Author

Mia Crnogaj

University of Cambridge

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:protein structure prediction· 2 Approach:contrastive learning· 1 Approach:deep learning· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

Eva SmorodinaiD· 2 shared Haowen ZhaoiD· 2 shared Mateusz JaskółowskiiD· 2 shared Matthew GreenigiD· 2 shared Montader AliiD· 2 shared Pietro SormanniiD· 2 shared Victor GreiffiD· 2 shared Aubin RamoniD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Eva SmorodinaORCID

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2 papers

Recent·Most cited

Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

Montader Ali, Mateusz Jaskółowski, Matthew Greenig, Tung H. Nguyen, Mia Crnogaj, Eva Smorodina, Aubin Ramon, Haowen Zhao, Monica L. Fernández‐Quintero, Elodie Ghedin, Victor Greiff, Pietro Sormanni

This paper introduces NbForge, a nanobody folding model that improves prediction of HCDR3 blueprint and disulphide bonds, achieving accuracy comparable to large predictors with faster speed.

bioRxiv · Feb 13, 2026 · PreprintBioinformatics

Improving nanobody structure prediction with self-distillation

Montader Ali, Matthew Greenig, Mateusz Jaskółowski, Mia Crnogaj, Eva Smorodina, Haowen Zhao, Victor Greiff, Pietro Sormanni

This study improves nanobody structure prediction by using synthetic data and a classifier to filter structural accuracy, achieving better results than existing methods with lower computational cost.

bioRxiv · Dec 2, 2025 · PreprintBioengineering