Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins

Abstract

Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves the integration of biophysical experiments and computational models. In this review, we discuss current approaches to determine conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.

Generating Ensembles of Dynamic Misfolding Proteins

“…The conformational heterogeneity of IDPs and proteins that contain a significant portion of IDRs precludes the conventional investigation of these species using methods able to determine high resolution structures, such as cryo-EM and X-ray crystallography ( Thomasen and Lindorff-Larsen, 2022 ). For amyloid precursors that are initially folded, even though the native monomeric state may be populated to an extent that allows its characterization by structural approaches, these methods cannot capture the rarely populated, partially folded species that can be crucial for aggregation ( Radford et al, 1992 ; Dhulesia et al, 2010 ; Buell et al, 2011 ; Karamanos et al, 2016 ), or the loosely associated oligomeric species that form early during assembly ( Laganowsky et al, 2012 ; Karamanos et al, 2014 , 2019 ; Fusco et al, 2017 ).…”

“…Approach 2, on the other hand, assumes that all relevant protein states are already present in the initial ensemble and may not be appropriate in cases where conformational sampling is not efficient. A detailed description of these computational protocols is beyond the scope of this review and we refer the reader to some excellent recent reviews on the topic ( Hummer and Köfinger, 2015 ; Bonomi et al, 2017 ; Bottaro and Lindorff-Larsen, 2018 ; Pietrek et al, 2020 ; Thomasen and Lindorff-Larsen, 2022 ). We note that the computer-generated ensembles are only a true reflection of the experimental data that were used for their generation.…”

IDPConformerGenerator: A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States

“…Disordered states of proteins, including unfolded states, intrinsically disordered regions (IDRs) of otherwise folded domains, as well as full intrinsically disordered proteins (IDPs) are increasingly recognized for the roles they play in folding kinetics 1 aggregation propensity 2,3 , critical biological functions 4 , and pathological disease states 5 . Structural insights are needed to better understand these disordered protein states, and a variety of solution experiments have been developed to enable structural and dynamic descriptions of disordered proteins using nuclear magnetic resonance (NMR), small angle X-ray scattering (SAXS), single molecule fluorescence (SMF) and other data types [6][7][8][9][10][11] . However, solution experimental data for disordered states are averaged over a very large number of heterogeneous interconverting conformations, leading to greater challenges in structural interpretation than for folded proteins.…”

Ligand-induced shifts in conformational ensembles that predict transcriptional activation

“…Powerful advancements in computational approaches have increased their application for the study of protein conformational ensembles. Computational methods for conformational sampling are notoriously hampered by two major challenges: inherent limitations in forcefields, and the difficulty of achieving sufficient sampling of the free energy landscape [30,31]. Enhanced sampling methods applied to molecular dynamics (MD) simulations, including accelerated MD, metadynamics and replica exchange have been useful for overcoming limitations in studying conformational ensembles, providing physical descriptions that illuminate structural and functional protein mechanisms [32][33][34][35][36][37].…”