Gabriele Orlando1,2, Alexandra Silva3,4, Sandra Macedo-Ribeiro3,4, Daniele Raimondi5, Wim Vranken1,2,6. 1. Interuniversity Institute of Bioinformatics in Brussels, ULB/VUB, Triomflaan, Brussels 1050, Belgium. 2. Structural Biology, Vrije Universiteit Brussel, Brussels 1050, Belgium. 3. IBMC-Instituto de Biologia Molecular e Celular. 4. Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto 4200-135, Portugal. 5. ESAT-STADIUS, KU Leuven, Leuven 3001, Belgium. 6. Centre for Structural Biology, VIB, Brussels 1050, Belgium.
Abstract
MOTIVATION: Protein beta-aggregation is an important but poorly understood phenomena involved in diseases as well as in beneficial physiological processes. However, while this task has been investigated for over 50 years, very little is known about its mechanisms of action. Moreover, the identification of regions involved in aggregation is still an open problem and the state-of-the-art methods are often inadequate in real case applications. RESULTS: In this article we present AgMata, an unsupervised tool for the identification of such regions from amino acidic sequence based on a generalized definition of statistical potentials that includes biophysical information. The tool outperforms the state-of-the-art methods on two different benchmarks. As case-study, we applied our tool to human ataxin-3, a protein involved in Machado-Joseph disease. Interestingly, AgMata identifies aggregation-prone residues that share the very same structural environment. Additionally, it successfully predicts the outcome of in vitro mutagenesis experiments, identifying point mutations that lead to an alteration of the aggregation propensity of the wild-type ataxin-3. AVAILABILITY AND IMPLEMENTATION: A python implementation of the tool is available at https://bitbucket.org/bio2byte/agmata. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION:Protein beta-aggregation is an important but poorly understood phenomena involved in diseases as well as in beneficial physiological processes. However, while this task has been investigated for over 50 years, very little is known about its mechanisms of action. Moreover, the identification of regions involved in aggregation is still an open problem and the state-of-the-art methods are often inadequate in real case applications. RESULTS: In this article we present AgMata, an unsupervised tool for the identification of such regions from amino acidic sequence based on a generalized definition of statistical potentials that includes biophysical information. The tool outperforms the state-of-the-art methods on two different benchmarks. As case-study, we applied our tool to humanataxin-3, a protein involved in Machado-Joseph disease. Interestingly, AgMata identifies aggregation-prone residues that share the very same structural environment. Additionally, it successfully predicts the outcome of in vitro mutagenesis experiments, identifying point mutations that lead to an alteration of the aggregation propensity of the wild-type ataxin-3. AVAILABILITY AND IMPLEMENTATION: A python implementation of the tool is available at https://bitbucket.org/bio2byte/agmata. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Luciano Kagami; Joel Roca-Martínez; Jose Gavaldá-García; Pathmanaban Ramasamy; K Anton Feenstra; Wim F Vranken Journal: BMC Mol Cell Biol Date: 2021-04-23