Computational Drug Discovery and Design

Chapter 21

AGGRESCAN3D: Toward the Prediction of the Aggregation

Propensities of Protein Structures

Jordi Pujols, Samuel Pen ̃a-Dı ́az, and Salvador Ventura

Abstract

Protein aggregation is responsible for the onset and spread of many human diseases, ranging from
neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the
production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web
server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of
sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows
the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational
fluctuations on the aggregation of native polypeptides.

Key wordsAGGRESCAN3D, Bioinformatics, 3D structure, Protein aggregation, Protein misfold-

ing, Protein production, Protein solubility

1 Introduction

Protein aggregation is currently considered to be a generic property

of the vast majority of existing polypeptides [1, 2]. It is triggered by

the permanent or transient exposure of specific clusters of amino

acids, named Aggregation Prone Regions (APR) or “hot spots”,

mainly composed by hydrophobic residues. These clusters are able

to form non native intermolecular contacts that promote protein

self-assembly and deposition into insoluble proteinaceous aggre-

gates [3, 4]. Remarkably, these threatening stretches are present

and somehow conserved across all phylogenetic kingdoms, consti-

tuting a paradox in protein evolution and biochemistry. It is

assumed that they cannot be purged from protein sequences due

to a shocking overlap between the physicochemical principles

underlying these type of aberrant intermolecular contacts and

those that govern native interactions, protein interfaces and struc-

ture compaction [1, 5–7]. Accordingly, cellular proteomes

have developed orthogonal protecting strategies, best illustrated

by the chaperone-proteasome machinery, to prevent an imbalance

Mohini Gore and Umesh B. Jagtap (eds.),Computational Drug Discovery and Design, Methods in Molecular Biology, vol. 1762,
https://doi.org/10.1007/978-1-4939-7756-7_21,©Springer Science+Business Media, LLC, part of Springer Nature 2018

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