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Oriol Gracia Carmona1,2, Vilde Leipart1,3, Gro V Amdam3,4

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Summary
This summary is machine-generated.

Protein language models (PLMs) now predict genetic variant impact effectively. A new tool, IndeLLM, accurately scores indel pathogenicity using only sequence data, enhancing variant interpretation.

Keywords:
indelsinterpretabilitypathogenicity predictorsprotein language modelstransfer learningzero-shot inference

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Protein language models (PLMs) are powerful tools for predicting genetic variant pathogenicity.
  • Assessing in-frame insertions and deletions (indels) remains a challenge due to altered protein lengths and limited data.

Purpose of the Study:

  • To develop a novel scoring approach, IndeLLM, for evaluating indel pathogenicity.
  • To create a user-friendly tool for analyzing indel effects on protein sequence and structure.

Main Methods:

  • Developed IndeLLM, a zero-shot scoring method utilizing only protein sequence information.
  • Implemented a Siamese network using transfer learning for enhanced indel pathogenicity prediction.
  • Created a plug-and-play Google Colab notebook for accessibility.

Main Results:

  • IndeLLM achieves performance comparable to existing predictors with minimal computational resources.
  • The Siamese network model achieved a Matthews correlation coefficient of 0.77, outperforming other indel predictors.
  • The tool facilitates visualization of indel impacts on protein sequence and structure.

Conclusions:

  • IndeLLM offers an accessible and efficient method for assessing indel pathogenicity.
  • The developed Siamese network significantly improves the prediction of indel effects.
  • This work enhances the utility of PLMs for variant interpretation, particularly for indels.