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TASK OVERVIEW

Extract knowledge from all publicly available sources regarding protein sequence-to-function relationships to empower future protein and gene reengineering efforts against aging.

THE PROBLEM

Given a human protein X — how do we extract knowledge from all publicly available sources regarding its sequence-to-function relationship to empower future protein and gene reengineering efforts?

Protein reengineering efforts are often bottlenecked by lack of sufficient sequence-to-function data that would inform first rounds of designs. This challenge aims to create a comprehensive knowledge base of known protein modifications linked to functional outcomes in experiments.

MISSION AND GOALS

The mission is to speed up research on protein engineering, especially in the context of aging. The aggregated data will help researchers identify the most promising approaches to modifying wild-type protein sequences.

Essentially, an agent is expected to reproduce a GenAge type database but writing actual articles about the protein/gene sequence-to-function relationships related to longevity.

SYSTEM REQUIREMENTS

  1. Use wikicrow as reference. For starters, you can use WikiCrow by FutureHouse as a reference format (Wikipedia-style articles about genes, e.g. APOE).
  2. Mapping protein/gene sequence to function. This is the key requirement! The system must establish clear relationships between protein/gene sequences and their functional outcomes related to longevity.
  3. Comprehensive articles Write articles about protein/gene sequence-to-function relationships related to longevity. Include information about: – Evolutionary conservation – Known genetic interventions – Orthologs /p aralogs across species – Mutant strains data
  4. Bonus features – Small molecule binding data — integrate binding information for additional context – Tunable coarse-graining — from individual nucleotides/amino acids to larger domains or even families of domains

EVALUATION FRAMEWORK

  1. Breadth of Coverage (25%). Can your approach be applied to any human gene?
  2. Depth of Evidence (25%). Can your approach recover at least 5 various sources of modifications for each gene?
  3. Relation to Aging (30%). Is your source of protein sequence modification data relevant to aging? Is there association with lifespan?
  4. Source Citations (20%). Bonus points if agent extracts original figures with key data from source studies and cites them in the article.

REQUIRED OUTPUT

DATA STRUCTURE

– Gene/Protein Name/ID <> – Protein/DNA Sequence <> – Interval in Sequence <> – Function (text format)

PROTEIN IDENTIFIER

Use standard protein name and/or Uniprot ID linked to a protein sequence

ANNOTATIONS TABLE

Specify intervals in the protein sequence & introduced modifications and the change in function the modifications induced

TEST CASES AND VALIDATION

Test your agent with these specific proteins to validate its capability to extract comprehensive sequence-to-function relationships:

TEST CASE 1: NRF2 Your agent should be able to find: – Neoaves have a KEAP1 mutation that leads to over-active NRF2 (https://pmc.ncbi.nlm.nih.gov/articles/PMC7234996/) – SKN-1 (nematode's ortholog of NRF2) increases lifespan in C.elegans (https://pubmed.ncbi.nlm.nih.gov/28612944/)

TEST CASE 2: SOX2 Should be able to recover the results of SuperSOX: – SuperSOX study (https://www.sciencedirect.com/science/article/pii/S1934590923004022) — Modified SOX2 with enhanced reprogramming capabilities

TEST CASE 3: APOE Should recover all major APOE variants and their longevity associations: – APOE2 — protective variant associated with longevity – APOE3 — common neutral variant – APOE4 — risk variant for Alzheimer's and reduced longevity

TEST CASE 4: OCT4 Should recover papers converting OCT6 into a reprogramming factor: – EMBR study (https://www.embopress.org/doi/full/10.15252/embr.201642958) — Converting OCT6 into reprogramming factor through sequence modifications

TECHNICAL SPECIFICATIONS

Knowledge base structure: – Gene/Protein Name/ID – Protein/DNA Sequence – Interval in Sequence – Function (Text Format) – Modification Effects – Longevity Association

The desired structure should enable researchers to quickly identify sequence intervals of interest, understand their functional roles, and see how modifications in those regions affect longevity-related outcomes.