdom
- Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yi-Zheng Zhen
- Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei 230026, China
- Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China
| | - Xiangjing Liu
- Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Daniel Ebler
- Theory Laboratory, Central Research Institute, 2012 Labs, Huawei Technology Company Limited, Hong Kong Science Park, Hong Kong SAR, China
- Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Oscar Dahlsten
- Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China
- Institute of Nanoscience and Applications, Southern University of Science and Technology, Shenzhen 518055, China
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31
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Yan T, Zhang H, Fichthorn KA. Minimum Free-Energy Shapes of Ag Nanocrystals: Vacuum vs Solution. ACS NANO 2023; 17:19288-19304. [PMID: 37781898 DOI: 10.1021/acsnano.3c06395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
We use two variants of replica-exchange molecular dynamics (MD) simulations, parallel tempering MD and partial replica exchange MD, to probe the minimum free-energy shapes of Ag nanocrystals containing 100-200 atoms in a vacuum, ethylene glycol (EG) solvent, and EG solvent with a PVP polymer containing 100 repeat units. Our simulations reveal a shape intermediate between a Dh and an Ih, a Dh-Ih, that has distinct structural signatures and magic sizes. We find several prominent features associated with entropy: pure FCC nanocrystals are less common than FCC crystals containing stacking faults, and crystals with the minimum potential energy are not always preferred over the range of relevant temperatures. The shapes of the nanocrystals in solution are influenced by the chemical identities of the solution-phase molecules. Comparing Ag nanocrystal shapes in EG to those in an EG+PVP solution, we find more icosahedra in EG and more decahedra in EG+PVP across all of the nanocrystal sizes probed in this study. At certain critical sizes, nanocrystal shapes can change dramatically with the addition and removal of a single atom or with a change in temperature at a fixed size. The information in our study could be useful in efforts to devise processing routes to achieve selective nanocrystal shapes.
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Affiliation(s)
- Tianyu Yan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Huaizhong Zhang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Kristen A Fichthorn
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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32
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Geist N, Nagel F, Delcea M. Molecular interplay of ADAMTS13-MDTCS and von willebrand Factor-A2: deepened insights from extensive atomistic simulations. J Biomol Struct Dyn 2023; 41:8201-8214. [PMID: 36271641 DOI: 10.1080/07391102.2022.2135138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/24/2022] [Indexed: 10/24/2022]
Abstract
Thrombotic thrombocytopenic purpura (TTP) is a rare and life-threatening disease. One hallmark is severe ADAMTS13 deficiency, causing ultra-large von Willebrand factor (VWF) multimers to accumulate, leading to microthrombi and lastly to microangiopathic hemolytic anemia and severe thrombocytopenia. Despite great success in recent decades, the molecular picture of the interaction between VWF and ADAMTS13 remains vague. Here, we utilized modern replica-exchange molecular dynamics simulations with the TIGER2h method to sample a vast configurational space of the isolated ADAMTS13-MDTCS domains and the exposure to its substrate and activating cofactor - the unraveled VWF-A2 domain. The sampling of binding sites and conformations was guided and filtered in agreement with available experimental evidence. We provide comprehensive information on exosites for each domain and direct pairs of interacting amino acids, for the first time. The major binding cluster for the active site of the MP domain contrasts the previous mapping of VWF-A2 residues and reciprocal binding pockets. Two major binding modes are revealed and provide access to conformational changes of an extended gatekeeper tetrad upon overcoming local latency during substrate binding and to a dedicated recruitment mechanism. Our work adds the first molecular interaction model that places previous experimental results in perspective to better understand disease-related mutations towards improved therapies. Numerous empirical targets are proposed to verify the given binding modes, to refine the overall picture of MP binding pockets, the role of Dis binding in MP activation and the passage of the Cys-rich domain through VWF-A2, thus deepening the understanding of a highly dynamic interplay.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Norman Geist
- University of Greifswald, Biophysical Chemistry, Institute of Biochemistry, Greifswald, Germany
| | - Felix Nagel
- University of Greifswald, Biophysical Chemistry, Institute of Biochemistry, Greifswald, Germany
| | - Mihaela Delcea
- University of Greifswald, Biophysical Chemistry, Institute of Biochemistry, Greifswald, Germany
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33
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Jiang YM, Holland CW, Dosso SE, Dettmer J. Depth and frequency dependence of geoacoustic properties on the New England Mud Patch from reflection coefficient inversiona). THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2023; 154:2383-2397. [PMID: 37850832 DOI: 10.1121/10.0021309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/21/2023] [Indexed: 10/19/2023]
Abstract
Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. This paper presents a generally applicable model for sediment-column structure and variability on the New England Mud Patch (NEMP), based on trans-dimensional Bayesian inversion of wide-angle, broadband reflection-coefficient data in this work and in two previously published reflection-coefficient inversions at different sites on the NEMP. The data considered here include higher frequencies and larger bandwidth and cover lower reflection grazing angles than the previous studies, hence, resulting in geoacoustic profiles with significantly better structural resolution and smaller uncertainties. The general sediment-column structure model includes an upper mud layer in which sediment properties change slightly with depth due to near-surface processes, an intermediate mud layer with nearly uniform properties, and a geoacoustic transition layer where properties change rapidly with depth (porosity decreases and sound speed, density, and attenuation increase) due to increasing sand content in the mud above a sand layer. Over the full frequency band considered in the new and two previous data sets (400-3125 Hz), there is no significant sound-speed dispersion in the mud, and attenuation follows an approximately linear frequency dependence.
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Affiliation(s)
- Yong-Min Jiang
- School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
| | - Charles W Holland
- Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon 97201, USA
| | - Stan E Dosso
- School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada
| | - Jan Dettmer
- Department of Geoscience, University of Calgary, Calgary, Alberta T2N 1N4, Canada
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34
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Cox AA, Keller CB. A Bayesian inversion for emissions and export productivity across the end-Cretaceous boundary. Science 2023; 381:1446-1451. [PMID: 37769089 DOI: 10.1126/science.adh3875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/25/2023] [Indexed: 09/30/2023]
Abstract
The end-Cretaceous mass extinction was marked by both the Chicxulub impact and the ongoing emplacement of the Deccan Traps flood basalt province. To understand the mechanism of extinction, we must disentangle the timing, duration, and intensity of volcanic and meteoritic environmental forcings. In this study, we used a parallel Markov chain Monte Carlo approach to invert for carbon dioxide (CO2) and sulfur dioxide (SO2) emissions, export productivity, and remineralization from 67 to 65 million years ago using the LOSCAR (Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir) model. Our results closely match observed and proxy data and suggest decoupled CO2 and SO2 emissions, a two-step decline in export productivity with a protracted recovery, and no clear volatile impulse at the boundary. More broadly, our methods provide a potential path forward for efficient parallel inversion of complex Earth system models.
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Affiliation(s)
- Alexander A Cox
- Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - C Brenhin Keller
- Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
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35
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Settem M, Roncaglia C, Ferrando R, Giacomello A. Structural transformations in Cu, Ag, and Au metal nanoclusters. J Chem Phys 2023; 159:094303. [PMID: 37668252 DOI: 10.1063/5.0159257] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/15/2023] [Indexed: 09/06/2023] Open
Abstract
Finite-temperature structures of Cu, Ag, and Au metal nanoclusters are calculated in the entire temperature range from 0 K to melting using a computational methodology that we proposed recently [M. Settem et al., Nanoscale 14, 939 (2022)]. In this method, Harmonic Superposition Approximation (HSA) and Parallel Tempering Molecular Dynamics (PTMD) are combined in a complementary manner. HSA is accurate at low temperatures and fails at higher temperatures. PTMD, on the other hand, effectively samples the high temperature region and melts. This method is used to study the size- and system-dependent competition between various structural motifs of Cu, Ag, and Au nanoclusters in the size range 1-2 nm. Results show that there are mainly three types of structural changes in metal nanoclusters, depending on whether a solid-solid transformation occurs. In the first type, the global minimum is the dominant motif in the entire temperature range. In contrast, when a solid-solid transformation occurs, the global minimum transforms either completely to a different motif or partially, resulting in the co-existence of multiple motifs. Finally, nanocluster structures are analyzed to highlight the system-specific differences across the three metals.
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Affiliation(s)
- Manoj Settem
- Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, via Eudossiana 18, 00184 Roma, Italy
| | - Cesare Roncaglia
- Dipartimento di Fisica dell'Università di Genova, via Dodecaneso 33, 16146 Genova, Italy
| | - Riccardo Ferrando
- Dipartimento di Fisica dell'Università di Genova and CNR-IMEM, via Dodecaneso 33, 16146 Genova, Italy
| | - Alberto Giacomello
- Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, via Eudossiana 18, 00184 Roma, Italy
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36
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Cheung DL. Aggregation of an Amyloidogenic Peptide on Gold Surfaces. Biomolecules 2023; 13:1261. [PMID: 37627326 PMCID: PMC10452923 DOI: 10.3390/biom13081261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Solid surfaces have been shown to affect the aggregation and assembly of many biomolecular systems. One important example is the formation of protein fibrils, which can occur on a range of biological and synthetic surfaces. The rate of fibrillation depends on both the protein structure and the surface chemistry, with the different molecular and oligomer structures adopted by proteins on surfaces likely to be crucial. In this paper, the aggregation of the model amyloidogenic peptide, Aβ(16-22), corresponding to a hydrophobic segment of the amyloid beta protein on a gold surface is studied using molecular dynamics simulation. Previous simulations of this peptide on gold surfaces have shown that it adopts conformations on surfaces that are quite different from those in bulk solution. These simulations show that this then leads to significant differences in the oligomer structures formed in solution and on gold surfaces. In particular, oligomers formed on the surface are low in beta-strands so are unlike the structures formed in bulk solution. When oligomers formed in solution adsorb onto gold surfaces they can then restructure themselves. This can then help explain the inhibition of Aβ(16-22) fibrillation by gold surfaces and nanoparticles seen experimentally.
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Affiliation(s)
- David L Cheung
- School of Biological and Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
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37
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Especial JNC, Faísca PFN. Effects of sequence-dependent non-native interactions in equilibrium and kinetic folding properties of knotted proteins. J Chem Phys 2023; 159:065101. [PMID: 37551809 DOI: 10.1063/5.0160886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/24/2023] [Indexed: 08/09/2023] Open
Abstract
Determining the role of non-native interactions in folding dynamics, kinetics, and mechanisms is a classic problem in protein folding. More recently, this question has witnessed a renewed interest in light of the hypothesis that knotted proteins require the assistance of non-native interactions to fold efficiently. Here, we conduct extensive equilibrium and kinetic Monte Carlo simulations of a simple off-lattice C-alpha model to explore the role of non-native interactions in the thermodynamics and kinetics of three proteins embedding a trefoil knot in their native structure. We find that equilibrium knotted conformations are stabilized by non-native interactions that are non-local, and proximal to native ones, thus enhancing them. Additionally, non-native interactions increase the knotting frequency at high temperatures, and in partially folded conformations below the transition temperatures. Although non-native interactions clearly enhance the efficiency of transition from an unfolded conformation to a partially folded knotted one, they are not required to efficiently fold a knotted protein. Indeed, a native-centric interaction potential drives the most efficient folding transition, provided that the simulation temperature is well below the transition temperature of the considered model system.
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Affiliation(s)
- João N C Especial
- Departamento de Física, Faculdade de Ciências, Ed. C8, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
- BioISI-Biosystems and Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
| | - Patrícia F N Faísca
- Departamento de Física, Faculdade de Ciências, Ed. C8, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
- BioISI-Biosystems and Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal
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38
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Kilic Z, Schweiger M, Moyer C, Pressé S. Monte Carlo samplers for efficient network inference. PLoS Comput Biol 2023; 19:e1011256. [PMID: 37463156 PMCID: PMC10353823 DOI: 10.1371/journal.pcbi.1011256] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data's structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.
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Affiliation(s)
- Zeliha Kilic
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Max Schweiger
- Center for Biological Physics, ASU, Tempe, Arizona, United States of America
- Department of Physics ASU, Tempe, Arizona, United States of America
| | - Camille Moyer
- Center for Biological Physics, ASU, Tempe, Arizona, United States of America
- School of Mathematics and Statistical Sciences, ASU, Tempe, Arizona, United States of America
| | - Steve Pressé
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
- Center for Biological Physics, ASU, Tempe, Arizona, United States of America
- School of Molecular Sciences, ASU, Tempe, Arizona, United States of America
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39
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Alamdari S, Torkelson K, Wang X, Chen CL, Ferguson AL, Pfaendtner J. Thermodynamic Basis for the Stabilization of Helical Peptoids by Chiral Sidechains. J Phys Chem B 2023. [PMID: 37379071 DOI: 10.1021/acs.jpcb.3c01913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Peptoids are a class of highly customizable biomimetic foldamers that retain properties from both proteins and polymers. It has been shown that peptoids can adopt peptide-like secondary structures through the careful selection of sidechain chemistries, but the underlying conformational landscapes that drive these assemblies at the molecular level remain poorly understood. Given the high flexibility of the peptoid backbone, it is essential that methods applied to study peptoid secondary structure formation possess the requisite sensitivity to discriminate between structurally similar yet energetically distinct microstates. In this work, a generalizable simulation scheme is used to robustly sample the complex folding landscape of various 12mer polypeptoids, resulting in a predictive model that links sidechain chemistry with preferential assembly into one of 12 accessible backbone motifs. Using a variant of the metadynamics sampling method, four peptoid dodecamers are simulated in water: sarcosine, N-(1-phenylmethyl)glycine (Npm), (S)-N-(1-phenylethyl)glycine (Nspe), and (R)-N-(1-phenylethyl)glycine (Nrpe)─to determine the underlying entropic and energetic impacts of hydrophobic and chiral peptoid sidechains on secondary structure formation. Our results indicate that the driving forces to assemble Nrpe and Nspe sequences into polyproline type-I helices in water are found to be enthalpically driven, with small benefits from an entropic gain for isomerization and steric strain due to the presence of the chiral center. The minor entropic gains from bulky chiral sidechains in Nrpe- and Nspe-containing peptoids can be explained through increased configurational entropy in the cis state. However, overall assembly into a helix is found to be overall entropically unfavorable. These results highlight the importance of considering the many various competing interactions in the rational design of peptoid secondary structure building blocks.
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Affiliation(s)
- Sarah Alamdari
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Kaylyn Torkelson
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Xiaoqian Wang
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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40
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Forero-Martinez NC, Cortes-Huerto R, Ward L, Ballone P. Water Harvesting by Thermoresponsive Ionic Liquids: A Molecular Dynamics Study of the Water Absorption Kinetics and of the Role of Nanostructuring. J Phys Chem B 2023. [PMID: 37267503 DOI: 10.1021/acs.jpcb.3c01655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Ionic liquids (ILs) whose water solutions are thermoresponsive provide an appealing route to harvest water from the atmosphere at an energy cost that can be accessed by solar heating. IL/water solutions that present a lower critical solution temperature (LCST), i.e., demix upon increasing temperature, represent the most promising choice for this task since they could absorb vapor during the night when its saturation is highest and release liquid water during the day. The kinetics of water absorption at the surface and the role of nanostructuring in this process have been investigated by atomistic molecular dynamics simulations for the ionic liquid tetrabutyl phosphonium 2,4-dimethylbenzenesulfonate whose LCST in water occurs at Tc = 36 °C for solutions of 50-50 wt % composition. The simulation results show that water molecules are readily adsorbed on the IL and migrate along the surface to form thick three-dimensional islands. On a slightly longer time scale, ions crawl on these islands, covering water and recreating the original surface whose free energy is particularly low. At a high deposition rate, this mechanism allows the fast incorporation of large amounts of water, producing subsurface water pockets that eventually merge into the populations of water-rich and IL-rich domains in the nanostructured bulk. Simulation results suggest that strong nanostructuring could ease the separation of water and water-contaminated IL phases even before macroscopic demixing.
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Affiliation(s)
- Nancy C Forero-Martinez
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany
- Max-Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | | | - Lainey Ward
- School of Physics, University College Dublin, UCD Belfield Campus, D04V1W8 Dublin 4, Ireland
| | - Pietro Ballone
- School of Physics, University College Dublin, UCD Belfield Campus, D04V1W8 Dublin 4, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, UCD Belfield Campus, D04V1W8 Dublin 4, Ireland
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41
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Ghorai S, Nandi M, Chaudhury P. Impurity effects on phase change in Lennard-Jones atomic clusters. J CHEM SCI 2023; 135:35. [DOI: 10.1007/s12039-023-02156-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/16/2023] [Accepted: 02/19/2023] [Indexed: 07/19/2023]
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42
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Kuriata A, Sikorski A. Structure of adsorbed linear and cyclic block copolymers: A computer simulation study. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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43
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Rahimi K, Piaggi PM, Zerze GH. Comparison of On-the-Fly Probability Enhanced Sampling and Parallel Tempering Combined with Metadynamics for Atomistic Simulations of RNA Tetraloop Folding. J Phys Chem B 2023. [PMID: 37196167 DOI: 10.1021/acs.jpcb.3c00117] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Atomistic simulations with reliable models are extremely useful in providing exquisitely detailed pictures of biomolecular phenomena that are not always accessible to experiments. One such biomolecular phenomenon is RNA folding, which often requires exhaustive simulations with combined advanced sampling techniques. In this work, we employed the multithermal-multiumbrella on-the-fly probability enhanced sampling (MM-OPES) technique and compared it against combined parallel tempering and metadynamics simulations. We found that MM-OPES simulations were successful in reproducing the free energy surfaces from combined parallel tempering and metadynamics simulations. Importantly, we also investigated a wide range of temperature sets (minimum and maximum temperatures) for MM-OPES simulations in order to identify some guidelines for deciding the temperature limits for an accurate and efficient exploration of the free energy landscapes. We found that most temperature sets yielded almost the same accuracy in reproducing the free energy surface at the ambient conditions as long as (i) the maximum temperature is reasonably high, (ii) the temperature at which we run the simulation is reasonably high (in our simulations, running temperature is defined as [minimum temperature + maximum temperature]/2), and (iii) the effective sample size at the temperature of interest is statistically reasonable. In terms of the computational cost, all MM-OPES simulations were nearly 4 times less costly than the combined parallel tempering and metadynamics simulations. We concluded that the demanding combined parallel tempering and metadynamics simulations can safely be replaced with approximately 4 times less costly MM-OPES simulations (with carefully selected temperature limits) to obtain the same information.
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Affiliation(s)
- Kosar Rahimi
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Pablo M Piaggi
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Gül H Zerze
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
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44
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Lüking M, van der Spoel D, Elf J, Tribello GA. Can molecular dynamics be used to simulate biomolecular recognition? J Chem Phys 2023; 158:2889489. [PMID: 37158325 DOI: 10.1063/5.0146899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/19/2023] [Indexed: 05/10/2023] Open
Abstract
There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. In particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. Many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation.
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Affiliation(s)
- Malin Lüking
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, SE-75124 Uppsala, Sweden
| | - David van der Spoel
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, SE-75124 Uppsala, Sweden
| | - Johan Elf
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, SE-75124 Uppsala, Sweden
| | - Gareth A Tribello
- Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, United Kingdom
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45
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Fram B, Truebridge I, Su Y, Riesselman AJ, Ingraham JB, Passera A, Napier E, Thadani NN, Lim S, Roberts K, Kaur G, Stiffler M, Marks DS, Bahl CD, Khan AR, Sander C, Gauthier NP. Simultaneous enhancement of multiple functional properties using evolution-informed protein design. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.09.539914. [PMID: 37214973 PMCID: PMC10197589 DOI: 10.1101/2023.05.09.539914] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Designing optimized proteins is important for a range of practical applications. Protein design is a rapidly developing field that would benefit from approaches that enable many changes in the amino acid primary sequence, rather than a small number of mutations, while maintaining structure and enhancing function. Homologous protein sequences contain extensive information about various protein properties and activities that have emerged over billions of years of evolution. Evolutionary models of sequence co-variation, derived from a set of homologous sequences, have proven effective in a range of applications including structure determination and mutation effect prediction. In this work we apply one of these models (EVcouplings) to computationally design highly divergent variants of the model protein TEM-1 β-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays. Nearly all designed variants were functional, including one with 84 mutations from the nearest natural homolog. Surprisingly, all functional designs had large increases in thermostability and most had a broadening of available substrates. These property enhancements occurred while maintaining a nearly identical structure to the wild type enzyme. Collectively, this work demonstrates that evolutionary models of sequence co-variation (1) are able to capture complex epistatic interactions that successfully guide large sequence departures from natural contexts, and (2) can be applied to generate functional diversity useful for many applications in protein design.
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Affiliation(s)
- Benjamin Fram
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ian Truebridge
- Institute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA
- Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School; Boston, MA, USA
- current address: AI Proteins; Boston, MA, USA
| | - Yang Su
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Adam J. Riesselman
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Program in Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - John B. Ingraham
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Alessandro Passera
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- current address: Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Eve Napier
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
| | - Nicole N. Thadani
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuel Lim
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Kristen Roberts
- Selux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA
| | - Gurleen Kaur
- Selux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA
| | - Michael Stiffler
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Debora S. Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Christopher D. Bahl
- Institute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA
- Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School; Boston, MA, USA
- current address: AI Proteins; Boston, MA, USA
| | - Amir R. Khan
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
- Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA, USA
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Nicholas P. Gauthier
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
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46
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Białas P, Czarnota P, Korcyl P, Stebel T. Simulating first-order phase transition with hierarchical autoregressive networks. Phys Rev E 2023; 107:054127. [PMID: 37329036 DOI: 10.1103/physreve.107.054127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/04/2023] [Indexed: 06/18/2023]
Abstract
We apply the hierarchical autoregressive neural network sampling algorithm to the two-dimensional Q-state Potts model and perform simulations around the phase transition at Q=12. We quantify the performance of the approach in the vicinity of the first-order phase transition and compare it with that of the Wolff cluster algorithm. We find a significant improvement as far as the statistical uncertainty is concerned at a similar numerical effort. In order to efficiently train large neural networks we introduce the technique of pretraining. It allows us to train some neural networks using smaller system sizes and then employ them as starting configurations for larger system sizes. This is possible due to the recursive construction of our hierarchical approach. Our results serve as a demonstration of the performance of the hierarchical approach for systems exhibiting bimodal distributions. Additionally, we provide estimates of the free energy and entropy in the vicinity of the phase transition with statistical uncertainties of the order of 10^{-7} for the former and 10^{-3} for the latter based on a statistics of 10^{6} configurations.
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Affiliation(s)
- Piotr Białas
- Institute of Applied Computer Science, Jagiellonian University, 30-348 Kraków, Poland
| | - Paulina Czarnota
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Piotr Korcyl
- Institute of Theoretical Physics, Jagiellonian University, 30-348 Kraków, Poland
| | - Tomasz Stebel
- Institute of Theoretical Physics, Jagiellonian University, 30-348 Kraków, Poland
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47
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Alizadeh Sahraei A, Mejia Bohorquez B, Tremblay D, Moineau S, Garnier A, Larachi F, Lagüe P. Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes. ACS APPLIED MATERIALS & INTERFACES 2023; 15:17922-17937. [PMID: 37010879 PMCID: PMC10103053 DOI: 10.1021/acsami.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/16/2023] [Indexed: 06/19/2023]
Abstract
Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na+ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.
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Affiliation(s)
- Abolfazl Alizadeh Sahraei
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Barbara Mejia Bohorquez
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Denise Tremblay
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Sylvain Moineau
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Alain Garnier
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Faïçal Larachi
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Patrick Lagüe
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
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48
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Shmilovich K, Ferguson AL. Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables. J Phys Chem A 2023; 127:3497-3517. [PMID: 37036804 DOI: 10.1021/acs.jpca.3c00505] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.
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Affiliation(s)
- Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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49
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Abstract
A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed.
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Affiliation(s)
- Kristen A Fichthorn
- Department of Chemical Engineering and Department of Physics The Pennsylvania State University University Park, Pennsylvania 16803 United States
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50
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Koskin V, Kells A, Clayton J, Hartmann AK, Annibale A, Rosta E. Variational kinetic clustering of complex networks. J Chem Phys 2023; 158:104112. [PMID: 36922127 DOI: 10.1063/5.0105099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Efficiently identifying the most important communities and key transition nodes in weighted and unweighted networks is a prevalent problem in a wide range of disciplines. Here, we focus on the optimal clustering using variational kinetic parameters, linked to Markov processes defined on the underlying networks, namely, the slowest relaxation time and the Kemeny constant. We derive novel relations in terms of mean first passage times for optimizing clustering via the Kemeny constant and show that the optimal clustering boundaries have equal round-trip times to the clusters they separate. We also propose an efficient method that first projects the network nodes onto a 1D reaction coordinate and subsequently performs a variational boundary search using a parallel tempering algorithm, where the variational kinetic parameters act as an energy function to be extremized. We find that maximization of the Kemeny constant is effective in detecting communities, while the slowest relaxation time allows for detection of transition nodes. We demonstrate the validity of our method on several test systems, including synthetic networks generated from the stochastic block model and real world networks (Santa Fe Institute collaboration network, a network of co-purchased political books, and a street network of multiple cities in Luxembourg). Our approach is compared with existing clustering algorithms based on modularity and the robust Perron cluster analysis, and the identified transition nodes are compared with different notions of node centrality.
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Affiliation(s)
- Vladimir Koskin
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Adam Kells
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Joe Clayton
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
| | | | - Alessia Annibale
- Department of Mathematics, King's College London, SE11 6NJ London, United Kingdom
| | - Edina Rosta
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
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