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BABAPPAlign: a multiple sequence alignment engine with a learned residue-level scoring function.

Krishnendu Sinha1

  • 1Department of Zoology, Jhargram Raj College, Jhargram, West Bengal 721507, India.

Bioinformatics (Oxford, England)
|April 17, 2026
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Summary
This summary is machine-generated.

BABAPPAlign introduces a novel protein sequence alignment method using trained residue-level scoring on language model embeddings. This approach enhances accuracy over traditional static matrices, outperforming existing tools in benchmarks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Protein Sequence Analysis

Background:

  • Traditional multiple sequence alignment (MSA) methods utilize static substitution matrices.
  • These matrices lack adaptability to sequence-specific contexts, limiting alignment accuracy.
  • There is a need for dynamic, context-aware scoring in MSA.

Purpose of the Study:

  • To develop a novel progressive MSA engine, BABAPPAlign.
  • To replace static substitution scoring with a trained residue-level scorer.
  • To integrate codon-aware alignment capabilities.

Main Methods:

  • Implemented a progressive MSA engine (BABAPPAlign) in Python.
  • Utilized fixed protein-language-model embeddings for residue-level scoring.
  • Retained exact affine-gap dynamic programming.
  • Incorporated an integrated codon-aware alignment mode.

Main Results:

  • BABAPPAlign demonstrated superior performance compared to in-engine controls (EBA-style cosine, BLOSUM62).
  • The learned backend significantly outperformed MAFFT in benchmark tests.
  • Validation was performed using BAliBASE, PREFAB, and HOMSTRAD datasets.

Conclusions:

  • BABAPPAlign offers a more accurate and adaptive approach to multiple sequence alignment.
  • The use of protein language model embeddings represents a significant advancement in MSA.
  • The tool provides an accessible and effective solution for complex sequence alignment tasks.