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Computational approaches for inferring the functions of intrinsically disordered proteins.

Varadi M, Vranken W, Guharoy M, Tompa P - Front Mol Biosci (2015)

Bottom Line: 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.Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

View Article: PubMed Central - PubMed

Affiliation: Flemish Institute of Biotechnology Brussels, Belgium ; Department of Structural Biology, VIB, Vrije Universiteit Brussels Brussels, Belgium.

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.

No MeSH data available.


Related in: MedlinePlus

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.
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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.

Mentions: In response to this challenge, a number of approaches were developed that combine experimental data with computational methodology with the aim to accurately describe the full conformational ensemble adopted by IDPs. Experimental data from techniques that rely on measurements performed in solution are particularly well suited for studying the dynamic structure of an IDP, even though they often represent an average over the different conformations that are adopted by the IDP. These experimental measurements predominantly include NMR-derived parameters, such as chemical shifts (CSs) (Jensen et al., 2011), residual dipolar couplings (RDCs) (Mittag et al., 2010b), paramagnetic relaxation enhancements (PREs) (Mittag et al., 2010b), and J-couplings (Mittag et al., 2010b), as well as scattering intensities from small-angle X-ray scattering (SAXS) (Allison et al., 2009) and probe distances from Forster resonance energy transfer (FRET) (Haas, 2012). These experimental data are then combined with computational methods to determine an ensemble of conformations for an IDP, with two main approaches being used; the first approach is referred to as pool-based modeling, while the second one is based on molecular dynamics (MD) simulations (Tompa and Varadi, 2014) (Figure 1).


Computational approaches for inferring the functions of intrinsically disordered proteins.

Varadi M, Vranken W, Guharoy M, Tompa P - Front Mol Biosci (2015)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525029&req=5

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.
Mentions: In response to this challenge, a number of approaches were developed that combine experimental data with computational methodology with the aim to accurately describe the full conformational ensemble adopted by IDPs. Experimental data from techniques that rely on measurements performed in solution are particularly well suited for studying the dynamic structure of an IDP, even though they often represent an average over the different conformations that are adopted by the IDP. These experimental measurements predominantly include NMR-derived parameters, such as chemical shifts (CSs) (Jensen et al., 2011), residual dipolar couplings (RDCs) (Mittag et al., 2010b), paramagnetic relaxation enhancements (PREs) (Mittag et al., 2010b), and J-couplings (Mittag et al., 2010b), as well as scattering intensities from small-angle X-ray scattering (SAXS) (Allison et al., 2009) and probe distances from Forster resonance energy transfer (FRET) (Haas, 2012). These experimental data are then combined with computational methods to determine an ensemble of conformations for an IDP, with two main approaches being used; the first approach is referred to as pool-based modeling, while the second one is based on molecular dynamics (MD) simulations (Tompa and Varadi, 2014) (Figure 1).

Bottom Line: 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.Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

View Article: PubMed Central - PubMed

Affiliation: Flemish Institute of Biotechnology Brussels, Belgium ; Department of Structural Biology, VIB, Vrije Universiteit Brussels Brussels, Belgium.

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.

No MeSH data available.


Related in: MedlinePlus