Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Author:

Prabakaran R1ORCID,Rawat Puneet2ORCID,Kumar Sandeep3,Gromiha M Michael4ORCID

Affiliation:

1. Indian Institute of Technology Madras, India

2. Indian Institute of Technology, Madras, India

3. Department of Biotherapeutics Discovery in Boehringer-Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA

4. Bharathidasan University, India

Abstract

Abstract Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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