Group Leader 2019-
Habilitation: "Determination of transmembrane protein structure and function considering their evolutionary diversity", Faculty of Chemistry, University of Warsaw.
Modelowanie procesów komórkowych
Białka błonowe stanowią 20-30 % ludzkiego genomu. Stanowią trudny obiekt do badań eksperymentalnych i w związku z tym jest ich znacznie mniej w Protein Data Bank niż białek globularnych. Biologia obliczeniowa stanowi uzupełnienie badań eksperymentalnych a w wielu przypadkach je zastępuje - właśnie w przypadku białek błonowych. Zajmujemy się przewidywaniem struktur białek transbłonowych metodami modelowania porównawczego i de novo, badaniem dynamiki i mechanizmów ich działania oraz projektowaniem dla nich substancji o znaczeniu farmakologicznym oraz ich optymalizacją.
Zapraszamy studentów do realizacji prac dyplomowych i doktorskich. Przykładowe tematy prac licencjackich i magisterskich: "Mechanizmy aktywacji receptora sekretynowego", "Odróżnianie agonistów od antagonistów receptorów GPCR", "Projektowanie nowych substancji czynnych modulujących działanie receptora kalcytoninowego", "Potencjały statystyczne w modelowaniu białek transbłonowych", "Badanie dynamiki kompleksu ligand-receptor na przykładzie receptora glukagonowego", "Aplikacje internetowe w modelowaniu receptorów GPCR", "Badanie mechanizmów transportu przez błonę komórkową na przykładzie rodziny transporterów nukleozydowych".
Projektowanie Leków
INPHARMA
Dynamika Molekularna
VPAC1 receptor
Modelowanie przez Homologię
GPCRM
Tworzenie Oprogramowania
Receptory GPCR
OPUS NCN 2020/39/B/NZ2/00584
GPCR receptors constitute large and diverse group of drug targets. This project is aimed at design of self-learning systems that are fitted to specific GPCR pharmacology. Such systems will decrease the number of compounds that need to be tested experimentally in a pre-clinical phase of drug discovery. This project involves ligand-based and structure-based approaches to drug design. As a result, data on the actual receptor response for a ligand together with its physicochemical properties can be combined with the structural description of the ligand-receptor complex.
If you are interested in participation send your CV to dlatek@chem.uw.edu.pl.
Modeling of cellular processes
GPCR receptors
Homology modeling of GPCR structures & drug design
Activation switches
SLC transporters
De novo structure prediction & pharmacogenomics
SLC28
Protein Structure Prediction
Model building from multiple templates & sequence profiles comparison
GPCRM, GPCRDOCK 2013, De novo structure prediction, NMR-guided modeling
Molecular Dynamics
Dynamics of biological processes on a molecular level
S1P1, FPR1, class B GPCRs, VPAC1, V-ATPase
Drug Discovery
Structure-based drug design & lead optimization Glucagon receptors, VPAC1, V-ATPase, INPHARMA - NMR
Software for Computational Biology
We are not only Users but we actively contribute to scientific software. We developed two web applications for modeling of G protein-coupled receptors: GUT-DOCK & GPCRM and one standalone program for using sparse NMR data in protein structure prediction - CABS-NMR and contributed to INPHARMA. CABS-CONT was the early usage of interresidual contacts predicted from, e.g., correlated mutations, MSA or HMMs, in protein folding.
Our recent project is focused on: Resources for GPCR research.
Scripts for processing of MD trajectories.
Molecular dynamics used to observe activation of chemotactic receptors: simulations data for C5aR1 (stable interactions with Gi protein subunits) and C5aR2 (unstable).
GUT-DOCK (virtual screening for class B GPCR receptors) has moved from gut-dock.miningmembrane.com to https://gut-dock.chem.uw.edu.pl
Dorota Latek, PhD, DSc
For recent publications visit: Google ScholarSONATA - Principle Investigator 2013-2018
"Structural studies based on the low sequence similarity of G-protein coupled receptors activated by hormones and allosteric modulators.", National Science Centre in Poland, grant no DEC-2012/07/D/NZ1/04244
Post-doc 2010-2012
Post-doc in the Laboratory of Prof. Filipek in the International Institute of Molecular and Cell Biology in Warsaw. Short-term post-doc fellowships: EMBL-Heidelberg, University of California San Francisco.
PhD Studies 2004-2009
PhD studies in the Laboratory of Prof. Kolinski, Faculty of Chemistry, University of Warsaw. PhD thesis title: "Protein structure determination using sparse experimental data", MNiSW grant for PhD students: „Modeling of protein structures based on fragmentary data from simple NMR experiments”.
Master Degree 2004
Supervisor: Prof. A. Kolinski, Faculty of Chemistry, University of Warsaw.
Recent
P. Dragan, K. Joshi, A. Atzei, D. Latek Keras/TensorFlow in Drug Design for Immunity Disorders. Int. J. Mol. Sci. 2023, 24, 15009. https://doi.org/10.3390/ijms241915009
P. Dragan, M. Merski, S. Wisniewski, S.G. Sanmukh, D. Latek Chemokine Receptors - Structure-Based Virtual Screening Assisted by Machine Learning. Pharmaceutics 2023, 15(2), 516; https://doi.org/10.3390/pharmaceutics15020516
SG Sanmukh, N Jose dos Santos, C Nascimento Barquilha, M de Carvalho, P Pintor dos Reis, FK Delella, HF Carvalho, D Latek, T Feher, SL Felisbino. Oncology Letters 25.2 (2023): 86. https://doi.org/10.3892/ol.2023.13672
Dragan P, Atzei A, Sanmukh SG, Latek D. Computational and experimental approaches to probe GPCR activation and signaling. Prog Mol Biol Transl Sci. 2022;193(1):1-36. doi:10.1016/bs.pmbts.2022.06.001.
S. Wisniewski; P. Dragan; A. Makal; D. Latek Helix 8 in chemotactic receptors of the complement system. PLoS Comput Biol 2022, 18(7):e1009994. https://doi.org/10.1371/journal.pcbi.1009994 Additional data available.
Langer, I.; Jeandriens, J.; Couvineau, A.; Sanmukh, S.; Latek, D. Signal Transduction by VIP and PACAP Receptors. Biomedicines 2022, 10, 406. https://doi.org/10.3390/biomedicines100204061
I. Langer, D. Latek. Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors. Frontiers in Endocrinology 2021, 12:711906. https://doi.org/10.3389/fendo.2021.711906 in Front. Endocrinol. Sec. Cellular Endocrinology, Vol. 14: 'Peptide-binding GPCRs coming of age' https://doi.org/10.3389/fendo.2023.1189508
M. Mizera, D. Latek. Ligand-receptor interactions and machine learning in GCGR and GLP-1R drug discovery. Int. J. Mol. Sci. 2021, 22(8), 4060.
GPCRs
I. Rodriguez-Espigares et al. GPCRmd uncovers the dynamics of the 3D-GPCRome. Nature Methods 2020, 17, 777-787.
M. Mizera, D. Latek, J. Cielecka-Piontek. Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2. Int. J. Mol. Sci. 2020, 21(15), 5308.
D Latek*, I Langer, KA Krzysko, L Charzewski. A Molecular Dynamics Study of Vasoactive Intestinal Peptide Receptor 1 and the Basis of Its Therapeutic Antagonism. Int. J. Mol. Sci. 2019, 20, 4348.
Latek D*, Rutkowska E, Niewieczerzal S, Cielecka-Piontek J. Drug-induced diabetes type 2: In silico study involving class B GPCRs. PLoS ONE 2019 14(1): e0208892. https://doi.org/10.1371/journal.pone.0208892
Miszta P, Pasznik P, Jakowiecki J, Sztyler A, Latek D, Filipek S*. GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models. Nucleic Acids Res. 2018;46(W1):W387‐W395. doi:10.1093/nar/gky429
Latek D*, Bajda M, Filipek S*. A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors, J Chem Inf Model 2016, 56(4), 630-641, DOI: 10.1021/acs.jcim.5b00451
Yuan S, Wu R, Latek D, Trzaskowski B, Filipek S*. Lipid Receptor S1P1 Activation Scheme Concluded from Microsecond All-Atom Molecular Dynamics Simulations. PLoS Comput Biol 2013, 9(10): e1003261. doi:10.1371/journal.pcbi.1003261.
Latek D*, Pasznik P, Carlomagno T & Filipek S*. Towards Improved Quality of GPCR Models by Usage of Multiple Templates and Profile-Profile Comparison. PLOS ONE 2013, 8, e56742, doi: 10.1371/journal.pone.0056742.
Yuan S, Ghoshdastider U, Trzaskowski B, Latek D, Debinski A, Pulawski W, Wu R, Gerke V & Filipek S*. The role of water in activation mechanism of human N-formyl peptide receptor 1 (FPR1) based on molecular dynamics simulations. PLOS ONE 2012, 7, e47114, doi: 10.1371/journal.pone.0047114.
Latek D, Kolinski M, Ghoshdastider U, Debinski A, Bombolewski R, Plazinska A, Jozwiak K & Filipek S*. Modeling of ligand binding to G protein coupled receptors: cannabinoid CB1, CB2 and adrenergic beta 2 AR. J Mol Model 2011, 17, 2353-2366, doi:10.1007/s00894-011-0986-7.
Latek D, Modzelewska A, Trzaskowski B, Palczewski K & Filipek S*. G protein-coupled receptors--recent advances. Acta Biochim Pol 2012, 59, 515-529.
Trzaskowski B, Latek D, Yuan S, Ghoshdastider U, Debinski A & Filipek S*. Action of molecular switches in GPCRs--theoretical and experimental studies. Curr Med Chem 2012, 19, 1090-1109.
Other transmembrane proteins
Latek D. Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases, BMC Structural Biology 2017, 17:8, DOI: 10.1186/s12900-017-0078-8.
Dreisigacker S, Latek D, Bockelmann S, Huss M, Wieczorek H, Filipek S, Gohlke H, Menche D & Carlomagno T*. Understanding the inhibitory effect of highly potent and selective archazolides binding to the vacuolar ATPase. J Chem Inf Model 2012, 52, 2265-2272, doi: 10.1021/ci300242d.
NMR
Latek D* & Kolinski A. CABS-NMR--De novo tool for rapid global fold determination from chemical shifts, residual dipolar couplings and sparse methyl-methyl NOEs. J Comput Chem 2011, 32, 536-544, doi: 10.1002/jcc.21640.
Latek D*, Ekonomiuk D & Kolinski A. Protein structure prediction: combining de novo modeling with sparse experimental data. J Comput Chem 2007, 28, 1668-1676, doi: 10.1002/jcc.20657.
Codutti L, Skjaerven L, Angelini A, Grimaldi M, Latek D, Monecke P, Dreyer M & Carlomagno T*. Accounting for conformational variability in protein-ligand docking with NMR-guided rescoring. J Am Chem Soc 2013, 135(15), 5819-27.
Recruitment
Oferowane wykłady
Materiały do zajęć
D Latek, B Trzaskowski, S Niewieczerzał, P Miszta, K Młynarczyk, A Dębiński, W Puławski, S Yuan, A Sztyler, U Orzeł, J Jakowiecki, S Filipek*. Modeling of Membrane Proteins. In: Liwo A. (eds) Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes. Springer Series on Bio- and Neurosystems, 2019, vol 8. Springer, Cham
Gront D, Latek D, Kurcinski M & Kolinski A*. (2008) Template-Free Predictions of Three-Dimensional Protein Structures: From First Principles to Knowledge-Based Potentials. In Prediction of Protein Structures, Functions, and Interactions, pp. 117-141. John Wiley & Sons, Ltd.
Kufareva I, Katritch V, Participants of GPCR Dock 2013, Stevens RCS, Abagyan R. (2014) Advances in GPCR Modeling Evaluated by the GPCR Dock 2013 Assessment: Meeting New Challenges. Structure , 22(8) , 1120 – 1139
Recent Blog
Sept 1, 2023 | Oncornet 2.0
Oncornet 2.0 symposium at VU Amsterdam
'With the [presented on Oncornet 2.0] tools and model systems for the oncogenic chemokine receptor CXCR4 (involved in cancer and metastasis) and the atypical receptor ACKR3 in place, the signaling networks and translational effects can be studied in more detail.'
June 1, 2023 | CEBC 2023
CEBC conference in Cracow
Our PhD student Paulina Dragan received the best talk award at the Young Scientific Investigator's Session. She spoke about chemokine receptors and discovery of novel drug scaffolds using machine learning-assisted virtual screening.
March 17, 2023 | Pod znakiem medycyny
Otwarcie Wydziału Medycznego UW
Spotkanie "Pod znakiem medycyny" Uniwersytet Warszawski - Wojskowy Instytut Medyczny - Państwowy Instytut Badawczy
Jul 15, 2021 | Machine learning
Highly accurate protein structure prediction with AlphaFold
'Through an enormous experimental effort the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. [...] AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.'
May 23, 2023 | Machine learning
Machine learning: mapping the motion and structure of flexible proteins from cryo-EM data
'A deep learning algorithm maps out the continuous conformational changes of flexible protein molecules from single-particle cryo-electron microscopy images, allowing the visualization of the conformational landscape of a protein with improved resolution of its moving parts.'
March, 2023 | Crystallography
Crystallography: The Promise and Challenge of High Pressure Macromolecular Crystallography
'Since its introduction in the early 1970s, high pressure crystallography (HPX) has shown great potential for the investigation of different types of matter. Using diamond anvil cells, HPX is an emerging technique that has been rapidly implemented, making it available to biologists, and there is immense potential for utilizing this technique in biological systems in the future...'
December 15, 2021 | Drug discovery
GPCR drug discovery: integrating solution NMR data with crystal and cryo-EM structures
'Identifying new GPCR drugs will need additional detailed knowledge of GPCR biology, especially knowledge from structural biology, given the complex structure–function relationships involved in GPCR signaling. [...] In this respect, nuclear magnetic resonance (NMR) spectroscopy in solution is a key tool for analysing function-related conformational equilibria in GPCRs as they relate to allosteric coupling, variable efficacies and biased signaling of GPCR ligands... '
November 23, 2021 | GPCR Receptors
Structure of the class C orphan GPCR GPR158 in complex with RGS7-Gbeta5
'GPR158, a class C orphan GPCR, functions in cognition, stress-induced mood control, and synaptic development. Among class C GPCRs, GPR158 is unique as it lacks a Venus flytrap-fold ligand-binding domain and terminates Galphai/o protein signaling through the RGS7-Gbeta5 heterodimer. Here, we report the cryo-EM structures of GPR158 alone and in complex with one or two RGS7-Gbeta5 heterodimers.'
December 15, 2021 | Signaling
Structure of the class D GPCR Ste2 dimer coupled to two G proteins
'G-protein-coupled receptors (GPCRs) are divided phylogenetically into six classes, denoted A to F. More than 370 structures of vertebrate GPCRs (belonging to classes A, B, C and F) have been determined [...]. Here we determine the structure of a [first] class D GPCR, the Saccharomyces cerevisiae pheromone receptor Ste2, in an active state coupled to the heterotrimeric G protein Gpa1–Ste4–Ste18. Ste2 was purified as a homodimer coupled to two G proteins.'
October 24, 2019 | Drug Discovery
Toward G protein-coupled receptor structure-based drug design using X-ray lasers
'Rational structure-based drug design (SBDD) relies on the availability of a large number of co-crystal structures to map the ligand-binding pocket of the target protein and use this information for lead-compound optimization via an iterative process.[...] Here, a method is presented for the rapid determination of multiple co-crystal structures for a target GPCR in complex with various ligands, taking advantage of the serial femtosecond crystallography approach, which obviates the need for large crystals and requires only submilligram quantities of purified protein.'
November 18, 2019 | GPCR receptors
Structure of an endosomal signaling GPCR-G protein-Beta-arrestin megacomplex
'Classically, G-protein-coupled receptors (GPCRs) are thought to activate G protein from the plasma membrane and are subsequently desensitized by Beta-arrestin (Beta-arr). However, some GPCRs continue to signal through G protein from internalized compartments, mediated by a GPCR–G protein–Beta-arr ‘megaplex’. [...] Here, we present its cryo-electron microscopy structure'
November 4, 2019 | Protein Structure
Membrane protein megahertz crystallography at the European XFEL
'The world’s first superconducting megahertz repetition rate hard X-ray free-electron laser (XFEL), the European XFEL, began operation in 2017, featuring a unique pulse train structure with 886 ns between pulses. [...] Here, we report the first membrane protein megahertz SFX experiment, where we determined a 2.9 Å-resolution SFX structure of the large membrane protein complex, Photosystem I, a > 1 MDa complex containing 36 protein subunits and 381 cofactors.'