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Congratulations to…

• Hannah Lydon, who graduated with a L4 project dedicated to profiling our JabberDock protein-protein docking engine. Hannah has now started her PhD studies at King’s college London.
• Cameron Stewart, who graduated with a L4 project on assessing the ability of molearn to generate protein bound states from simulations of their unbound state. Cameron now works at Ocado.
• Yuxi Liu and Jamie Luo who, under the co-supervision of Prof. Martin Cann in Durham’s Department of Biosciences, obtained postgraduate degrees in data science with projects dedicated to the prediction of lysine carbamylation sites using neural networks.

Welcome to…

• Marco Mattia who, under the main supervision of Dr. Antonia Mey at the University of Edinburgh and funded by the SOFI2 CDT, started a PhD on the usage of generative models to characterize the conformational spaces of disordered proteins.
• Asal Azar, who started a PhD on the usage of generative models to characterize protein conformational spaces.
• George Weston who, co-supervised by Prof Martin Cann and sponsored by the BiSCOP CDT, started a PhD on the characterization of protein carbamylation sites.
• Mateusz Wiszniovski, who started a L4 project on the usage of molearn to predict transition states between protein conformations.
• Louise Persson, visiting PhD student from the Marklund group at the University of Uppsala and funded by a Matariki fellowship, working on the identification of protonation sites on proteins subjected to nano-electrospray ionization.

Do you want to design, train, and test a neural network with protein conformational ensemble? This is quite fiddly, especially if you want the training to talk to a molecular dynamics engine. This is why we are now releasing molearn, a Python package streamlining the whole process. Among its many perks, molearn offers the possibility of talking directly to OpenMM’s backend. Have look here!

Phew, this has been a busy month! We started with the CCPBioSim conference in Leeds, with presentations by Matteo and Gudong, and posters by Cameron and Ryan. Ryan won an award for his work on combining molearn models with transition path sampling, congratulations!
Then, Marco and Ryan attended the CCP5 Summer School, where Matteo taught Machine Learning for the analysis of MD simulation data. Finally, on to EBSA conference in Stockholm, with Sam presenting his work on the new molearn package.

A fully funded PhD studentship in the area of machine learning protein conformational spaces is currently available. The student will work on developing molearn, our generative neural network trainable with protein conformations generated via molecular dynamics simualtions or experiments.

To apply, please follow this link. For informal enquiries, please contact Matteo (matteo.t.degiacomi[AT]durham.ac.uk).

Our work, in collaboration with the Zuber and Posthaus groups in the University of Bern, was just published in EMBO Reports (well done Julia Bruggisser and Ioan Iacovache for the beautiful cover image!).

We conclude that beta-toxin could be suitable candidate for small molecule sensing or selective molecule delivery and transport.

Reference:  J. Bruggisser, I. Iacovache, S. C. Musson, M. T. Degiacomi, H. Posthaus, B. Zuber (2022). Cryo-EM structure of the octameric pore of Clostridium perfringens β-toxin, Embo Reports

Congratulations to…

• Louis Sayer, who graduated with a L4 project dedicated to profiling molearn, our neural network trainable with protein molecular dynamics conformations.
• Saabir Petker, who graduated with a L4 project on assessing the peformance of JabberDock, our protein-protein docking engine
• Hao Man who, under the co-supervision of Prof. Martin Cann in Durham’s Department of Biosciences, obtained a postgraduate degree in data science with a project dedicated to the prediction of lysine carbamylation sites using neural networks.

Welcome to…

• Ella Finley and Victoria Liu who, sponsored by the BiSCOP and MosMed CDTs, start a PhD on the characterization of protein carbamylation sites under the main supervision of Prof. Martin Cann.
• Ryan Zhu who, under the main supervision of Dr. Antonia Mey at the University of Edinburgh, starts a PhD funded by Redesign Science on the usage of generative models to characterize protein conformational spaces.
• Hannah Lydon, starting a L4 project on the refinement of docking poses generated by JabberDock
• Cameron Stewart, starting a L4 project on the usage of molearn to predict transition states between protein conformations.

This will be a busy month for the group… let us know if you want to meet up!

Matteo, Sam and Cameron will attend the CCPBioSim conference in Edinburgh (6-8 June). The conference will be followed by a MD+ML workshop, where Matteo will show how to use the MDAnalysis and sklearn Python packages to characterize molecular dynamics simulations.

Matteo will then travel to Erice for the MolSim22 conference (25-29 June) and then, along with Sam, to Barcelona for the MMSML Workshop (14-16 July).

Finally, Josh will attend the CCP5 Summer School in Durham (17-28 July).

A fully funded studentship in the area of machine learning for computational biophysics is available in our group, starting October 2022.

All living organisms contain millions of proteins; biopolymers that fold into three-dimensional biologically active structures playing a vital role in the regulation of life and diseases. Research has seen a lot of focus on determining the atomic structure of different proteins. However, the flexible movement of these biopolymers plays a crucial role in their biological (mal)function.

In recent years, machine learning has been revolutionizing the way we interpret data in many scientific areas. For example, the deep neural network AlphaFold2 can predict the 3-dimensional structures of proteins, whose shape is not known experimentally. In our research, we have designed a deep neural network that can also learn an ensemble of structures of specific proteins from molecular simulations. This project builds upon this breakthrough.

In collaboration with the Willcocks group (Department of Computer Science), the student will develop a general neural network capable of learning and predicting the dynamics of any protein. The neural network will be trained with existing and new data you will produce from molecular dynamics simulations. Applications of this work are vast, ranging from understanding the effect of genetic mutations in cancers to informing the design of proteins to carry out a desired function.

Do you have a background in physics, computer science, chemistry, biology, or related discipline, and are keen to develop your computational skills to address biomolecular problems? Then please direct any informal enquiries to Matteo (matteo.t.degiacomi@durham.ac.uk). More information on the application process can be found here. Applications will be considered until the 3rd April.

Are you thinking about writing a Python program to load a bunch of PDB files and measure some of their properties (CCS, interatomic distances, solvent-accessible paths, SAXS, …)? Perhaps you would like to move a bunch of structures around to form a complex in your integrative modelling pipeline, where every subunit may take several conformations? Or how about creating bead models of electron density maps, or the opposite? Do you find it fiddly? We did too, this is why we have created Biobox!

Biobox is a Python package that caters for virtually all our biomolecular modelling needs. Basically every publication we ever produced in the last 8 years did, in a way or another, feature Biobox. Now we are making it public, hopefully you will find as helpful for your work as it was for ours! We have put information about it here, including API and a Jupyter notebook demonstrating its main features.

Congratulations to….

• Lucas Rudden, who is now a doctor after having successfully defended his thesis titled “The Impact of Dynamics in Protein Assembly“. Lucas finished with a bang, by winning two prizes for his work: the BSI Thesis Prize and the Departmental Winton Prize. He has now moved to the group of Prof. Patrick Barth in EPFL as PDRA.
• Venkat Ramaswamy, who started his new job as researcher in Cresset after having spent two years with us as PDRA, developing machine learning methods to sample protein conformational space.
• Lucy Vost, who successfully completed her L4 project on molecular dynamics coupled with machine learning, and in October will start her doctoral studies in Computational Discovery at the University of Oxford.

Welcome to…

• Cameron McAllister, who joins us as PhD student within the SOFI2 CDT programme. Cameron, co-supervised by Dr. Beth Bromley and Prof. Kevin Weatherhill, will develop molecular modelling methods to predict proteins’ Enhanced Acoustic Raman (EAR) spectra.
• Louis Sayer, joining us for the 2021/22 L4 project on machine learning protein conformational spaces with molearn.
• Saabir Petker, joining us for 2021/22 L4 project on protein-protein docking with JabberDock.
• Charles Brown, doing an 8 week-long internship with us. In collaboration with Prof. Martin Cann, Charlie will develop a computational pipeline to investigate lysine carbamylation.