{
  "abstract": "Dynamic programming (DP) yields exact quadratic-time (O(NM)) pairwise sequence alignments. Static banding heuristics (O(NW)) fail catastrophically on low-identity (<30%), asymmetric insertions/deletions (indels), or extreme length ratios, dropping core-block Sum-of-Pairs (SP) score recovery to 20%-50%. Conversely, recent protein language model (PLM) aligners evaluate all N x M cells without search grid constraints. To bridge this gap, we introduce Adaptive-Banding Needleman-Wunsch (AB-NW), pruning the search space without sacrificing sequence alignment signal by leveraging PLM contextual representations to construct a confidence-adaptive DP corridor prior to fine-resolution DP while keeping downstream scoring unmodified. AB-NW downsamples residue embeddings, computes a coarse alignment, and sets per-row corridor bounds via normalized confidence metrics. Evaluated via JIT-compiled buffers, this reduces time complexity to O(NW_bar) and space to O(NW_max), where W_bar << M. Benchmarked across three PLM backbones (ESM2-8M, ESM2-35M, ProtBERT) across nine structural challenge categories, AB-NW recovers >98.9% of exact unconstrained alignment scores and core-block SP accuracy across static banding failure modes (Twilight Zone, Asymmetric Indels, Extreme Aspect Ratios) while eliminating 55.3%-78.8% of active DP cells. On large protein sequence matrices (N, M >= 3,700), AB-NW eliminates 87.6%-91.7% of cells, achieving speedups of 9.79x-13.30x (pure DP) and 1.73x-2.94x (end-to-end), reaching up to 18.12x on unbiased controls (p < 0.05 to p < 10^-15), making AB-NW practical for large-scale, high-throughput sequence alignment pipelines.",
  "abstract_shown": true,
  "affiliations": [
    "University of Engineering and Technology Lahore"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. Shoaib"
    },
    {
      "name": "W. Ali"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4dexeexhayftd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.26.747234",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.26.747234v2.full",
  "id": "4dexeexhayftd",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.26.747234v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Genomics and Phylogenetic Studies"
    ]
  ],
  "openalex": "W7204461866",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.26.747234v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4dexeexhayftd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:30Z",
      "native_id": "10.64898/2026.08.26.747234",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.26.747234v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Pruning the Search, Not the Signal: Adaptive-Banding Needleman-Wunsch Sequence Alignment via Protein Language Model Confidence",
  "title_text": "Pruning the Search, Not the Signal: Adaptive-Banding Needleman-Wunsch Sequence Alignment via Protein Language Model Confidence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:29Z",
  "url": "https://research.draconitic.com/paper/4dexeexhayftd",
  "venue": "bioRxiv",
  "versions": []
}