@L_Geras

Research Fellow @jmarshlab | Institute of Genetics and Cancer, Edinburgh

 Edinburgh
Joined September 2020
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Our @jmarshlab/@MRC_HGU investigation of structural protein stability predictors using deep mutational scanning (DMS) data has been published in Protein Science @ProteinSociety doi.org/10.1002/pro.4688

Correspondence between functional scores from deep mutational scans and predicted effects on...

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a...

onlinelibrary.wiley.com
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Lukas Gerasimavičius retweeted
Joe Marsh@jmarshlab25 Feb 2025
New review out with @L_Geras @teichlab ! 🧬🔬 We explore how protein structure is improving variant effect prediction. Advances in structural modelling, including AlphaFold, are powering methods like AlphaMissense, PrimateAI-3D, and CPT-1, leading to better accuracy and interpretability. Read more: sciencedirect.com/science/ar…
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Lukas Gerasimavičius retweeted
Kudla lab@kudlalab21 Sep 2023
We have an open postdoctoral position on deep mutational scanning of transcription factor genes @kudlalab @mrc_hgu @varianteffects. Apply by 4th October! jobs.ac.uk/job/DCK493/resear…
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Lukas Gerasimavičius retweeted
Joe Marsh@jmarshlab19 Sep 2023
Excited to share our Perspective on the new AlphaMissense variant effect predictor tool from @GoogleDeepMind @ScienceMagazine science.org/doi/full/10.1126…

Predicting pathogenic protein variants

Machine-learning algorithm uses structure prediction to spot disease-causing mutations

science.org
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I'm happy to present you the final version of Matteo's work on predicting functionally important sites in proteins using a machine-learning model that leverages structure and sequences and is trained using 2D-multiplexed data on abundance and activity doi.org/10.1038/s41467-023-3…
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Finally, repeating the correlation benchmark just on the abundance-based DMS data, also including a number of VEPs, we again find that the 'Foldetta' consensus dominates the ranking, even against dedicated variant effect predictors that were designed specifically for the task.
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Empirical force-field scoring functions remain the most useful stability tools for identifying disease variants. Could machine-learned ensembles of top tools improve agreement with experimental variant effect landscapes? Excited about what future stability predictors might bring.
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Lukas Gerasimavičius retweeted
Joe Marsh@jmarshlab22 Nov 2022
Update to our 2020 study benchmarking variant effect predictors using deep mutational scanning data, featuring more VEPs and more DMS datasets. @BenLivesey6 biorxiv.org/content/10.1101/…

Updated benchmarking of variant effect predictors using deep mutational scanning

Variant effect predictors (VEPs) provide a potential solution to the influx of variants of uncertain clinical significance produced by genome sequencing studies. However, the assessment of VEP...

biorxiv.org
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Lukas Gerasimavičius retweeted
Joe Marsh@jmarshlab18 Nov 2022
Excited to share this new preprint where we finally are able to address the longstanding hypothesis that cotranslational protein complex assembly can decrease the likelihood of dominant-negative mutations @BdonyiMihly biorxiv.org/content/10.1101/…

Buffering of genetic dominance by allele-specific protein complex assembly

Protein complex assembly often begins while at least one of the subunits is still in the process of being translated. When such cotranslational assembly occurs for homomeric complexes, made up of...

biorxiv.org
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Lukas Gerasimavičius retweeted
Hannah Long@Hannah_K_Long22 Oct 2022
New #biorxiv preprint! We identify structural elements at a human disease locus that promote domain-wide interactions and facilitate extreme long range gene regulation. A fantastic collaboration with Liang-Fu Chen, @minheepark8, Tomek Swigut, @Aboettiger and Joanna Wysocka!
bioRxiv@biorxivpreprint21 Oct 2022
Structural elements facilitate extreme long-range gene regulation at a human disease locus biorxiv.org/cgi/content/shor… #bioRxiv
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Lukas Gerasimavičius retweeted
Pranas Grigaitis@pranasag20 Sep 2022
... which is another shameless self-promotion of our paper, recently published in @mSystemsJ on the computational tools to dissect pombe metabolism: journals.asm.org/doi/10.1128… 2/2

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission...

Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species...

journals.asm.org
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