MetAmyl: A METa-Predictor for AMYLoid Proteins

Abstract

The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimer's, Huntington's and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the initiation of fibril formation remain largely unknown. Several lines of evidence revealed that short amino-acid segments (hot spots), located in amyloid precursor proteins act as seeds for fibril elongation. Therefore, hot spots are potential targets for diagnostic/therapeutic applications, and a current challenge in bioinformatics is the development of methods to accurately predict hot spots from protein sequences. In this paper, we combined existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors. We evaluated the performances of MetAmyl through a large scale comparative study based on three independent datasets and thus demonstrated its ability to differentiate between amyloidogenic and non-amyloidogenic polypeptides. Compared to 9 other methods, MetAmyl provides significant improvement in prediction on studied datasets. We further show that MetAmyl is efficient to highlight the effect of point mutations involved in human amyloidosis, so we suggest this program should be a useful complementary tool for the diagnosis of these diseases.

Structure of FUS Protein Fibrils and Its Relevance to Self-Assembly and Phase Separation of Low-Complexity Domains

“…S7). Aggrescan, FISH, FoldAmyloid, MetAmyl, and PASTA 2.0 predict a low propensity for amyloid formation across the FUS-LC sequence, presumably because these algorithms emphasize the importance of hydrophobic interactions (de Groot et al, 2012; Emily et al, 2013; Garbuzynskiy et al, 2010; Gasior and Kotulska, 2014; Walsh et al, 2014). ZipperDB, Zyggregator, and WALTZ, which include other physicochemical and structural properties of protein segments (Thompson et al, 2006) (Tartaglia and Vendruscolo, 2008) (Maurer-Stroh et al, 2010), predict that certain short segments of FUS-LC may form amyloidlike structures, but with no consensus on the identities of these segments.…”

Contact-dependent killing by Caulobacter crescentus via cell surface-associated, glycine zipper proteins

“…The protein aggregation propensity and amylogenic regions of CdzC, CdzD and CdzI were predicted based on amino acid sequence using AmylPred 2 (Tsolis et al, 2013), WALZ (high specificity threshold) (Oliveberg, 2010), MetAmyl (with high specificity threshold) (Emily et al, 2013) and PASTA 2.0 (with peptides threshold) (Walsh et al, 2014). A sequence region was considered aggregation prone if at least three of the above algorithms were in accordance.…”

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

“…Normal proteins can become toxic upon fibrillation [Bucciantini et al, 2004] and other proteins not related to amyloid diseases can aggregate under destabilizing conditions [Chiti et al, 1999]. Numerous tools predict amyloidogenic regions in protein sequences [Tartaglia et al, 2008;Garbuzynskiy et al, 2010;Maurer-Stroh et al, 2010;Emily et al, 2013]. Amino acid substitutions can increase the tendency of amyloidogenic proteins to aggregate.…”