Publications by Martin Pacesa | EPFL Graph Search

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Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology and human health. CRISPR-based gene editors derived from microorganisms, although powerful, often show notable functional tradeoffs when ported into non-native env ...

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This k ...

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with t ...

Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules2, 3, 4-5. Despite recent ad ...

De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, ...

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes1–4 to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off- ...

Martin Pacesa

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Design of Highly Functional Genome Editors by Modelling Crispr-cas Sequences

De novo design of protein interactions with learned surface fingerprints

Past, present, and future of CRISPR genome editing technologies

Targeting protein-ligand neosurfaces with a generalizable deep learning tool

Computational design of soluble and functional membrane protein analogues

Custom CRISPR—Cas9 PAM variants via scalable engineering and machine learning