babappalign 1.2.0

Embedding-first deep learning multiple sequence alignment engine with affine-gap DP

BABAPPAlign

Overview

BABAPPAlign is an embedding-first progressive multiple sequence alignment (MSA) engine for protein and coding nucleotide sequences.

It integrates pretrained protein language model embeddings with a learned neural residue–residue scoring function within a classical, exact affine-gap dynamic programming framework (Gotoh).

Version 1.2.0 introduces native codon alignment mode, allowing direct CDS alignment without requiring external PAL2NAL.

BABAPPAlign is fully functional on CPU-only systems. GPU acceleration is optional and affects performance only, not correctness.

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Key Features

• Progressive multiple sequence alignment (MSA)
• Strict learned residue–residue scoring model (BABAPPAScore)
• Pretrained protein language model residue embeddings
• Column-aware profile scoring
• True affine-gap dynamic programming (Gotoh algorithm)
• Exact dynamic programming (no heuristics inside DP)
• Neural inference performed outside DP recursion
• Native codon alignment mode (CDS → translate → back-map)
• Automatic frame validation in codon mode
• CPU-only compatible
• Optional GPU acceleration
• Explicit model specification (no silent fallback)
• Reproducible and Zenodo-backed model distribution

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Installation

Install from PyPI:

pip install babappalign

This installs a CPU-compatible version. No GPU or CUDA is required.

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Quick Start

Protein alignment (default)

babappalign input.fasta --model babappascore

Output:

input.protein.aln.fasta

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Codon alignment (v1.2.0)

babappalign cds.fasta --model babappascore --mode codon

Outputs:

cds.protein.aln.fasta
cds.codon.aln.fasta

No -o option is required. Output filenames are generated automatically.

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Codon Mode Details

When --mode codon is enabled:

1. CDS sequences are validated:

   • Length divisible by 3
   • No internal stop codons
   • Valid nucleotide alphabet

2. Sequences are translated to protein.

3. Alignment is performed in protein space using the learned neural scoring model.

4. Aligned proteins are back-mapped to codon alignment (PAL2NAL-style logic).

Gap penalties are automatically scaled in codon mode for biological consistency.

No external PAL2NAL dependency is required.

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How BABAPPAlign Works

1. Residue Embedding
   Protein sequences are converted into residue-level embeddings using a pretrained protein language model.

2. Learned Residue Scoring
   Residue compatibility is evaluated using a pretrained neural scoring model (BABAPPAScore), replacing traditional substitution matrices.

3. Progressive Alignment
   Sequences are progressively aligned using exact affine-gap dynamic programming (Gotoh). Neural inference is performed outside the DP recursion to preserve correctness.

The progressive ordering is a computational heuristic and is not interpreted as a phylogeny.

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Alignment Core Integrity

The alignment engine uses:

• Three-state affine-gap DP (M, Ix, Iy)
• Explicit traceback matrices
• Exact dynamic programming
• No heuristic shortcuts inside recursion

Version 1.2.0 does not modify the alignment core logic. Scientific reproducibility from earlier versions is preserved.

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Model Weights (Required)

BABAPPAlign requires a trained neural residue-level scoring model (BABAPPAScore), distributed separately via Zenodo.

Concept DOI (all versions):

https://doi.org/10.5281/zenodo.18053200

Download model:

mkdir -p ~/.cache/babappalign/models

wget https://zenodo.org/record/18053201/files/babappascore.pt \
  -O ~/.cache/babappalign/models/babappascore.pt

Run using cached model name:

babappalign input.fasta --model babappascore

Or using explicit path:

babappalign input.fasta \
  --model ~/.cache/babappalign/models/babappascore.pt

At runtime, BABAPPAlign prints the resolved model path and checksum for reproducibility.

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CPU and GPU Execution

BABAPPAlign produces identical alignments on CPU and GPU. GPU acceleration affects performance only.

Component CPU GPU

Progressive alignment (DP) Yes Yes Learned scoring Yes Yes Embedding generation Slower Faster

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Input Requirements

Protein mode:

• Protein FASTA sequences

Codon mode:

• CDS nucleotide FASTA sequences
• Length divisible by 3
• No internal stop codons

No strict limits on sequence number or length (runtime depends on hardware).

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Command Line Interface

babappalign --help

Key options:

--model MODEL           (mandatory)
--mode {protein,codon}
--gap-open FLOAT
--gap-extend FLOAT
--device {cpu,cuda}

Output filenames are generated automatically.

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License

MIT License. See LICENSE file.

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Citation

Manuscript in preparation.

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Author

Krishnendu Sinha https://github.com/sinhakrishnendu/BABAPPAlign