AGGRESCAN: a server for the prediction and evaluation of ”hot spots” of aggregation in polypeptides. BACKGROUND: Protein 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. RESULTS: AGGRESCAN 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. CONCLUSION: By 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
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Alves Moreira, Carlos; Philot, Eric Allison; Lima, Angélica Nakagawa; Scott, Ana Ligia: Predicting regions prone to protein aggregation based on SVM algorithm (2019)
- Amina Asif, Wajid Arshad Abbasi, Farzeen Munir, Asa Ben-Hur, Fayyaz ul Amir Afsar Minhas: pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications (2017) arXiv
- David, Maria Pamela C.; Concepcion, Gisela P.; Padlan, Eduardo A.: Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies (2010) ioport