Francesco Costa

Phil Hinchliffe and Steve Burston, Delhi Kalwan, Jennifer de Jong, Fabio Parmeggiani and Paul Race

This project was made possible thanks to a collaboration between @bristoluni.bsky.social and @ebi.embl.org. A great thanks goes to @robbarringer.bsky.social, Ioannis Riziotis (Crick), Antonina Andreeva,
@alexbateman1.bsky.social and ...

⚠️⚠️⚠️Preprint alert⚠️⚠️⚠️
We mapped intramolecular isopeptide bonds across the AlphaFold database and found that they are widely distributed in microbial surface proteins, such as fibrillar adhesins or pilins, suggesting new targets for broad-spectrum antimicrobial strategies.

🙏 Special thanks to my amazing collaborators: Ioannis Riziotis, @robbarringer.bsky.social, Antonina Andreeva and to my supervisor @alexbateman1.bsky.social for their invaluable contributions to this work!

Google Colab

💻 Available as a Python package for easy integration into bioinformatics workflows, and accessible via Google Colab for everyone: colab.research.google.com/github/Franc...

🧬 Isopeptor enables reliable detection and geometry evaluation of these covalent links using a template-based strategy powered by pyJess.

🔍 The tool demonstrates a precision of 1.0 and recall of 0.947 in identifying incorrectly modelled isopeptide bonds in PDB structures.

Isopeptor: a tool for detecting intramolecular isopeptide bonds in protein structures AbstractMotivation. Intramolecular isopeptide bonds contribute to the structural stability of proteins, and have primarily been identified in domains of ba

📢 Just published! Introducing Isopeptor: a computational tool for detecting intramolecular isopeptide bonds in protein structures with high precision and recall.
academic.oup.com/bioinformati...

Check out this article about my research at @embl.org where I focus on protein modelling and on the study of fibrillar adhesins. Recently, we developed a method to detect isopeptide bonds, a key stabilizing feature in bacterial proteins. Thanks @oanastroe.bsky.social for putting this together!👇 👇 👇

Protein structure prediction from the AlphaFold Database

Prediction confidence scores help #AlphaFold users gauge the reliability of protein structure predictions. 🖥️🧬

But things get more challenging for protein families.

A new method helps improve low confidence predictions within protein families.

academic.oup.com/bioinformati...

Thanks Gonzalo!

A special thanks goes to my supervisor @alexbateman1.bsky.social
and to @matthiasblum.bsky.social
for their support and guidance. 7/7

Our findings have important implications for improving structure predictions, especially for proteins from organisms with limited representation in sequence databases or for rapidly evolving taxa. 6/7

We show that using high plDDT models as templates can increase the speed of AlphaFold2 as implemented in ColabFold, potentially reducing computational costs and carbon footprint. 5/7

We introduced a novel "Best Pick" strategy that combines predictions made with and without multiple sequence alignment (MSA) information, selecting the model with the highest average plDDT. 4/7

This observation led us to explore whether low-confidence predictions could be improved using high-confidence templates from the same protein family. About one-third of low-confidence structures can be "rescued" to reasonable confidence levels using this method. 3/7

By observing the plDDT distribution within protein domain families, we noticed a certain degree of heterogeneity in the confidence of AlphaFold2 predictions. 2/7

Keeping it in the family: using protein family templates to rescue low confidence AlphaFold2 models AbstractMotivation. High confidence structure prediction models have become available for nearly all protein sequences. More than 200 million AlphaFold2 mo

Excited to announce our latest publication: “Keeping it in the family: Using protein family templates to rescue low confidence AlphaFold2 models” where we explore plDDT variability in #AF2 models of @pfamdb.bsky.social
domains. doi.org/10.1093/bioa... 1/7