2019/12/31 by Carlos A. Gandarilla-Pérez, Pierre Mergny, Martin Weigt +2 · 13 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bioinformatics and Genomic Networks #Computer science #Data mining #Gene Regulatory Network Analysis #Inference #Machine learning #Mathematics #Monte Carlo method #Noise (video) #Physics #Protein Structure and Dynamics #Quality (philosophy) #Scaling #Set (abstract data type) #Statistics #Theoretical computer science #cond-mat.stat-mech #physics.bio-ph #q-bio.BM
paper · pdf · doi:10.1103/physreve.101.032413
published in Physical review. E 101(3), 032413 (American Physical Society) · 18 pages, 16 figures
arxiv created 2020/03/16 · openalex publication_date 2020/03/20 · arxiv updated 2020/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.