The Mixed-Gen on-line series is aimed mainly at PhD students and researchers in their first post-doc. Our goal is to continue providing a venue for these young scientists to share their work, get expert feedback and have an opportunity to strengthen scientific relations within the CECAM community and beyond.

Sessions consist of two parts. In the first, publicly available on Zoom, an experienced speaker and two/three young scientists present talks. In the second, accessible only to registered participants, posters are presented in a GatherTown room.

More detailed information on the program will appear on this page closer to the date of the event.

Links for the session:

To register use the Participate tab on this page

If you do not have a CECAM account register by clicking here...and welcome to CECAM!

Submission of posters

(Please note that - at least for the time being - we shall accept posters only from PhD students or researchers in their first two post-docs)

Register for the session as described above.

After your application is accepted, you will be able to submit a poster. In the CECAM page for this event, go to “My participation” tab and click on “Add a poster”, providing title and abstract following the recommended format. On the same form you can upload your poster file in png or jpg as soon as it is ready. These formats are strict to enable showing of the poster in the Gather session. If the poster file is not ready at the moment of submitting your abstract, you can upload it later by editing your submission (Go to “My participation” tab and click three vertical dots on “Actions” column on table “My posters”). Please upload your poster as soon as possible to enable a decision from the selection committee - see below.

Please note that posters will be visible in the Gather room associated with this session until the end of the series (June 2023) unless otherwise requested.

DEADLINE FOR SUBMISSION: TEN DAYS BEFORE THE EVENT

Selection of posters

Posters will be selected by the event organisers with the support of our main speaker and experts who will take part in the poster session. 

Selection of the talks by PhD or first year postdocs

These contributions, to be broadcasted in the Zoom webinar in the first part of the event, will be selected, after a preliminary screening by the organisers, the main speaker and guest experts, from the posters selected for the Gather session. Please tick “No” to the question “Upgrade to talk?” in your application if you DO NOT WANT your poster to be considered for upgrade to a talk. 

THE DECISION ON THE POSTER AND THE OUTCOME OF THE SELECTION OF THE TALKS WILL BE COMMUNICATED AT THE LATEST FOUR DAYS BEFORE THE EVENT

POSTER SUBMISSIONS BEYOND THIS DEADLINE WILL BE ACCEPTED BUT NOT CONSIDERED FOR UPGRADE TO TALK. SUBMISSION WILL BE DEFINITELY CLOSED FOUR DAYS BEFORE THE EVENT.

SESSION 5. Title and abstract of talks

Predicting protein-membrane interfaces using molecular simulations and ensemble machine learning

Zoe Cournia, Academy of Athens

Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for membrane proteins that have been considered so far undruggable. In this talk, we explore the free energy landscape of membrane protein dimerization using parallel tempering metadynamics simulations in the well-tempered ensemble and coarse-grained force fields and reproduce the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput [1] We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the energy minima ranking. Moreover, we study oncogenes, including the H1047R and E545K hotspot mutants of PI3Kα, [2] and KRAS-4B [3] using metadynamics to understand the basis of protein overactivation. We calculate their allosteric pathways and show residues important in delivering communication signals between functional domains of each protein.

Finally, we describe an ensemble machine learning methodology to predict protein-membrane interfaces of peripheral membrane proteins [5] and present a drug design pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web-server https://dreamm.ni4os.eu. Taking into account these results, we investigate opportunities for allosteric drug design [6].

The research project was supported by the Hellenic Foundation of Research and Innovation (H.F.R.I) under the 1^st call for H.F.R.I. Research Projects to support Faculty Members & Researchers and the Procurement of high-cost research equipment grant (Project Number: 1780).

References

[1] C. Lamprakis, I. Andreadelis, J. Manchester, C. Velez-Vega, J. Duca, Z. Cournia, J. Chem. Theory Comput., 17, 3088-3102 (2021)
[2] I. Galdadas, F. Gervasio, Z. Cournia, Chem. Sci., 11, 3511-3515 (2020)
[3] I. Andreadelis, S. Kiriakidi, C. Lamprakis, A. Theodoropoulou, S. Doerr, A. Chatzigoulas, J. Manchester, C. Velez-Vega, J. Duca, Z. Cournia, J. Phys. Chem. B, 126, 1504-1519 (2022)
[4] Z. Cournia, A. Chatzigoulas, Current Opinion in Structural Biology, 62, 197-204 (2020)
[5] A. Chatzigoulas, Z. Cournia, Briefings in Bioinformatics, 23, (2022)
[6] A. Chatzigoulas, Z. Cournia, WIREs. Comput. Mol. Sci., 11, (2021)

A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints

Maurice Karrenbrock, Piero Procacci, Francesco Luigi Gervasio, University of Geneva

We test a method for calculating absolute binding free energies, that uses a combined Hamiltonian replica exchange and non-equilibrium alchemical approach, on 11 ligands of the bromodomain protein BRD4. The study demonstrates the benefits of using an improved sampling technique prior to alchemical transformations to obtain accurate estimates of binding free energies, even when starting from sub-optimal initial binding poses. The effect of different restraint mechanisms on the results is also investigated and a new 'Loose-Tight' restraint algorithm is introduced. Overall, the method provides a good balance between ease of use, automation, speed and accuracy for the calculation of absolute ligand binding free energies, and the scripts provided allow easy integration into pre-existing computational drug discovery pipelines.[1]

References

[1] M. Karrenbrock, P. Procacci, F. Gervasio, A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints, 2023
[2] M. Macchiagodena, M. Pagliai, M. Karrenbrock, G. Guarnieri, F. Iannone, P. Procacci, J. Chem. Theory Comput., 16, 7160-7172 (2020)
[3] M. Macchiagodena, M. Karrenbrock, M. Pagliai, P. Procacci, J. Chem. Inf. Model., 61, 5320-5326 (2021)

Unraveling the ribosome stalling mechanism induced by the human XBP1u arresting peptide

Francesco Di Palma, IIT

The eukaryotic ribosome stalling mechanism is a key biological process whose precise atomistic details are still elusive. In order to study in depth the 80S ribosome in a stalled state induced by the human XBP1u translational arrest peptide, we combined molecular dynamics and a “gentle” enhanced sampling simulation method. Multi-microseconds simulations (~15 µs) of the entire ribosome provided an atomistic picture of XBP1u-induced translational stalling mechanism in the presence of the nascent chain inside the exit tunnel; in this framework, we compared the effect of the wt XBP1u arresting peptide with the stalling induced by other 4 experimentally-selected significant mutants [1,2]. Together with the mechanistic details, by means of adiabatic bias molecular dynamics [3], we furthermore ranked the 5 variants in terms of detachment kinetics finding a nice correlation with their stalling strength [4]. The quantitative agreement between simulations and earlier experimental data supports our in silico outcomes, opening to future investigations in the field. Moreover, the amount of data collected paves the way for unprecedented pocket prediction opportunity on such a fundamental macromolecular target.

References

[1] V. Shanmuganathan, N. Schiller, A. Magoulopoulou, J. Cheng, K. Braunger, F. Cymer, O. Berninghausen, B. Beatrix, K. Kohno, G. von Heijne, R. Beckmann, eLife, 8, (2019)
[2] K. Yanagitani, Y. Kimata, H. Kadokura, K. Kohno, Science, 331, 586-589 (2011)
[3] M. Marchi, P. Ballone, The Journal of Chemical Physics, 110, 3697-3702 (1999)
[4] F. Di Palma, S. Decherchi, F. Pardo-Avila, S. Succi, M. Levitt, G. von Heijne, A. Cavalli, J. Chem. Theory Comput., 18, 1905-1914 (2021)