Abstract

Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule–IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions.

Highlights

  • Proteins are central components of regulatory networks that dictate virtually all aspects of cellular decision-making [1]

  • Given the fundamental challenges of disordered ensemble modeling based on experimental restraints alone, physics-based atomistic simulations have a crucial role to play in helping elucidate the conformational properties of intrinsically disordered proteins (IDPs) and establishing a reliable molecular basis of their function and regulation [72,73,74,75,76]

  • The results revealed that cyclized nordihydroguaiaretic acid (cNDGA) induced modest compaction of the conformational ensemble of monomeric α-synuclein, apparently mediated by dynamic and transient interactions with the protein and without hindering membrane association. cNDGA-treated α-synuclein is resistant to aggregation even when seeded with α-synuclein aggregates

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Summary

Introduction

Proteins are central components of regulatory networks that dictate virtually all aspects of cellular decision-making [1]. A wide range of biophysical methods can be applied to characterize disordered protein states, including NMR, circular dichroism (CD), small-angle X-ray scattering (SAXS), Förster resonance energy transfer (FRET), hydrogen/deuterium (H/D) exchange, mass spectrometry, and others [57,58] These methods can provide complementary information on the local, intermediate, and long-range structural organizations of IDPs. NMR in particular is arguably the most powerful technique for structural studies of IDP. The most robust methods generally involve first generating a large number of candidate random structures and using experimental structural restraints to select and construct optimal sub-ensembles according to various statistical criteria [62,63,64,65,66,67,68,69,70,71] These methods rely critically on the ability to generate initial candidate structures that are diverse enough to cover the range of accessible states of the protein and specific enough to contain any nontrivial local (and long-range) structure features associated with a particular protein state. These experimental restraint-based ensemble construction approaches are likely inadequate for capturing potentially subtle effects of ligand binding on the disordered ensemble

Recent Advances in de Novo Simulations of Disordered Protein Ensembles
Overcoming Sampling Bottleneck using Enhanced Sampling and GPU Computing
Modulating Disordered Protein Ensembles via Dynamic Interactions
Dynamic Interactions of c-Myc Inhibitors
Inhibition of Aggregation by Induced Compaction
Concluding Discussions
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