This is the sixth of a series of on line events aimed mainly at PhD students and researchers in their first post-doc. Our goal is to provide a new venue for these young scientists to share their work, get expert feedback and have an opportunity to strengthen scientific relations within the CECAM community.

The event is fully on line and will have two parts. In the first, broadcasted as a Zoom webinar, Alessandro Laio, SISSA, Trieste, will present a general talk in the area of activated events (title and abstract below). This will be followed by seminars given by three young members of the community to describe their work in the same area. In the second part of the event, we shall move to a virtual poster session hosted in a Gather room where more PhD students and researchers in their first post-doc will present pertinent projects. The session’s speaker and other (surprise) expert guests will join us for this poster session to discuss exciting new science. 

To participate

If you are a PhD student or a post-doc:

Please use the Participate Tab on this page to start the application. You will have to login using your CECAM account to access the application form. If you don't have a CECAM account yet, use the register option on the top right corner of the login page...and welcome to CECAM!

If you are a more senior scientist:

Please contact the organisers and we shall process your registration.

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 post-doc)

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 in particular title and abstract following the recommended format. On the same form you can already upload your poster file in png or jpg if 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 on the Gather room associated with this session until the end of the series (July 2021) 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 place in the poster session. 

Selection of the two 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, via a lottery from the posters selected for the Gather session. Please indicate in your application if you DO NOT WANT your poster to be considered for this lottery. 

THE DECISION ON THE POSTER AND THE OUTCOME OF THE LOTTERY SELECTION WILL BE COMMUNICATED ONE WEEK 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 6. Title and abstract of talks

Estimating the free energy: from enhanced sampling to manifold learning

Alessandro Laio, SISSA, Trieste

Multidimensional free energy landscapes are able to provide a synthetic description of  complex molecular systems, revealing their salient features. In the last decades we have witnessed the development of several powerful enhanced sampling methods, and estimating  the free energy as a simultaneous function of 2-3 variables is nowadays routine. However, in many relevant systems one cannot project the free energy to a low-dimensional space without artificially merging distinct free energy minima, therefore losing relevant information. But how can one find the minimal number of variables which are really necessary to describe a system? How can one choose these variables among a set of many candidates?  Can one compute the free energy as a simultaneous function of, say, 10 variables? And how can one visualize and interpret a 10-dimensional landscape? We will illustrate how these questions  can at least partially be addressed exploiting tools offered by unsupervided manifold learning and dimensional reduction.

Nucleating different coordination in crystal under pressure: Study of B1-B2 transition in NaCl by metadynamics

Matej Badin, SISSA; Comenius University

Prediction of crystal structures has reached a high level of reliability, but much less is known about the mechanisms of structural transitions and pertinent barriers. The barriers related to nucleation of crystal structure inside another one are critically important for kinetics and eventually decide what structure will be created in experiment.  

We show here an NPT metadynamics simulation scheme [1] employing coordination number and volume as collective variables and illustrate its application on a well-known example of reconstructive structural transformation B1/B2 in NaCl. Studying systems with size up to 64000 atoms we reach beyond the collective mechanism and observe the nucleation regime. We reveal the structure of the critical nucleus and calculate the free energy barrier of nucleation and also uncover details of the atomistic transition mechanism and show that it is size-dependent. 

Our approach is likely to be applicable to a broader class of structural phase transitions induced by compression/decompression and could find phases unreachable by standard crystal structure prediction methods as well as reveal complex nucleation and growth effects of martensitic transitions. 

Reference

[1] Matej Badin and Roman Martoňák, arXiv:2105.02036

Benchmarking the accuracy of free energy landscapes generated by adaptive sampling strategies

Eugen Hruska, Emory University

Verifying the accuracy of free energy landscapes generated by sampling methods is limited due to the limited number of test systems. We investigate the accuracy of adaptive sampling methods on a test set of proteins with sizes ranging up to 70 amino acids, establishing a baseline of accuracy demonstration. Besides the free energy landscape; the convergence of kinetic results from sampling methods is crucial. The kinetic convergence, upper limits for sampling performance, and scaling of these sampling methods are considered. 

References

[1] E. Hruska, V. Balasubramanian, H. Lee, S. Jha, C. Clementi, J. Chem. Theory Comput., 16, 7915-7925 (2020)
[2] E. Hruska, J. Abella, F. Nüske, L. Kavraki, C. Clementi, J. Chem. Phys., 149, 244119 (2018)

N-glycan conformers explored by enhanced sampling and machine learning

Isabell Louise Grothaus, University Bremen

Glycosylation is one of the bulkiest post-translational modification of proteins but has long been overlooked in molecular dynamics simulations, despite its omnipresence in the cell. However, the structure, function and interaction of many biochemical systems is governed by N-glycans covalently linked to asparagine residues in specific protein sequences. Due to the flexibility of their glycosidic linkages and their sugar units, N-glycans assume many different conformations, unlike the more rigid protein structure to which they are attached. A complete description of their conformational phase space requires thus the consideration of a large number of internal degrees of freedom. We show that an enhanced-sampling molecular dynamics scheme based on enhancing transitions of all relevant barriers with concurrent one-dimensional energy potentials in the framework of metadynamics can in fact capture effectively global conformers of branched glycans, importantly also including the monomer puckering states. Interestingly, our approach revealed altered N-glycan conformer populations depending on the puckering state of individual monosaccharides. These puckering-dependent conformer distributions, so far mostly ignored in glycoprotein simulations, might be crucial in explaining biological phenomena involving N-glycans.