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Development of large-scale molecular and nanomaterial models
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för material- och miljökemi (MMK).ORCID-id: 0000-0001-5234-7848
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Molecular simulations can access unique atomic-scale information about new materials, pharmaceuticals, and biological environments, making cost-effective predictions and aiding experimental studies. They are particularly useful for describing the mechanisms of nanoscale phenomena and the biological/inorganic interfaces. However, the computational cost of molecular simulations increases with the size of the system as well as with the model complexity, which is related to the accuracy of the simulation. This thesis aims to develop efficient large-scale molecular models that capture important structural details of the atomistic simulations. In particular, we focus on the TiO2-lipid interface, which forms in the living cells, exposed to TiO2 nanomaterials, but is also relevant in the context of biomedical applications. We have studied the interface using atomistic molecular dynamics simulations and found that the characteristics of the lipid adsorption depend on the type of the TiO2 surface, lipid headgroup composition, and the presence of cholesterol. We then derive a coarse-grained molecular model of the TiO2-lipid interface to enable the large-scale simulations of TiO2 nanoparticles interacting with model cell membranes. We show that the strength of the lipid adsorption increases with the size of the nanoparticle and that a small TiO2 nanoparticle can become partially wrapped by a lipid membrane. To improve the transferability of the coarse-grained model, we design and test an artificial neural network that learns the interactions in coarse-grained water-methanol solutions from the structural data obtained in multiple reference simulations at atomistic resolution. We show that in the studied system, the neural network learns the many-body interactions and accurately reproduces the structural properties of the solution at different concentrations. 

Abstract [sv]

Molekylära simuleringar kan ge tillgång till unik information på atomnivå om nya material, läkemedel och biologiska miljöer, vilket gör det möjligt att göra kostnadseffektiva förutsägelser och underlätta experimentella studier. De är särskilt användbara för att beskriva mekanismerna för fenomen på nanoskala och de biologiska/oorganiska gränssnitten. Beräkningskostnaden för molekylära simuleringar ökar dock med systemets storlek såväl som med modellens komplexitet, vilket är relaterat till simuleringens noggrannhet. Den här avhandlingen syftar till att utveckla effektiva storskaliga molekylära modeller som fångar viktiga strukturella detaljer från de atomistiska simuleringarna. Särskilt fokuserar vi på gränssnittet mellan TiO2 och lipider, som bildas i levande celler, exponerade för TiO2-nanomaterial, men är också relevant inom biomedicinska tillämpningar. Vi har studerat gränssnittet med hjälp av atomistiska molekyldynamiksimuleringar och funnit att egenskaperna hos lipidadsorptionen beror på typen av TiO2-yta, sammansättningen av lipidhuvudgrupper och närvaron av kolesterol. Sedan härleder vi en grovkornig molekylär modell av TiO2-lipidgränssnittet för att möjliggöra storskaliga simuleringar av TiO2-nanopartiklar som interagerar med modellcellmembran. Vi visar att styrkan hos lipidadsorptionen ökar med nanopartiklens storlek och att en liten TiO2-nanopartikel delvis kan omslutas av ett lipidmembran. För att förbättra överförbarheten hos den grovkorniga modellen designar och testar vi ett artificiellt neuralt nätverk som lär sig interaktionerna i grovkorniga vatten-metanollösningar från strukturella data som erhållits i flera referenssimuleringar med atomär upplösning. Vi visar att i det studerade systemet lär sig det neurala nätverket flerkroppsinteraktioner och återger strukturegenskaperna hos lösningar med olika koncentrationer noggrant.

Abstract [ru]

Молекулярное моделирование может предоставить уникальную информацию с атомарным разрешением о новых материалах, лекарственных средствах и биологических средах, что делает его эффективным в прогнозировании и поддержке экспериментальных исследований. Особенной областью применения молекулярного моделирования является исследование процессов, происходящих на поверхности неорганических материалов, которые соприкосаются с биомолекулами. Однако, вычислительная сложность симуляций возрастает с размером моделируемой системы и сложностью модели, которая связана с точностью моделирования. Целью настоящей диссертации является разработка эффективных крупномасштабных молекулярных моделей, воспроизводящих ключевые особенности структуры молекулярных систем, полученных в ходе атомистического моделирования. В этой работе мы сосредотачиваемся на взаимодействии поверхности диоксида титана (TiO2) с молекулами липидов, которое возникает при попадании наночастиц TiO2 в живые клетки организма, что также является актуальным для биомедицинских исследований. Мы изучили взаимодействие с использованием атомистической молекулярной динамики и обнаружили, что адсорбция молекул липидов зависит от типа поверхности TiO2, функциональных групп в полярной части молекулы и присутствия холестерина. На основании полученных данных мы разработали грубозернистую молекулярную модель взаимодействия TiO2 с липидами для проведения крупномасштабных симуляций наночастиц диоксида титана, взаимодействующих с липидными мембранами, представляющих упрощённую структуру клеточных мембран. Наши симуляции показывают, что адсорбция липидов растёт вместе с радиусом наночастицы, а наночастица TiO2 с наименьшим радиусом оказывается лишь частично обёрнута липидной мембраной. Чтобы сделать полученные грубозернистые модели более универсальными, мы разработали и отладили нейронную сеть, которая учится воспроизводить взаимодействия в грубозернистых водно-метанольных растворах на основании структурных данных, полученных из нескольких атомистических симуляций. Мы демонстрируем, что в данной системе нейронная сеть учитывает многочастичные взаимодействия, что позволяет ей воспроизвести структурные свойства растворов разных концентраций с высокой точностью.  

sted, utgiver, år, opplag, sider
Stockholm: Department of Materials and Environmental Chemistry (MMK), Stockholm University , 2024. , s. 86
Emneord [en]
Molecular simulations, Coarse-grained models, Lipids, TiO2 surface, Machine learning
HSV kategori
Forskningsprogram
fysikalisk kemi
Identifikatorer
URN: urn:nbn:se:su:diva-227287ISBN: 978-91-8014-705-7 (tryckt)ISBN: 978-91-8014-706-4 (digital)OAI: oai:DiVA.org:su-227287DiVA, id: diva2:1843685
Disputas
2024-05-03, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B and online via Zoom, public link is available at the department website, Stockholm, 14:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2024-04-10 Laget: 2024-03-11 Sist oppdatert: 2024-03-22bibliografisk kontrollert
Delarbeid
1. Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium
Åpne denne publikasjonen i ny fane eller vindu >>Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium
Vise andre…
2020 (engelsk)Inngår i: Advanced Materials, ISSN 0935-9648, E-ISSN 1521-4095, Vol. 32, nr 47, artikkel-id 2003913Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

On a daily basis, people are exposed to a multitude of health-hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, this work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives. 

Emneord
advanced microscopies, adverse outcome pathways, disease prediction, material safety and health hazards, mode of action
HSV kategori
Identifikatorer
urn:nbn:se:su:diva-214205 (URN)10.1002/adma.202003913 (DOI)000579030900001 ()33073368 (PubMedID)2-s2.0-85092622114 (Scopus ID)
Tilgjengelig fra: 2023-01-26 Laget: 2023-01-26 Sist oppdatert: 2024-03-11bibliografisk kontrollert
2. Atomistic Molecular Dynamics Simulations of Lipids Near TiO2 Nanosurfaces
Åpne denne publikasjonen i ny fane eller vindu >>Atomistic Molecular Dynamics Simulations of Lipids Near TiO2 Nanosurfaces
2021 (engelsk)Inngår i: Journal of Physical Chemistry B, ISSN 1520-6106, E-ISSN 1520-5207, Vol. 125, nr 29, s. 8048-8059Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Understanding of interactions between inorganic nanomaterials and biomolecules, and particularly lipid bilayers, is crucial in many biotechnological and biomedical applications, as well as for the evaluation of possible toxic effects caused by nanoparticles. Here, we present a molecular dynamics study of adsorption of two important constituents of the cell membranes, 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), lipids to a number of titanium dioxide planar surfaces, and a spherical nanoparticle under physiological conditions. By constructing the number density profiles of the lipid headgroup atoms, we have identified several possible binding modes and calculated their relative prevalence in the simulated systems. Our estimates of the adsorption strength, based on the total fraction of adsorbed lipids, show that POPE binds to the selected titanium dioxide surfaces stronger than DMPC, due to the ethanolamine group forming hydrogen bonds with the surface. Moreover, while POPE shows a clear preference toward anatase surfaces over rutile, DMPC has a particularly high affinity to rutile(101) and a lower affinity to other surfaces. Finally, we study how lipid concentration, addition of cholesterol, as well as titanium dioxide surface curvature may affect overall adsorption.

HSV kategori
Identifikatorer
urn:nbn:se:su:diva-197051 (URN)10.1021/acs.jpcb.1c04547 (DOI)000680434200013 ()34269053 (PubMedID)
Tilgjengelig fra: 2021-09-27 Laget: 2021-09-27 Sist oppdatert: 2024-03-11bibliografisk kontrollert
3. Development of a bottom-up coarse-grained model for interactions of lipids with TiO2 nanoparticles
Åpne denne publikasjonen i ny fane eller vindu >>Development of a bottom-up coarse-grained model for interactions of lipids with TiO2 nanoparticles
2024 (engelsk)Inngår i: Journal of Computational Chemistry, ISSN 0192-8651, E-ISSN 1096-987X, Vol. 45, nr 16, s. 1364-1379Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO2) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO2 surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO2 nanoparticles (𝑟=2 nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO2 nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial–lipid structures.

Emneord
coarse-graining, inverse Monte Carlo, lipid membrane, nanotoxicity, titanium dioxide
HSV kategori
Identifikatorer
urn:nbn:se:su:diva-227228 (URN)10.1002/jcc.27310 (DOI)001173652500001 ()38380763 (PubMedID)2-s2.0-85186417241 (Scopus ID)
Tilgjengelig fra: 2024-03-06 Laget: 2024-03-06 Sist oppdatert: 2024-05-08bibliografisk kontrollert
4. Coarse-Grained Modeling Using Neural Networks Trained on Structural Data
Åpne denne publikasjonen i ny fane eller vindu >>Coarse-Grained Modeling Using Neural Networks Trained on Structural Data
2023 (engelsk)Inngår i: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 19, nr 19, s. 6704-6717Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set.

HSV kategori
Identifikatorer
urn:nbn:se:su:diva-223178 (URN)10.1021/acs.jctc.3c00516 (DOI)001069923500001 ()37712507 (PubMedID)
Tilgjengelig fra: 2023-10-26 Laget: 2023-10-26 Sist oppdatert: 2024-03-11bibliografisk kontrollert

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