An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins

Puneet Rawat, Sandeep Kumar, M. Michael Gromiha

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protein aggregates, amyloid fibrils and plaques. In-vitro experiments have shown that even single amino acid substitutions can drastically enhance or mitigate protein aggregation kinetics. In this work, we have collected a dataset of 220 unique mutations in 25 proteins and classified them as enhancers or mitigators on the basis of their effect on protein aggregation rate. The data were analyzed via machine learning to identify features capable of distinguishing between aggregation rate enhancers and mitigators. Our initial Support Vector Machine (SVM) model separated such mutations with an overall accuracy of 69%. When local secondary structures at the mutation sites were considered, the accuracies further improved by 13–15%. The machine-learnt features are distinct for each secondary structure class at mutation sites. Protein stability and flexibility changes are important features for mutations in α-helices. β-strand propensity, polarity and charge become important when mutations occur in β-strands and ability to form secondary structure, helical tendency and aggregation propensity are important for mutations lying in coils. These results have been incorporated into a sequence-based algorithm (available at http://www.iitm.ac.in/bioinfo/aggrerate-disc/) capable of predicting whether a mutation will enhance or mitigate a protein's aggregation rate. This algorithm will find several applications towards understanding protein aggregation in human diseases, enable in-silico optimization of biopharmaceuticals and enzymes for improved biophysical attributes and de novo design of bio-nanomaterials.

Original languageEnglish
Pages (from-to)1157-1167
Number of pages11
JournalInternational Journal of Biological Macromolecules
Volume118
DOIs
StatePublished - 15 Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Funding

We thank Bioinformatics Infrastructure facility, Department of Biotechnology and Indian Institute of Technology Madras for computational facilities and Ministry of human resource and development (MHRD) for HTRA scholarship to PR. The work was partially supported by the Department of Biotechnology, Government of India to MMG (BT/PR16710/BID/7/680/2016). We thank Bioinformatics Infrastructure facility, Department of Biotechnology and Indian Institute of Technology Madras for computational facilities and Ministry of human resource and development (MHRD) for HTRA scholarship to PR. The work was partially supported by the Department of Biotechnology, Government of India to MMG ( BT/PR16710/BID/7/680/2016 ).

FundersFunder number
Department of Biotechnology , Ministry of Science and TechnologyBT/PR16710/BID/7/680/2016
Department of Biotechnology, Government of West Bengal

    Keywords

    • Aggregation rate
    • Amyloids
    • Machine learning
    • Support vector machine

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