Computational approaches for inferring the functions of intrinsically disordered proteins

Abstract

Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

Keywords: IDP ensembles; IDP function; disorder prediction; intrinsically disordered proteins; protein ensemble database.

Figure 1

Schematic view of the two main ensemble modeling approaches. Pool-based ensemble modeling (left) starts by generating a pool of random or semi-random conformations based on the protein sequence. Subsets of conformations are selected iteratively from the pool and theoretical parameters are calculated for each conformer in the subset. The final ensemble consists of conformations for which the theoretical parameters are in agreement with the experimental data. MD-based approaches start by initiating short replica MD simulations in parallel using an initial conformation. The MD replicas are constrained with the experimental data. The final ensemble is a combination of the resulting replica runs.