March 18, 2024

Exploring Flexibility and Mobility in SARS–CoV–2 Protein Structures: Insights into Spike Protein Mut

Writing about web page

Exploring Flexibility and Mobility in SARS-CoV-2 Protein Structures: Insights into Spike Protein Mutations

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a significant threat to global public health since its emergence. Understanding the flexibility and mobility of SARS-CoV-2-related protein structures, particularly in the spike protein, is crucial for elucidating its pathogenesis, transmission dynamics, and evolution. This review provides an overview of the structural dynamics of SARS-CoV-2 proteins, emphasizing the role of flexibility and mobility in natural mutations of the spike protein. We discuss methodologies used to investigate protein flexibility, highlight key findings regarding SARS-CoV-2 spike protein mutations, and explore implications for therapeutic interventions and vaccine development.


The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, has spurred intense research efforts to unravel the molecular mechanisms underlying viral infection and transmission. Central to these efforts is understanding the structural biology of SARS-CoV-2 proteins, particularly the spike (S) protein, which mediates viral entry into host cells. The S protein is a key target for therapeutic and vaccine development due to its critical role in viral infectivity and immune evasion. This review focuses on the flexibility and mobility of SARS-CoV-2-related protein structures, with a specific emphasis on characterizing natural mutations in the S protein [1,2].

Structural Dynamics of SARS-CoV-2 Proteins

The dynamic nature of proteins plays a fundamental role in their biological functions. Techniques such as X-ray crystallography, cryo-electron microscopy (cryo-EM), and molecular dynamics simulations (MD) have provided insights into the three-dimensional structures of SARS-CoV-2 proteins and their conformational flexibility. The S protein exists in prefusion and postfusion conformations, with conformational changes essential for membrane fusion during viral entry. Understanding the dynamics of these conformations is critical for deciphering viral entry mechanisms and designing intervention strategies.

Methodologies for Investigating Protein Flexibility

Various experimental and computational approaches are employed to study protein flexibility and mobility. X-ray crystallography and cryo-EM provide static snapshots of protein structures at atomic resolution, while MD simulations offer dynamic insights into protein motions over time scales ranging from picoseconds to milliseconds. Additionally, nuclear magnetic resonance (NMR) spectroscopy can elucidate protein dynamics in solution, complementing structural data obtained from other techniques. Integrating these approaches allows for a comprehensive understanding of protein flexibility and its functional implications.

Flexibility and Mobility in SARS-CoV-2 Spike Protein Mutations

Natural mutations in the S protein have been extensively studied to assess their impact on viral infectivity, transmission, and immune evasion. Mutations within the receptor-binding domain (RBD) of the S protein can alter receptor binding affinity and affect viral tropism. For example, the D614G mutation, located in the S1 subunit of the S protein, has been associated with increased viral transmissibility. Structural analyses have revealed that the D614G mutation stabilizes the prefusion conformation of the S protein, enhancing its binding affinity to the host cell receptor angiotensin-converting enzyme 2 (ACE2).

Moreover, emerging variants of concern, such as the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2) variants, harbor mutations in the S protein that may impact viral fitness and immune recognition. These variants exhibit enhanced transmissibility and potential resistance to neutralizing antibodies elicited by natural infection or vaccination. Structural studies have demonstrated that mutations in the S protein can modulate antibody recognition by altering epitope accessibility or conformational dynamics. Additionally, mutations in the S protein may influence viral escape from host immune surveillance, posing challenges for vaccine efficacy and therapeutic development.

Implications for Therapeutic Interventions and Vaccine Development

Understanding the flexibility and mobility of SARS-CoV-2 protein structures, particularly in the context of natural mutations, is crucial for guiding the development of effective antiviral therapies and vaccines. Targeting conserved regions of the S protein, such as the RBD, may offer promising avenues for therapeutic intervention. Structure-based design approaches can be employed to develop small molecule inhibitors or monoclonal antibodies that disrupt viral entry or neutralize viral infectivity. Furthermore, vaccine strategies incorporating conserved epitopes or multivalent immunogens can enhance immune responses against diverse SARS-CoV-2 variants.

Future Directions and Concluding Remarks

Continued research into the flexibility and mobility of SARS-CoV-2 protein structures is essential for advancing our understanding of viral pathogenesis and evolution. Integrating experimental and computational techniques will enable comprehensive characterization of protein dynamics and inform the design of next-generation antiviral therapies and vaccines. Moreover, surveillance efforts to monitor the emergence of novel variants and their impact on viral fitness and immune escape are critical for pandemic preparedness and response. By elucidating the structural basis of SARS-CoV-2 protein flexibility, we can mitigate the impact of emerging viral threats and facilitate effective control measures against future outbreaks.

In conclusion, the flexibility and mobility of SARS-CoV-2-related protein structures, particularly in the spike protein, play pivotal roles in viral infectivity, transmission dynamics, and immune evasion. Characterizing natural mutations in the spike protein provides valuable insights into viral evolution and informs therapeutic and vaccine development efforts. By leveraging structural biology approaches, we can devise strategies to combat the ongoing COVID-19 pandemic and mitigate the risk of future coronavirus outbreaks.

Acknowledgements and motivation

Since publishing [1+2], I am being asked very often by automated journal emails to simply submit a slight copy of these two articles to their journal, presumably to fill a need on their side. So this "article" is meant to fill this need. I thank ChaptGPT 3.5 for valuable assistance in writing it.


[1] Panayis, J., Römer, N. S., Bellini, D., Katrine Wallis, A., & Römer, R. A. (2022). Characterizing flexibility and mobility in the natural mutations of the SARS-CoV-2 spikes. Journal of Physics: Conference Series, 2207(1), 012016.

[2] Römer, R. A., Römer, N. S., & Wallis, A. K. (2021). Flexibility and mobility of SARS-CoV-2-related protein structures. Scientific Reports, 11, 4257.

February 23, 2023

The new link to become a reviewer for PHYSE.

Follow-up to Applying to review for Physica E from Rudo's blog

PHYSE has now changed from EVise to EM, hence the the link should now be as given below:

Dear ,

Thanks a lot for your interest in joining the Physica E pool of reviewers. Indeed, we are always keen to enlarge our group of experts to go out for a review. One of our key criteria is that these colleagues are known authors themselves in the field of specialization and have a publication record in the Physica E areas of excellence, i.e. nanostructures and low-dimensional systems. This excellence and engagement with Physica E’s research areas is usually best supported by previous publications in Physica E.

You can join our peer review pool by registering in EM to start the process, i.e. or

In EM, as soon as a person registers in the system, they become available as reviewers; however, they will be able to access their reviewer profile/account of a journal only when they are invited by the journal for the review process.

February 02, 2021

Updating to FastAI v2 on ubuntu and anaconda

Follow-up to Keras, TensorFlow and more on the SC–RTP desktops from Rudo's blog

I wanted to upgrade my FastAI installation to V2, using my existing ubuntu installation of anaconda. After a few wrong tipps on google, I converged to the following.

First, I made a new conda environment, i.e.

$ conda create -n FastAI2

Then I installed all that was needed into the new environment:

$ conda update -n base -c defaults conda
$ conda install -c fastai -c pytorch fastai
$ conda install jupyter
$ conda install -c fastai nbdev

OK, that seems to work, i.e. the example jupyter notebooks from the FastAI V2 course now run.

January 16, 2021

WebDAV access to UoW H+M drives

In the Ubuntu (20.04) default file manager, if you type in the "Connect to Server" box


then I get a username/password prompt. Logging in with ITS credentials, I then have full access to the centrally managed H+M drives. In order to make the access more permanent, I "bookmark" the connection in nautilus.

October 23, 2020

Keras, TensorFlow and more on the SC–RTP desktops

Follow-up to Using PyTorch on the SC–RTP machines from Rudo's blog

This set of commands allows to load a working Keras/TF/scikit ML environment which will run on a GPU if present

$ module load GCC/8.3.0  CUDA/10.1.243  OpenMPI/3.1.4
$ module load IPython/7.9.0-Python-3.7.4
$ module load TensorFlow/2.1.0-Python-3.7.4 
$ module load Keras
$ module load scikit-learn
$ jupyter notebook &

using the versions

tensorflow:  2.1.0 , keras:  2.3.1 sklearn:  0.21.3

October 16, 2020

Using PyTorch on the SC–RTP machines

Writing about web page /rroemer/entry/installing_your_own/

Writing about an entry you don't have permission to view

We (the DisQS team) have now moved back from Cergy to the University of Warwick where the Scientific Computing RTP provides a centrally managed Linux desktop setup as well as bespoke HPC solutions, including some GPU-enabled machines. This means that we need to switch from our self-managed Ubuntu/Anaconda solution to the managed SC-RTP desktop. The following lines, executed in an SC-RTP terminal, allow us to use PyTorch, or preferred ML package at the moment, as follows

module load GCC/8.3.0  OpenMPI/3.1.4
module load IPython/7.9.0-Python-3.7.4
module load PyTorch
module load scikit-learn
pip install --user torchvision==0.4.0
module save ML (or some such name)

and later one can retrieve these settings via

module restore ML

Please not that we need to install torchvision locally via pip as it's not available on the SC-RTP system as a module. The pip mechanism is what replaces the "conda install ..." used under anaconda for missing libraries. Also, we found that the newer torchvision version 0.7.0 gave some error with our codes, so we are using an older version that seems to work. This may not be needed for your code. Obviously, the use of "module save/restore" is optional, but very convenient.

jupyter notebook &

then works as before/desired.

March 31, 2020

Screen casting while working from home

Writing about web page

As I am sitting at home due to the Covid-19 pandemic and the closure of "outside life", I find myself having to give feedback to PhD students who are not around. Normally, I would do this with them sitting next to me. Now, I am trying to do it using a "Screencast", i.e. a video that shows my screen and records my voice, perhaps even my video, when I go through their work and tell them what I think about it and how it could be improved. I have found that under Linux, there is a varirty of useful and free such screencast software. After a bit of googling, I found

and at the moment, I am using the "recordmyDesktop" tool which one can install via the ususal Linux software channels. After a few tries, I think this is indeed a great tool. I'll let you know what the students think ...

March 05, 2020

list of submitted projects and information about late submission penalties

Follow-up to Submission date and instructions for your project work from Rudo's blog

Dear all,

thus far I have received submissions from (10/02/30 10:00)

  1. Jettawat Khantaveramongkol
  2. Juan Roman Roche
  3. Hendrik Schlueter
  4. Alexios Malaquias
  5. DanielPepin Fowan
  6. Burak Civitcioglu
  7. Roman Perrier (one day late)
  8. Jeremy Cadran (two days late)

Please note that I intend to apply late submission penalties, following roughly similar Warwick penalties which penalize a late submission with a 5% reduction in final points for every day late. A late day starts in the first minute after the dealine and the time of submission follows from the time of the last upload.



March 02, 2020

PS: don't worry too much about maximizing epochs

Follow-up to Submission date and instructions for your project work from Rudo's blog

Just to add: do not worry too much about running your code to the maximum of epochs. It is clear to us that most people do not have access to state of the art graphics cards or hundreds of processors. What is more important is to see that you came up with a good idea where machine learning can be used, executed the right commands to get some training and analysis done, and found the right data to train on. In fact, we migth even try to run some of your code for more epochs to see how results can get better!

Submission date and instructions for your project work

Follow-up to teaching schedule, now ready from Rudo's blog

Dear all,

you should now all be close to finishing your project work for the machine learning course. I hope it has all gone well and that you had some fun exploring machine learning. The official date for submission is

Friday, March 6 at 17:00.

For submission, please remember that we want you to submit a running machine learning code using a jupyter notebook with comments that explain step by step what you did and why. Please also supply the data so that Djena and I can run the code ourselves. The easiest way to send us the submission is via

where you can upload both jupyter notebook and data (preferably as a single .zip file). If your data files are too big for upload there (I have 4.5GB in total on that upload drive), please send a link at which we can then download the data. Please remember that it is your responsibility to make sure data and notebook work "out of the box", i.e. without us having to change anything before they work, including directories.

We aim to have feedback for you by the end of March and will communicate this by email to each of you (so please also let me have your preferred email address in your jupyter notebook).


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  • Please note that this link to download is now obsolete. Attendees for the 2021 MPAGS/CYU course shou… by Rudo Roemer on this entry
  • To load Tensorflow and PyTorch (and Keras) modules on ORAC may you may have to module load a few mor… by Daniel Paget on this entry
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