{
  "abstract": "Excess sodium intake remains a major public health challenge, while salty and saltiness-enhancing peptides offer a potential route to preserve sensory saltiness in reduced-sodium foods. Machine-learning studies of salty peptides, however, are constrained by small datasets, heterogeneous evidence standards, uncertain negative labels, and sequence similarity leakage. Here we present SaltyMeta, a curated benchmark and web-accessible screening framework for salty or saltiness-enhancing short peptides. The benchmark contains 580 peptides, including 280 positive peptides and 300 negative peptides, all standardized to 2-15 residue one-letter amino-acid sequences. Quality control found no non-standard residues, exact duplicates, or positive-negative overlaps. A similarity-grouped split retained 456 peptides for training and 124 for held-out testing. We evaluated 548 interpretable peptide descriptors, frozen ESM2 embeddings at 8M, 35M, and 150M parameter scales, and descriptor-embedding fusion models under grouped cross-validation. The traditional ExtraTrees baseline selected by training-set grouped cross-validation achieved ROC-AUC=0.693 in cross-validation and ROC-AUC=0.704, PR-AUC=0.702, F1=0.626, and MCC=0.304 on the held-out test set. The best initial protein-language-model fusion was traditional descriptors plus ESM2-8M embeddings, with grouped CV ROC-AUC=0.696 and test ROC-AUC=0.700. Advanced optimization using PCA95 dimensionality reduction and ExtraTrees feature-importance filtering yielded a practical ESM2-8M PCA95 top-300 model with test ROC-AUC=0.715 and PR-AUC=0.703, although repeated-CV gains remained modest. SaltyMeta is a transparent prioritization tool, not a sensory validation substitute. We provide benchmark, models, scripts, GitHub, and Streamlit for reproducible screening of food-derived peptides.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16809",
  "author_total": 4,
  "authors": [
    {
      "name": "Wanchao Chen"
    },
    {
      "name": "Wen Li"
    },
    {
      "name": "Yanan He"
    },
    {
      "name": "Yan Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7fyewkhc7f80n.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16809",
  "fulltext_url": null,
  "id": "7fyewkhc7f80n",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16809",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.BM"
    ],
    [
      "openalex",
      "2916|Biochemical Analysis and Sensing Techniques"
    ]
  ],
  "openalex": "W7213408817",
  "pdf_url": "https://arxiv.org/pdf/2609.16809",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7fyewkhc7f80n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:46Z",
      "native_id": "2609.16809",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16809"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "arxiv:q-bio.BM",
      "subfield": "biophysics"
    }
  ],
  "title": "SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction",
  "title_text": "SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:46Z",
  "url": "https://research.draconitic.com/paper/7fyewkhc7f80n",
  "venue": "arXiv",
  "versions": []
}