kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Combining Evolution and Physics through Machine Learning to Decipher Molecular Mechanisms
KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-3219-1062
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

From E.coli to elephants, the cells of all living organisms are surrounded by a near impenetrable wall of lipids. The windows through the walls are membrane proteins - receptors, transporters and channels that confer communication, information and metabolites through the membrane. Without opening holes in the membrane, it is necessary for these proteins to alter their shapes by cycling between conformational states to transport signals or molecules. Owing to their important role as information bottle-necks, changes in their function can lead to cancer, infectious diseases, or metabolic disorders. Hence, they are important targets for drug discovery, therapeutic research and understanding the human body.

Due to the delicate thermodynamic balance of conformational states of these proteins that are modulated by external stimuli, it is difficult to trap them in experimental setups in which their native states are captured. To add to the problematic nature of their molecular mechanisms, they are too fast to kinetically trap in a certain state long enough to observe without breaking the molecular mechanism. Fast moving mechanisms makes them a good target for molecular dynamics (MD) simulations, where the movement of all atoms in the proteins is simulated over time. Although a powerful tool, modern MD simulations are not able to access long enough timescales to accurately measure macroscopic functionally relevant information, leaving a gap between simulations and reality in which many conclusions made with atomic resolution fail to translate into macroscopic phenomena, such as receptor activity, transport efficiency, mutational stability or allosteric signalling.

This work presents novel methodology that efficiently discovers and explores functionally relevant conformational states using MD simulations. By combining evolutionary information with physics using machine learning, the methodology accelerates the sampling while retaining the details of the molecular mechanism and the thermodynamic information. Additionally, the work shows how the methodology is capable of bridging the gap in resolution between experiments and simulations through the in-silico measurement of macroscopic phenomena on a microscopic scale. Moreover, it uniquely presents a framework applied to 4 studies on different target proteins of different families in which conformational change occurs, and is able to independently relate them to different types of measurements.

Abstract [sv]

Från E.coli till elefanter är levande organismers celler omslutna av en näst-intill oigenomtränglig vägg av lipider. Fönstrena genom dessa väggar är membranproteiner - receptorer, transportörer och kanaler. Därigenom färdas signaler, information och metaboliter. Av proteinerna krävs att de måste kunna utföra dessa funktioner utan att lämna stora hål i cellerna. Evolutionen har således producerat protein som kan ändra form, eller strukturellt tillstånd som svar på externa signaler. Genom deras oklanderligt viktiga position som informationsbärande flaskhalsar är det också katastrofalt när det blir fel i deras mekanismer, vilket kan ge upphov till allt ifrån cancer till sjukdomar rörande ämnesomsättning. Därför är de också av oerhört intresse för läkemedelsutveckling, utveckling av terapeutiska strategier, eller helt enkelt för att förstå dessa hörnstenar i vår komplexa anatomi.

På grund av deras väl avvägda termodynamiska balans mellan strukturella tillstånd som dessutom är reglerade av externa signaler är det svårt att experimentellt fånga dessa flyktiga tillstånd och fortfarande bevara deras naturliga struktur. Som om det inte vore nog är deras molekylära mekanismer ofta alldeles för kortvariga för att kunna prepareras och sedan observeras. Som konsekvens har istället molekylärdynamiksimulering (MD), ett verktyg som simulerar hur varje enskild atom rör sig över tid, använts för att studera dynamiken i övergångarna mellan olika tillstånd. Trots enorma framsteg i högprestandaberäkningsvetenskap är det fortfarande svårt att nå de tidsskalorna i vilka de molekylära mekanismerna blir synliga, vilket lämnar ett stort gap mellan simuleringar och verkligheten. I det gapet faller ofta viktiga aspekter såsom receptoraktivitet, transporteffektivitet, mutationsstabilitet eller allosterisk signallering, som alla är viktiga att förstå för att kunna modulera dessa mekanismer.

Detta arbete presenterar nydanande teknik som på ett effektivt sätt upptäcker och utforskar det strukturella landskapet i vilket olika proteintillstånd ligger med hjälp av MD simuleringar. Genom att kombinera evolutionär information med fysik med hjälp av maskininlärning byggs en metod som accelerar tidsskalan för utforskandet men samtidigt bevarar de viktiga molekylära detaljerna. Dessutom visar arbetet hur metoden kan användas för att överbrygga det sistnämnda gapet mellan simuleringar och verkligheten genom att i datorn mäta storheter som förekommer på makroskopisk skala i laboratorieexperiment. Slutligen visar arbetet också 4 exempel på hur metoden gör detta på system som är av intresse för läkemedelsforskning, och tar reda på nya insikter kring dessa molekylära maskiner.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2024.
Series
TRITA-SCI-FOU ; 2024:23
Keywords [en]
Molecular Dynamics Simulation, Evolution, Enhanced Sampling, Machine Learning, Molecular Mechanism
Keywords [sv]
Molekyldynamiksimuleringar, Evolution, Accelererad utforskning, Maskininlärning, Molekylära mekanismer
National Category
Biophysics
Research subject
Biological Physics
Identifiers
URN: urn:nbn:se:kth:diva-345862ISBN: 978-91-8040-921-6 (print)OAI: oai:DiVA.org:kth-345862DiVA, id: diva2:1853732
Public defence
2024-05-15, FA32, Roslagtullsbacken 21, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 2024-04-23

Available from: 2024-04-23 Created: 2024-04-23 Last updated: 2024-05-15Bibliographically approved
List of papers
1. Reconstructing the transport cycle in the sugar porter superfamily using coevolution-powered machine learning
Open this publication in new window or tab >>Reconstructing the transport cycle in the sugar porter superfamily using coevolution-powered machine learning
Show others...
2023 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 12Article in journal (Refereed) Published
Abstract [en]

Sugar porters (SPs) represent the largest group of secondary-active transporters. Some members, such as the glucose transporters (GLUTs), are well known for their role in maintaining blood glucose homeostasis in mammals, with their expression upregulated in many types of cancers. Because only a few sugar porter structures have been determined, mechanistic models have been constructed by piecing together structural states of distantly related proteins. Current GLUT transport models are predominantly descriptive and oversimplified. Here, we have combined coevolution analysis and comparative modeling, to predict structures of the entire sugar porter superfamily in each state of the transport cycle. We have analyzed the state-specific contacts inferred from coevolving residue pairs and shown how this information can be used to rapidly generate free-energy landscapes consistent with experimental estimates, as illustrated here for the mammalian fructose transporter GLUT5. By comparing many different sugar porter models and scrutinizing their sequence, we have been able to define the molecular determinants of the transport cycle, which are conserved throughout the sugar porter superfamily. We have also been able to highlight differences leading to the emergence of proton-coupling, validating, and extending the previously proposed latch mechanism. Our computational approach is transferable to any transporter, and to other protein families in general.

Place, publisher, year, edition, pages
eLife Sciences Publications, Ltd, 2023
Keywords
membrane protein, molecular biophysics, molecular dynamics simulations, molecular modeling, none, structural biology, structure prediction, transporter
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-334624 (URN)10.7554/eLife.84805 (DOI)001071912700001 ()37405846 (PubMedID)2-s2.0-85164005539 (Scopus ID)
Note

QC 20231123

Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2024-04-23Bibliographically approved
2. Determinants of sugar-induced influx in the mammalian fructose transporter GLUT5
Open this publication in new window or tab >>Determinants of sugar-induced influx in the mammalian fructose transporter GLUT5
Show others...
2023 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 12, article id e84808Article in journal (Refereed) Published
Abstract [en]

In mammals, glucose transporters (GLUT) control organism-wide blood-glucose homeostasis. In human, this is accomplished by 14 different GLUT isoforms, that transport glucose and other monosaccharides with varying substrate preferences and kinetics. Nevertheless, there is little difference between the sugar-coordinating residues in the GLUT proteins and even the malarial Plasmodium falciparum transporter PfHT1, which is uniquely able to transport a wide range of different sugars. PfHT1 was captured in an intermediate 'occluded' state, revealing how the extracellular gating helix TM7b has moved to break and occlude the sugar-binding site. Sequence difference and kinetics indicated that the TM7b gating helix dynamics and interactions likely evolved to enable substrate promiscuity in PfHT1, rather than the sugar-binding site itself. It was unclear, however, if the TM7b structural transitions observed in PfHT1 would be similar in the other GLUT proteins. Here, using enhanced sampling molecular dynamics simulations, we show that the fructose transporter GLUT5 spontaneously transitions through an occluded state that closely resembles PfHT1. The coordination of D-fructose lowers the energetic barriers between the outward- and inward-facing states, and the observed binding mode for D-fructose is consistent with biochemical analysis. Rather than a substrate-binding site that achieves strict specificity by having a high affinity for the substrate, we conclude GLUT proteins have allosterically coupled sugar binding with an extracellular gate that forms the high-affinity transition-state instead. This substrate-coupling pathway presumably enables the catalysis of fast sugar flux at physiological relevant blood-glucose concentrations.

Place, publisher, year, edition, pages
eLife Sciences Publications, Ltd, 2023
Keywords
biochemistry, chemical biology, fructose transport, MD simulations, mechanism, S. cerevisiae
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-333242 (URN)10.7554/eLife.84808 (DOI)001024510300001 ()37405832 (PubMedID)2-s2.0-85163948061 (Scopus ID)
Note

QC 20230731

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2024-04-23Bibliographically approved
3. Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor
Open this publication in new window or tab >>Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor
2023 (English)In: Journal of Physical Chemistry B, ISSN 1520-6106, E-ISSN 1520-5207, Vol. 127, no 46, p. 9891-9904Article in journal (Refereed) Published
Abstract [en]

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the beta 2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the beta 2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the beta 2 adrenergic receptor.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2023
National Category
Theoretical Chemistry Biophysics
Identifiers
urn:nbn:se:kth:diva-342730 (URN)10.1021/acs.jpcb.3c04897 (DOI)001140917400001 ()37947090 (PubMedID)2-s2.0-85178112205 (Scopus ID)
Note

QC 20240213

Available from: 2024-02-13 Created: 2024-02-13 Last updated: 2024-04-23Bibliographically approved
4. The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics
Open this publication in new window or tab >>The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Membrane transporters play a fundamental role in the tissue distribution of endogenous compounds and xenobiotics and are major determinants of efficacy and side effects profiles. Polymorphisms within these drug transporters result in inter-individual variation in drug response, with some patients not responding to the recommended dosage of drug whereas others experience catastrophic side effects. For example, variants within the major hepatic Human organic cation transporter OCT1 (SLC22A1) can change endogenous organic cations and many prescription drug levels. To understand how variants mechanistically impact drug uptake, we systematically study how all known and possible single missense and single amino acid deletion variants impact expression and substrate uptake of OCT1. We find that human variants primarily disrupt function via folding rather than substrate uptake. Our study revealed that the major determinants of folding reside in the first 300 amino acids, including the first 6 transmembrane domains and the extracellular domain (ECD) with a stabilizing and highly conserved stabilizing helical motif making key interactions between the ECD and transmembrane domains. Using the functional data combined with computational approaches, we determine and validate a structure-function model of OCT1s conformational ensemble without experimental structures. Using this model and molecular dynamic simulations of key mutants, we determine biophysical mechanisms for how specific human variants alter transport phenotypes. We identify differences in frequencies of reduced function alleles across populations with East Asians vs European populations having the lowest and highest frequency of reduced function variants, respectively. Mining human population databases reveals that reduced function alleles of OCT1 identified in this study associate significantly with high LDL cholesterol levels. Our general approach broadly applied could transform the landscape of precision medicine by producing a mechanistic basis for understanding the effects of human mutations on disease and drug response.

National Category
Biophysics
Research subject
Biological Physics
Identifiers
urn:nbn:se:kth:diva-345861 (URN)
Funder
Göran Gustafsson Foundation for promotion of scientific research at Uppala University and Royal Institute of TechnologySwedish Research Council, 2019 - 02433Swedish National Infrastructure for Computing (SNIC)
Note

QC 20240508

Available from: 2024-04-23 Created: 2024-04-23 Last updated: 2024-05-08Bibliographically approved

Open Access in DiVA

Thesis Kappa(75372 kB)58 downloads
File information
File name SUMMARY01.pdfFile size 75372 kBChecksum SHA-512
c2ab0342acf433ee9406404786ff6533e116d99f067a48c199c8589ca478874ee7014bfd291057308750d2093acc507d2629d2ac5532ff204bbb1445359af4a1
Type summaryMimetype application/pdf

Authority records

Mitrovic, Darko

Search in DiVA

By author/editor
Mitrovic, Darko
By organisation
BiophysicsScience for Life Laboratory, SciLifeLab
Biophysics

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 710 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf