A3DyDB

A3DyDB predicts structure-based aggregation propensities for 6,039 Saccharomyces cerevisiae proteins by applying Aggrescan 3D (A3D) to AlphaFold2 (AF2) structural models to characterize aggregation-prone regions and inform analyses of protein stability and solubility.


Key Features:

  • Proteome-wide aggregation predictions: Provides aggregation propensity scores for 6,039 S. cerevisiae proteins derived from AlphaFold2 (AF2) structural models.
  • Structure-based aggregation analysis: Uses the Aggrescan 3D (A3D) algorithm to compute intrinsic aggregation propensities from three-dimensional protein structures.
  • Mutation impact evaluation: Assesses effects of natural or engineered mutations on predicted protein aggregation, stability, and solubility.
  • Proteome-level correlation analyses: Enables investigation of correlations between structural aggregation propensity and other protein properties across the yeast proteome.

Scientific Applications:

  • Modeling protein aggregation: Supports structural modeling of aggregation processes relevant to protein misfolding and related disease studies.
  • Functional proteomics: Links structure-derived aggregation propensity with protein function and interaction studies in Saccharomyces cerevisiae.
  • Genetic engineering and design: Informs design of mutations to modulate protein stability and solubility for experimental and synthetic biology applications.

Methodology:

Aggrescan 3D (A3D) processes AlphaFold2 (AF2) structural models to compute intrinsic aggregation propensities for the included S. cerevisiae proteins.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Added:
3/18/2024
Last Updated:
11/24/2024

Operations

Data Inputs & Outputs

Publications

Garcia-Pardo J, Badaczewska-Dawid AE, Pintado-Grima C, Iglesias V, Kuriata A, Kmiecik S, Ventura S. A3DyDB: exploring structural aggregation propensities in the yeast proteome. Microbial Cell Factories. 2023;22(1). doi:10.1186/s12934-023-02182-3. PMID:37716955. PMCID:PMC10504709.

PMID: 37716955
Funding: - Spanish Ministry of Science and Innovation: Juan de la Cierva Incorporacion IJC2019-041039-I - European Cooperation in Science and Technology: COST Action ML4NGP CA21160 - European Commission: NextGenerationEU, PhasAge H2020-WIDESPREAD-2020-5 - Secretariat of Universities and Research of the Catalan Government and the European Social Fund: 2023 FI_3 00018 - National Science Centre, Sheng: 2021/40/Q/NZ2/00078 - Ministerio de Ciencia e Innovación: PID2019-105017RB-I00 - Institució Catalana de Recerca i Estudis Avançats: ICREA-Academia 2020