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Related Experiment Video

Updated: Jul 11, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.

Ketrin Gjoni^1,2, Katherine S Pollard^1,2,3

¹Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA.

Biorxiv : the Preprint Server for Biology

|November 14, 2023

Summary

This summary is machine-generated.

Sequence Mutator for Predictive Models (SuPreMo) enables in silico mutagenesis for genome editing. This tool helps prioritize pathogenic variants and discover functional sequences using machine learning models.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Computational genome editing is crucial but faces limitations with structural variants and data input requirements.
Existing methods struggle to pinpoint specific sequence changes responsible for functional effects.

Approach:

Introducing Sequence Mutator for Predictive Models (SuPreMo), a scalable tool for in silico mutagenesis.
SuPreMo facilitates the generation of reference and perturbed sequence pairs.

Key Points:

SuPreMo overcomes limitations of current genome editing tools.
Enables efficient in silico mutagenesis for various bioinformatics tasks.
Facilitates variant prioritization and functional sequence discovery.

Conclusions:

SuPreMo provides a comprehensive solution for in silico genome sequence manipulation.
Pairs of reference and perturbed sequences generated by SuPreMo can be integrated with machine learning models.
This integration aids in prioritizing pathogenic variants and discovering novel functional sequences.