AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides
By Ò. Conchillo-Solé, Natalia Sanchez de Groot, Francesc X. Avilés, Josep Vendrell, Xavier Daura, Salvador Ventura. BMC Bioinformatics, 2007
996 citations recorded by the source corpus.
BackgroundProtein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimer's and Parkinson's diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets.ResultsAGGRESCAN is based on an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.ConclusionBy identifying aggregation-prone segments in proteins, AGGRESCAN http://bioinf.uab.es/aggrescan/ shall facilitate (i) the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases and (ii) the anticipation of aggregation phenomena during storage or recombinant production of bioactive polypeptides or polypeptide sets.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/424215fe7326e0b41d5d44bd5aa1474ecb5dffe3
Citations in the corpus
- Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013, neutral): “The accuracy of three methods (Fold-Amyloid, Aggrescan and Waltz-Best performance) is poor with the average sensitivity and specificity (balanced accuracy) around 50 % and the MCC value between -0.04 and 0.01 for this dataset.”
- PASTA 2.0: an improved server for protein aggregation prediction (2014, neutral): “The two FoldAmyloid variants contact (26) and triple hybrid FoldAmyloid (14) and AGGRESCAN (27) were substantially lower with AUCs 1.18, 2.31 and 2.65 respectively.”
- Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, neutral): “Methods for predicting the effect of amino acid substitutions on amyloidogenic proteins include AGGRESCAN [Conchillo-Sole et al., 2007], PASTA2.”