Gorka Lasso
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gorkalasso.bsky.social
Gorka Lasso
@gorkalasso.bsky.social
86 followers 180 following 16 posts
Associate Professor @ UCL, London. Computational Biology, Structural Bioinformatics, machine Learning, Proteins, Viruses. https://profiles.ucl.ac.uk/103340-gorka-lasso-cabrera
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If you're a #Student or #Postdoc excited about #Viruses, #Bioinformatics, or both—reach out!
We’re building something new at @ucl and there’s room at the table 👾
📩 DMs open
🔁 RTs appreciated
Our lab values:
✔️ #Inclusivity
✔️ #ScientificCuriosity
✔️ #Mentorship & #CareerDevelopment (whether in #academia, #industry, or elsewhere)
Every lab member will grow—and be supported in the process.
We approach these problems by studying how viral infections work at the molecular level using:
🔹 #StructuralBioinformatics
🔹 #MachineLearning
🔹 #NetworkBiology
… and we collaborate with experimentalists worldwide 🌍 #TogetherStronger
We focus on 3 main research areas:
1️⃣ Assessing the epidemic potential of newly discovered viruses
2️⃣ Predicting host susceptibility to infections
3️⃣ Designing personalized therapies and antivirals informed by host & viral genetics
The #LassoLab sits at the intersection of #ComputationalBiology and #Virology.
As viral spillover and #Outbreaks become more frequent due to #Globalization and #ClimateChange, we aim to develop tools to predict and prevent future #Pandemics.
🚨 Lab launch alert!
I'm thrilled to share that I’ve joined the Division of Infection & Immunity at @ucl and officially launched the #LassoLab! Currently a one-person operation—but hopefully not for long 😊
🧵
Grateful for the constructive feedback from Cell Host & Microbe reviewers, which strengthened our paper. Their fair and thoughtful criticism was instrumental. 10/10
Huge thanks to our amazing coauthors—experts from viral ecology, molecular virology and computational biology across 9 countries. Special shoutouts to Michael Grodus, Estefania Valencia, @jangralab.bsky.social, @chandranlab.bsky.social, and Simon Anthony. #TogetherStronger 9/10
These findings provide actionable insights for targeted surveillance strategies, helping identify hosts of high-risk filoviruses. This can reduce spillover risks and enhance public health preparedness. 8/10
The model also applies to newly discovered filoviruses, bats in non-endemic regions and it can also be used to assess zoonotic potential—crucial for evaluating risks to human health. 7/10
Our model captures receptor-binding principles and predicts host susceptibility across filoviruses and bat species. By combining receptor binding with #biogeographic data, we identify bats acting as potential hosts for Ebola virus. 6/10
Our study provides the most extensive characterization of receptor binding in filoviruses to date, integrating experimental data with machine learning (#ML) to uncover molecular rules driving receptor binding. 5/10
The Niemann-Pick C1 (#NPC1) protein is a critical receptor for filoviruses, binding the #filovirus #glycoprotein (GP) to enable viral entry. Variations at the GP-NPC1 interface dictate viral #susceptibility and host range. 4/10
#Bats are the primary host suspects for #filoviruses, but the exact species involved remain poorly characterized. This knowledge gap hinders our ability to predict and prevent #spillover events. 3/10
#Filoviruses cause sporadic yet increasingly frequent outbreaks of fatal hemorrhagic fever in #humans. Monitoring host animals can help predict animal-to-human transmission and reduce the risk of future #outbreaks. 2/10
Exciting news! Our collaborative study reveals the molecular secrets of how #filoviruses, like #Ebola virus, interact with their #receptor and enter host cells. We also predict #bat species that could act as hosts for Ebola virus. Check it out: https://buff.ly/4akxlP1 1/10
buff.ly
Reposted by Gorka Lasso
Couldn’t find a virology starter pack so here’s one to get the ball rolling. Repost/reply if you are a virologist so I can find and add you to the list!

go.bsky.app/SinqoJU
Reposted by Gorka Lasso
Protein instability is the main driver of inherited genetic conditions, according to the largest map of protein variants to date − Human Domainome 1.0 🔎

This dataset can help predict how proteins behave, paving the way for new treatments 💊

www.sanger.ac.uk/news_item/hu...