Towards the physical basis of how intrinsic disorder mediates protein function

Coarse-Grained Simulations of Phosphorylation Regulation of p53 Autoinhibition

Intrinsically disordered proteins (IDPs) are key components of cellular signaling and regulatory networks. They frequently remain dynamic even in complexes and thus rely on potentially subtle shifts in the disordered conformational ensemble for function. Understanding the molecular basis of these fascinating mechanisms of IDP function and regulation requires a detailed characterization of dynamic ensembles in various biologically relevant states. Here, we study the phosphorylation dependence of the dynamic interaction between the N-terminal transactivation domain (NTAD) and DNA-binding domain (DBD) of tumor suppressor p53, which plays a key role in the autoinhibition and regulation of p53 activation or termination during various stages of stress response. By extending the hybrid-resolution (HyRes) coarse-grained (CG) protein force field to model phosphorylated side chains, we show that HyRes simulations accurately recapitulate the effects of phosphorylation on the p53 NTAD/DBD interactions. The simulated ensembles show that phosphorylation of Thr55 as well as Ser46 enhances dynamic NTAD/DBD interactions and further induces conformational shifts that promote trans interactions between two p53 dimers to drive dissociation from DNA. These CG simulations thus provide a strong molecular basis in support of previous experimental studies suggesting the central role of dynamic interactions of disordered domains and phosphorylation in the function of p53. The success of this study also suggests that HyRes provides an efficient and viable tool for studying dynamic interactions and post-translational modifications in IDP function and regulation.

Structural elucidation of four fungal hydrophobins belonging to classes I and II: Results from Alphafold and accelerated molecular dynamics simulations

Hydrophobins (HFB) find application in various industries including biotechnology and medical devices; therefore, it is imperative to elucidate and learn more about their folded structures. Few fungal HFB protein structures are available in the Protein Data Bank (PDB), and fewer have been elucidated using homology modeling or short molecular dynamics (MD) simulations in the literature. Moreover, many homology modeling algorithms will only model the region with sequence identity. Therefore, we turned towards the state-of-the-art, artificial intelligence powered AlphaFold. It performed well in predicting the core β-barrel, a characteristic of HFBs, except for HFB9A which was unfolded with low confidence scores. These initial structures were then prepared for accelerated MD simulation in the hope of observing higher protein folding. With 500 ns long aMD simulations, we were able to obtain folded and energetically stable conformations for all the proteins except HFB9A, which exhibited much higher disorder, connected with higher atomic fluctuation, lowest hydrophobicity and overall compactness, and lesser secondary structure formation as visualized during the aMD simulation. The underlying intrinsic disorder in the HFBs was found to be the basis of harder-to-reach folding by AF2 which can be compensated by enhanced sampling MD simulations like the aMD technique. The characteristic two disordered loops of class I HFBs were obtained for SC3, while HFB9A showed that not all class I HFBs contain them. Class II HFBs were more stable, folded and compact with secondary structure motifs conserved throughout the trajectory which can be correlated with their comparatively much lower intrinsic disorder.

Likely Overstabilization of Charge-Charge Interactions in CHARMM36m(w): A Case for a99SB-disp Water

Recent years have witnessed drastic improvements in general-purpose explicit solvent protein force fields, partially driven by the need to study intrinsically disordered proteins (IDPs), and yet the state-of-the-art force fields such as CHARMM36m (c36m) and a99SB-disp still provide different performances in simulating disordered protein states, where c36m has a bias toward overcompaction for large IDPs. Here, we examine the performance of c36m and a99SB-disp in describing the stabilities of a set of 46 amino acid backbone and side chain pairs in various configurations. The free energy results show that c36m systematically predicts stronger interactions compared to a99SB-disp by an average of 0.2 kcal/mol for nonpolar pairs, 0.6 kcal/mol for polar pairs, and 0.8 kcal/mol for salt bridges. The most severe overstabilization in c36m is observed for charged pairs involving the Arg and Glu side chains by up to 2.9 kcal/mol. Importantly, the systematic overstabilization of c36m is only marginally alleviated by c36mw, an ad hoc patch to c36m that increases the dispersion interactions between TIP3P hydrogens and protein atoms. Guided by free energy decomposition, we evaluated if revising the charges alone could alleviate the severe overstabilization of salt bridges of c36m(w) vs a99SB-disp. The results suggested that the direct modification of protein-water interactions is also necessary. Toward this end, we proposed a tentative modification to c36m, referred to as c36mrb-disp, which combines modified Arg side chain charges, retuned backbone hydrogen bonding strength, and the a99SB-disp water model. The modified force field successfully reproduces the secondary structures of several intrinsically disordered peptides and proteins including (AAQAA)3, GB1p, and p53 transactivation domain, while maintaining the stability of a set of folded proteins. This work provides a set of useful systems for benchmarking and optimizing protein force fields and highlights the importance of balancing protein-protein and protein-water electrostatic interactions for accurately describing both folded and disordered proteins.

Backbone interactions and secondary structures in phase separation of disordered proteins

Intrinsically disordered proteins (IDPs) are one of the major drivers behind the formation and characteristics of biomolecular condensates. Due to their inherent flexibility, the backbones of IDPs are significantly exposed, rendering them highly influential and susceptible to biomolecular phase separation. In densely packed condensates, exposed backbones have a heightened capacity to interact with neighboring protein chains, which might lead to strong coupling between the secondary structures and phase separation and further modulate the subsequent transitions of the condensates, such as aging and fibrillization. In this mini-review, we provide an overview of backbone-mediated interactions and secondary structures within biomolecular condensates to underscore the importance of protein backbones in phase separation. We further focus on recent advances in experimental techniques and molecular dynamics simulation methods for probing and exploring the roles of backbone interactions and secondary structures in biomolecular phase separation involving IDPs.

Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation

Intrinsically disordered proteins (IDPs) frequently mediate phase separation that underlies the formation of a biomolecular condensate. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding the sequence-specific phase separation of IDPs. However, the widely used Cα-only models are limited in capturing the peptide nature of IDPs, particularly backbone-mediated interactions and effects of secondary structures, in phase separation. Here, we describe a hybrid resolution (HyRes) protein model toward a more accurate description of the backbone and transient secondary structures in phase separation. With an atomistic backbone and coarse-grained side chains, HyRes can semiquantitatively capture the residue helical propensity and overall chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for the direct simulation of spontaneous phase separation and, at the same time, appears accurate enough to resolve the effects of single His to Lys mutations. HyRes simulations also successfully predict increased β-structure formation in the condensate, consistent with available experimental CD data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate the phase separation propensity as measured by the saturation concentration. The simulations successfully recapitulate the effect of these mutants on the helicity and phase separation propensity of TDP-43 CR. Analyses reveal that the balance between backbone and side chain-mediated interactions, but not helicity itself, actually determines phase separation propensity. These results support that HyRes represents an effective protein model for molecular simulation of IDP phase separation and will help to elucidate the coupling between transient secondary structures and phase separation.

Accurate Simulation of Coupling between Protein Secondary Structure and Liquid-Liquid Phase Separation

Intrinsically disordered proteins (IDPs) frequently mediate liquid-liquid phase separation (LLPS) that underlies the formation of membraneless organelles. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding sequence-specific phase separation of IDPs. However, the widely-used Cα-only models are severely limited in capturing the peptide nature of IDPs, including backbone-mediated interactions and effects of secondary structures, in LLPS. Here, we describe a hybrid resolution (HyRes) protein model for accurate description of the backbone and transient secondary structures in LLPS. With an atomistic backbone and coarse-grained side chains, HyRes accurately predicts the residue helical propensity and chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for direct simulation of spontaneous phase separation, and at the same time accurate enough to resolve the effects of single mutations. HyRes simulations also successfully predict increased beta-sheet formation in the condensate, consistent with available experimental data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate LLPS propensity. The simulations successfully recapitulate the effect of these mutants on the helicity and LLPS propensity of TDP-43 CR. Analyses reveal that the balance between backbone and sidechain-mediated interactions, but not helicity itself, actually determines LLPS propensity. We believe that the HyRes model represents an important advance in the molecular simulation of LLPS and will help elucidate the coupling between IDP transient secondary structures and phase separation.

Re-Balancing Replica Exchange with Solute Tempering for Sampling Dynamic Protein Conformations

Replica exchange with solute tempering (REST) is a highly effective variant of replica exchange for enhanced sampling in explicit solvent simulations of biomolecules. By scaling the Hamiltonian for a selected "solute" region of the system, REST effectively applies tempering only to the degrees of freedom of interest but not the rest of the system ("solvent"), allowing fewer replicas for covering the same temperature range. A key consideration of REST is how the solute-solvent interactions are scaled together with the solute-solute interactions. Here, we critically evaluate the performance of the latest REST2 protocol for sampling large-scale conformation fluctuations of intrinsically disordered proteins (IDPs). The results show that REST2 promotes artificial protein conformational collapse at high effective temperatures, which seems to be a designed feature originally to promote the sampling of reversible folding of small proteins. The collapse is particularly severe with larger IDPs, leading to replica segregation in the effective temperature space and hindering effective sampling of large-scale conformational changes. We propose that the scaling of the solute-solvent interactions can be treated as free parameters in REST, which can be tuned to control the solute conformational properties (e.g., chain expansion) at different effective temperatures and achieve more effective sampling. To this end, we derive a new REST3 protocol, where the strengths of the solute-solvent van der Waals interactions are recalibrated to reproduce the levels of protein chain expansion at high effective temperatures. The efficiency of REST3 is examined using two IDPs with nontrivial local and long-range structural features, including the p53 N-terminal domain and the kinase inducible transactivation domain of transcription factor CREB. The results suggest that REST3 leads to a much more efficient temperature random walk and improved sampling efficiency, which also further reduces the number of replicas required. Nonetheless, our analysis also reveals significant challenges of relying on tempering alone for sampling large-scale conformational fluctuations of disordered proteins. It is likely that more efficient sampling protocols will require incorporating more sophisticated Hamiltonian replica exchange schemes in addition to tempering.

Coupled binding and folding of disordered SPIN N-terminal region in myeloperoxidase inhibition

Gram-positive pathogenic bacteria Staphylococcus express and secret staphylococcal peroxidase inhibitor (SPIN) proteins to help evade neutrophil-mediated immunity by inhibiting the activity of the main oxidative-defense player myeloperoxidase (MPO) enzyme. SPIN contains a structured 3-helix bundle C-terminal domain, which can specifically bind to MPO with high affinity, and an intrinsically disordered N-terminal domain (NTD), which folds into a structured β-hairpin and inserts itself into the active site of MPO for inhibition. Mechanistic insights of the coupled folding and binding process are needed in order to better understand how residual structures and/or conformational flexibility of NTD contribute to the different strengths of inhibition of SPIN homologs. In this work, we applied atomistic molecular dynamics simulations on two SPIN homologs, from S. aureus and S. delphini, respectively, which share high sequence identity and similarity, to explore the possible mechanistic basis for their different inhibition efficacies on human MPO. Direct simulations of the unfolding and unbinding processes at 450 K reveal that these two SPIN/MPO complexes systems follow surprisingly different mechanisms of coupled binding and folding. While coupled binding and folding of SPIN-aureus NTD is highly cooperative, SPIN-delphini NTD appears to mainly utilize a conformational selection-like mechanism. These observations are in contrast to an overwhelming prevalence of induced folding-like mechanisms for intrinsically disordered proteins that fold into helical structures upon binding. Further simulations of unbound SPIN NTDs at room temperature reveal that SPIN-delphini NTD has a much stronger propensity of forming β-hairpin like structures, consistent with its preference to fold and then bind. These may help explain why the inhibition strength is not well correlated with binding affinity for different SPIN homologs. Altogether, our work establishes the relationship between the residual conformational stability of SPIN-NTD and their inhibitory function, which can help us develop new strategies towards treating Staphylococcal infections.

Toward Accurate Coarse-Grained Simulations of Disordered Proteins and Their Dynamic Interactions

Intrinsically disordered proteins (IDPs) play crucial roles in cellular regulatory networks and are now recognized to often remain highly dynamic even in specific interactions and assemblies. Accurate description of these dynamic interactions is extremely challenging using atomistic simulations because of the prohibitive computational cost. Efficient coarse-grained approaches could offer an effective solution to overcome this bottleneck if they could provide an accurate description of key local and global properties of IDPs in both unbound and bound states. The recently developed hybrid-resolution (HyRes) protein model has been shown to be capable of providing a semiquantitative description of the secondary structure propensities of IDPs. Here, we show that greatly improved description of global structures and transient interactions can be achieved by introducing a solvent-accessible surface area-based implicit solvent term followed by reoptimization of effective interaction strengths. The new model, termed HyRes II, can semiquantitatively reproduce a wide range of local and global structural properties of a set of IDPs of various lengths and complexities. It can also distinguish the level of compaction between folded proteins and IDPs. In particular, applied to the disordered N-terminal transactivation domain (TAD) of tumor suppressor p53, HyRes II is able to recapitulate various nontrivial structural properties compared to experimental results, some of them to a level of accuracy that is almost comparable to results from atomistic explicit solvent simulations. Furthermore, we demonstrate that HyRes II can be used to simulate the dynamic interactions of TAD with the DNA-binding domain of p53, generating structural ensembles that are highly consistent with existing NMR data. We anticipate that HyRes II will provide an efficient and relatively reliable tool toward accurate coarse-grained simulations of dynamic protein interactions.

Identification of Intrinsically Disordered Proteins and Regions in a Non-Model Insect Species Ostrinia nubilalis (Hbn.)

Research in previous decades has shown that intrinsically disordered proteins (IDPs) and regions in proteins (IDRs) are as ubiquitous as highly ordered proteins. Despite this, research on IDPs and IDRs still has many gaps left to fill. Here, we present an approach that combines wet lab methods with bioinformatics tools to identify and analyze intrinsically disordered proteins in a non-model insect species that is cold-hardy. Due to their known resilience to the effects of extreme temperatures, these proteins likely play important roles in this insect's adaptive mechanisms to sub-zero temperatures. The approach involves IDP enrichment by sample heating and double-digestion of proteins, followed by peptide and protein identification. Next, proteins are bioinformatically analyzed for disorder content, presence of long disordered regions, amino acid composition, and processes they are involved in. Finally, IDP detection is validated with an in-house 2D PAGE. In total, 608 unique proteins were identified, with 39 being mostly disordered, 100 partially disordered, 95 nearly ordered, and 374 ordered. One-third contain at least one long disordered segment. Functional information was available for only 90 proteins with intrinsic disorders out of 312 characterized proteins. Around half of the 90 proteins are cytoskeletal elements or involved in translational processes.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.

Cancer-Associated Mutations Perturb the Disordered Ensemble and Interactions of the Intrinsically Disordered p53 Transactivation Domain

Intrinsically disordered proteins (IDPs) are key components of regulatory networks that control crucial aspects of cell decision making. The intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 mediates its interactions with multiple regulatory pathways to control the p53 homeostasis during the cellular response to genotoxic stress. Many cancer-associated mutations have been discovered in p53-TAD, but their structural and functional consequences are poorly understood. Here, by combining atomistic simulations, NMR spectroscopy, and binding assays, we demonstrate that cancer-associated mutations can significantly perturb the balance of p53 interactions with key activation and degradation regulators. Importantly, the four mutations studied in this work do not all directly disrupt the known interaction interfaces. Instead, at least three of these mutations likely modulate the disordered state of p53-TAD to perturb its interactions with regulators. Specifically, NMR and simulation analysis together suggest that these mutations can modulate the level of conformational expansion as well as rigidity of the disordered state. Our work suggests that the disordered conformational ensemble of p53-TAD can serve as a central conduit in regulating the response to various cellular stimuli at the protein-protein interaction level. Understanding how the disordered state of IDPs may be modulated by regulatory signals and/or disease associated perturbations will be essential in the studies on the role of IDPs in biology and diseases.

Accelerating atomistic simulations of proteins using multiscale enhanced sampling with independent tempering

Efficient sampling of the conformational space is essential for quantitative simulations of proteins. The multiscale enhanced sampling (MSES) method accelerates atomistic sampling by coupling it to a coarse-grained (CG) simulation. Bias from coupling to the CG model is removed using Hamiltonian replica exchange, such that one could benefit simultaneously from the high accuracy of atomistic models and fast dynamics of CG ones. Here, we extend MSES to allow independent control of the effective temperatures of atomistic and CG simulations, by directly scaling the atomistic and CG Hamiltonians. The new algorithm, named MSES with independent tempering (MSES-IT), supports more sophisticated Hamiltonian and temperature replica exchange protocols to further improve the sampling efficiency. Using a small but nontrivial β-hairpin, we show that setting the effective temperature of CG model in all conditions to its melting temperature maximizes structural transition rates at the CG level and promotes more efficient replica exchange and diffusion in the condition space. As the result, MSES-IT drive faster reversible transitions at the atomic level and leads to significant improvement in generating converged conformational ensembles compared to the original MSES scheme.

Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.

Phosphorylation regulates the binding of intrinsically disordered proteins via a flexible conformation selection mechanism

Phosphorylation is one of the most common post-translational modifications. The phosphorylation of the kinase-inducible domain (KID), which is an intrinsically disordered protein (IDP), promotes the folding of KID and binding with the KID-interacting domain (KIX). However, the regulation mechanism of the phosphorylation on KID is still elusive. In this study, the structural ensembles and binding process of pKID and KIX are studied by all-atom enhanced sampling technologies. The results show that more hydrophobic interactions are formed in pKID, which promote the formation of the special hydrophobic residue cluster (HRC). The pre-formed HRC promotes binding to the correct sites of KIX and further lead the folding of pKID. Consequently, a flexible conformational selection model is proposed to describe the binding and folding process of intrinsically disordered proteins. The binding mechanism revealed in this work provides new insights into the dynamic interactions and phosphorylation regulation of proteins.

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

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.

Correlation between Labeling Yield and Surface Accessibility in Covalent Labeling Mass Spectrometry

The functional properties of a protein are strongly influenced by its topography, or the solvent-facing contour map of its surface. Together with crosslinking, covalent labeling mass spectrometry (CL-MS) has the potential to contribute topographical data through the measurement of surface accessibility. However, recent efforts to correlate measures of surface accessibility with labeling yield have been met with mixed success. Most applications of CL-MS involve differential analysis of protein interactions (i.e., footprinting experiments) where such inconsistencies have limited effect. Extending CL-MS into structural analysis requires an improved evaluation of the relationship between labeling and surface exposure. In this study, we applied recently developed diazirine reagents to obtain deep coverage of the large motor domain of Eg5 (a mitotic kinesin), and together with computational methods we correlated labeling yields with accessibility data in a number of ways. We observe that correlations can indeed be seen at a local structural level, but these correlations do not extend across the structure. The lack of correlation arises from the influence of protein dynamics and chemical composition on reagent partitioning and, thus, also on labeling yield. We conclude that our use of CL-MS data should be considered in light of "chemical accessibility" rather than "solvent accessibility" and suggest that CL-MS data would be a useful tool in the fundamental study of protein-solute interactions.

Residual Structures and Transient Long-Range Interactions of p53 Transactivation Domain: Assessment of Explicit Solvent Protein Force Fields

Molecular dynamics simulations using physics-based atomistic force fields have been increasingly used to characterize the heterogeneous structural ensembles of intrinsically disordered proteins (IDPs). To evaluate the accuracy of the latest atomistic explicit-solvent force fields in modeling larger IDPs with nontrivial structural features, we focus on the 61-residue N-terminal transactivation domain (TAD) of tumor suppressor p53, an important protein in cancer biology that has been extensively studied, and abundant experimental data is available for evaluation of simulated ensembles. We performed extensive replica exchange with solute tempering simulations, in excess of 1.0 μs/replica, to generate disordered structural ensembles of p53-TAD using six latest explicit solvent protein force fields. Multiple local and long-range structural properties, including chain dimension, residual secondary structures, and transient long-range contacts, were analyzed and compared against available experimental data. The results show that IDPs such as p53-TAD remain highly challenging for atomistic simulations due to conformational complexity and difficulty in achieving adequate convergence. Structural ensembles of p53-TAD generated using various force fields differ significantly from each other. The a99SB-disp force field demonstrates the best agreement with experimental data at all levels and proves to be suitable for simulating unbound p53-TAD and how its conformational properties may be modulated by phosphorylation and other cellular signals or cancer-associated mutations. Feasibility of such detailed structural characterization is a key step toward establishing the sequence-disordered ensemble-function-disease relationship of p53 and other biologically important IDPs.

Ion Mobility Mass Spectrometry Uncovers the Impact of the Patterning of Oppositely Charged Residues on the Conformational Distributions of Intrinsically Disordered Proteins

The global dimensions and amplitudes of conformational fluctuations of intrinsically disordered proteins are governed, in part, by the linear segregation versus clustering of oppositely charged residues within the primary sequence. Ion mobility-mass spectrometry (IM-MS) affords unique advantages for probing the conformational consequences of the linear patterning of oppositely charged residues because it measures and separates proteins electrosprayed from solution on the basis of charge and shape. Here, we use IM-MS to measure the conformational consequences of charge patterning on the C-terminal intrinsically disordered region (p27 IDR) of the cell cycle inhibitory protein p27^Kip1. We report the range of charge states and accompanying collisional cross section distributions for wild-type p27 IDR and two variants with identical amino acid compositions, κ14 and κ56, distinguished by the extent of linear mixing versus segregation of oppositely charged residues. Wild-type p27 IDR (κ31) and κ14, where the oppositely charged residues are more evenly distributed, exhibit a broad distribution of charge states. This is concordant with high degrees of conformational heterogeneity in solution. By contrast, κ56 with linear segregation of oppositely charged residues leads to limited conformational heterogeneity and a narrow distribution of charged states. Gas-phase molecular dynamics simulations demonstrate that the interplay between chain solvation and intrachain interactions (self-solvation) leads to conformational distributions that are modulated by salt concentration, with the wild-type sequence showing the most sensitivity to changes in salt concentration. These results suggest that the charge patterning within the wild-type p27 IDR may be optimized to sample both highly solvated and self-solvated conformational states.

Extreme Fuzziness: Direct Interactions between Two IDPs

Protein interactions involving intrinsically disordered proteins (IDPs) greatly extend the range of binding mechanisms available to proteins. In interactions employing coupled folding and binding, IDPs undergo disorder-to-order transitions to form a complex with a well-defined structure. In many other cases, IDPs retain structural plasticity in the final complexes, which have been defined as the fuzzy complexes. While a large number of fuzzy complexes have been characterized with variety of fuzzy patterns, many of the interactions are between an IDP and a structured protein. Thus, whether two IDPs can interact directly to form a fuzzy complex without disorder-to-order transition remains an open question. Recently, two studies of interactions between IDPs (4.1G-CTD/NuMA and H1/ProTα) have found a definite answer to this question. Detailed characterizations combined with nuclear magnetic resonance (NMR), single-molecule Förster resonance energy transfer (smFRET) and molecular dynamics (MD) simulation demonstrate that direct interactions between these two pairs of IDPs do form fuzzy complexes while retaining the conformational dynamics of the isolated proteins, which we name as the extremely fuzzy complexes. Extreme fuzziness completes the full spectrum of protein-protein interaction modes, suggesting that a more generalized model beyond existing binding mechanisms is required. Previous models of protein interaction could be applicable to some aspects of the extremely fuzzy interactions, but in more general sense, the distinction between native and nonnative contacts, which was used to understand protein folding and binding, becomes obscure. Exploring the phenomenon of extreme fuzziness may shed new light on molecular recognition and drug design.

Coarse-grained residue-based models of disordered protein condensates: utility and limitations of simple charge pattern parameters

Biomolecular condensates undergirded by phase separations of proteins and nucleic acids serve crucial biological functions. To gain physical insights into their genetic basis, we study how liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) depends on their sequence charge patterns using a continuum Langevin chain model wherein each amino acid residue is represented by a single bead. Charge patterns are characterized by the "blockiness" measure κ and the "sequence charge decoration" (SCD) parameter. Consistent with random phase approximation (RPA) theory and lattice simulations, LLPS propensity as characterized by critical temperature Tcr* increases with increasingly negative SCD for a set of sequences showing a positive correlation between κ and -SCD. Relative to RPA, the simulated sequence-dependent variation in Tcr* is often-though not always-smaller, whereas the simulated critical volume fractions are higher. However, for a set of sequences exhibiting an anti-correlation between κ and -SCD, the simulated Tcr*'s are quite insensitive to either parameter. Additionally, we find that blocky sequences that allow for strong electrostatic repulsion can lead to coexistence curves with upward concavity as stipulated by RPA, but the LLPS propensity of a strictly alternating charge sequence was likely overestimated by RPA and lattice models because interchain stabilization of this sequence requires spatial alignments that are difficult to achieve in real space. These results help delineate the utility and limitations of the charge pattern parameters and of RPA, pointing to further efforts necessary for rationalizing the newly observed subtleties.

Residual Structure Accelerates Binding of Intrinsically Disordered ACTR by Promoting Efficient Folding upon Encounter

Intrinsically disordered proteins (IDPs) often fold into stable structures upon specific binding. The roles of residual structure of unbound IDPs in coupling binding and folding have been under much debate. While many studies emphasize the importance of conformational flexibility for IDP recognition, it was recently demonstrated that stabilization the N-terminal helix of intrinsically disordered ACTR accelerated its binding to another IDP, NCBD of the CREB-binding protein. To understand how enhancing ACTR helicity accelerates binding, we derived a series of topology-based coarse-grained models that mimicked various ACTR mutants with increasing helical contents and reproduced their NCBD binding affinities. Molecular dynamics simulations were then performed to sample hundreds of reversible coupled binding and folding transitions. The results show that increasing ACTR helicity does not alter the baseline mechanism of synergistic folding, which continues to follow "extended conformational selection" with multiple stages of selection and induced folding. Importantly, these coarse-grained models, while only calibrated based on binding thermodynamics, recapitulate the observed kinetic acceleration with increasing ACTR helicity. However, the residual helices do not enhance the association kinetics via more efficient seeding of productive collisions. Instead, they allow the nonspecific collision complexes to evolve more efficiently into the final bound and folded state, which is the primary source of accelerated association kinetics. Meanwhile, reduced dissociation kinetics with increasing ACTR helicity can be directly attributed to smaller entropic cost of forming the bound state. Altogether, this study provides important mechanistic insights into how residual structure may modulate thermodynamics and kinetics of IDP interactions.

Conformational Ensembles Exhibit Extensive Molecular Recognition Features

Intrinsically disordered proteins (IDPs) are important for signaling and regulatory pathways. In contrast to folded proteins, they sample a diverse conformational space. IDPs have residue ranges within a sequence that have been referred to as molecular recognition features (MoRFs). A MoRF can be viewed as contiguous residues exhibiting a conformational disorder that become ordered upon binding to another protein or ligand. In this work, we introduce a structural characterization of MoRFs based on entropy and mutual information (MI). In this view, a MoRF is a set of contiguous residues that exhibit a large entropy (from rotameric residue sampling) and large MI, the latter indicating a dependence among the residues' rotameric sampling comprising the MoRF. The methodology is first applied to a number of ubiquitin ensembles that were obtained based on nuclear magnetic resonance experiments. One is a denatured Ub ensemble that has a large entropy for various unitSizes (number of contiguous residues) but essentially zero MI, indicting no dependence among the residue rotamer sampling. Another ensemble does exhibit extensive regions along the sequence where there are MoRFs centered on nonsecondary structure regions. The MoRFs are present for unitSizes 2-10. That a substantial number of MoRFs are present in Ub strongly suggests a conformational selection mechanism for this protein. Two additional ensembles for the cyclin-dependent kinase inhibitor Sic1 and for the amyloid protein α-synuclein, which have been shown to be IDPs, are also analyzed. Both exhibit MoRF-like character.

pKID Binds to KIX via an Unstructured Transition State with Nonnative Interactions

Understanding the detailed mechanism of interaction of intrinsically disordered proteins with their partners is crucial to comprehend their functions in signaling and transcription. Through its interaction with KIX, the disordered pKID region of CREB protein is central in the transcription of cAMP responsive genes, including those involved in long-term memory. Numerous simulation studies have investigated these interactions. Combined with experimental results, these can provide valuable and comprehensive understanding of the mechanisms involved. Here, we probe the transition state of this interaction experimentally through analyzing the kinetic effect of mutating both interface and solvent exposed residues in pKID. We show that very few specific interactions between pKID and KIX are required in the initial binding process. Only a small number of weak interactions are formed at the transition state, including nonnative interactions, and most of the folding occurs after the initial binding event. These properties are consistent with computational results and also the majority of experimental studies of intrinsically disordered protein coupled folding and binding in other protein systems, suggesting that these may be common features.

HyRes: a coarse-grained model for multi-scale enhanced sampling of disordered protein conformations

Efficient coarse-grained (CG) models can be coupled with atomistic force fields to accelerate the sampling of atomistic energy landscapes in the multi-scale enhanced sampling (MSES) framework. This approach may be particularly suitable for generating atomistic conformational ensembles of intrinsically disordered proteins (IDPs). While MSES is relatively robust to inherent CG artifacts, achieving optimal sampling efficiency requires CG modeling to generate the local and long-range fluctuations that are largely consistent with those at the atomistic level. Here, we describe a new hybrid resolution CG model (HyRes) for MSES simulations of disordered protein states, which is specifically designed to provide semi-quantitative secondary structure propensities together with a qualitative description of long-range nonspecific interactions. The HyRes model contains an atomistic description of the backbone with intermediate resolution side chains. The secondary structure propensities are tuned by adjusting the backbone hydrogen-bonding strength and the ϕ/ψ torsion profile. The sizes and covalent geometries of the side chains are parameterized to reproduce distributions derived from atomistic simulations. Lennard-Jones parameters for sidechain beads are assigned to reproduce statistical potentials derived from the protein structural database, and then globally parameterized with nonspecific electrostatic interactions to reproduce the free energy profiles of pair wise interactions and the key conformational properties of model peptides. Application of HyRes to MSES simulations of small IDPs suggests that it is capable of driving faster structural transitions at the atomistic level and increasing the convergence rate compared to the Cα-only Gō-like models previously utilized. With further optimization, we believe that the new CG model could greatly improve the efficiency of MSES simulations of the larger and more complex IDPs frequently involved in cellular signalling and regulation.

Enhanced Sampling of Intrinsic Structural Heterogeneity of the BH3-Only Protein Binding Interface of Bcl-xL

Antiapoptotic Bcl-xL plays central roles in regulating programed cell death. Partial unfolding of Bcl-xL has been observed at the interface upon specific binding to the pro-apoptotic BH3-only protein PUMA, which in turn disrupts the interaction of Bcl-xL with tumor suppressor p53 and promotes apoptosis. Previous analysis of existing Bcl-xL structures and atomistic molecular dynamics (MD) simulations have suggested that substantial intrinsic structure heterogeneity exists at the BH3-only protein binding interface of Bcl-xL to facilitate its conformational transitions upon binding. In this study, enhanced sampling is applied to further characterize the interfacial conformations of unbound Bcl-xL in explicit solvent. Extensive replica exchange with solute tempering (REST) simulations, with a total accumulated time of 16 μs, were able to cover much wider conformational spaces for the interfacial region of Bcl-xL. The resulting structural ensembles are much better converged, with local and long-range structural features that are highly consistent with existing NMR data. These simulations further demonstrate that the BH3-only protein binding interface of Bcl-xL is intrinsically disordered and samples many rapidly interconverting conformations. Intriguingly, all previously observed conformers are well represented in the unbound structure ensemble. Such intrinsic structural heterogeneity and flexibility may be critical for Bcl-xL to undergo partial unfolding induced by PUMA binding, and likely provide a robust basis that allows Bcl-xL to respond sensitively to binding of various ligands in cellular signaling and regulation.

Dynamics of the BH3-Only Protein Binding Interface of Bcl-xL

The balance and interplay between pro-death and pro-survival members of the B-cell lymphoma-2 (Bcl-2) family proteins play key roles in regulation of the mitochondrial pathway of programmed cell death. Recent NMR and biochemical studies have revealed that binding of the proapoptotic BH3-only protein PUMA induces significant unfolding of antiapoptotic Bcl-xL at the interface, which in turn disrupts the Bcl-xL/p53 interaction to activate apoptosis. However, the molecular mechanism of such regulated unfolding of Bcl-xL is not fully understood. Analysis of the existing Protein Data Bank structures of Bcl-xL in both bound and unbound states reveal substantial intrinsic heterogeneity at its BH3-only protein binding interface. Large-scale atomistic simulations were performed in explicit solvent for six representative structures to further investigate the intrinsic conformational dynamics of Bcl-xL. The results support that the BH3-only protein binding interface of Bcl-xL is much more dynamic compared to the rest of the protein, both unbound and when bound to various BH3-only proteins. Such intrinsic interfacial conformational dynamics likely provides a physical basis that allows Bcl-xL to respond sensitively to detailed biophysical properties of the ligand. The ability of Bcl-xL to retain or even enhance dynamics at the interface in bound states could further facilitate the regulation of its interactions with various BH3-only proteins such as through posttranslational modifications.

Binding of Disordered Peptides to Kelch: Insights from Enhanced Sampling Simulations

Keap1 protein plays an essential role in regulating cellular oxidative stress response and is a crucial binding hub for multiple proteins, several of which are intrinsically disordered proteins (IDP). Among Kelch's IDP binding partners, NRF2 and PTMA are the two most interesting cases. They share a highly similar binding motif; however, NRF2 binds to Kelch with a binding affinity of approximately 100-fold higher than that of PTMA. In this study, we perform an exhaustive sampling composed of 6 μs well-tempered metadynamics and 2 μs unbiased molecular dynamics (MD) simulations aiming at characterizing the binding mechanisms and structural properties of these two peptides. Our results agree with previous experimental observations that PTMA is remarkably more disordered than NRF2 in both the free and bound states. This explains PTMA's lower binding affinity. Our extensive sampling also provides valuable insights into the vast conformational ensembles of both NRF2 and PTMA, supports the hypothesis of coupled folding-binding, and confirms the essential role of linear motifs in IDP binding.

Accelerating the Conformational Sampling of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are a class of proteins lacking a well-defined secondary structure. Instead, they are able to attain multiple conformations, bind to multiple targets, and respond to changes in their surroundings. Functionally, IDPs have been associated with molecular recognition, cell regulation, and signal transduction. The dynamic conformational ensemble of IDPs is highly environmental and binding partner dependent, rendering the characterization of IDPs extremely challenging. Here, we compare the sampling efficiencies of conventional molecular dynamics (MD), well-tempered metadynamics (WT-META), and bias-exchange metadynamics (BE-META). The total simulation time was over 10 μs, and a 20-mer peptide derived from the Neh2 domain of the Nuclear factor erythroid 2-related factor 2 (Nrf2) protein was simulated. BE-META, with a neutral replica and seven biased replicas employing a set of seven relevant collective variables (CVs), provided the most reliable and efficient sampling. Finally, we propose a free-energy reconstruction method based on the probability distribution of the secondary structure contents. This postprocessing analysis confirms the presence of not only the β-hairpin conformation of the free Neh2 peptide but also its rare bound-state-like conformation, both of that have been experimentally observed. In addition, our simulations also predict other possible conformations to be verified with future experiments.

Computational Design of Biomimetic Phosphate Scavengers

Phosphorus has long been the target of much research, but in recent years the focus has shifted from being limited only to reducing its detrimental environmental impact, to also looking at how it is linked to the global food security. Therefore, the interest in finding novel techniques for phosphorus recovery, as well as improving existing techniques, has increased. In this study we apply a hybrid simulation approach of molecular dynamics and quantum mechanics to investigate the binding modes of phosphate anions by a small intrinsically disordered peptide. Our results confirm that the conformational ensemble of the peptide is significantly changed, or stabilized, by the binding of phosphate anions and that binding does not take place purely as a result of a stable P-loop binding nest, but rather that multiple binding modes may be involved. Such small synthetic peptides capable of binding phosphate could be the starting point of new novel technological approaches toward phosphorus recovery, and they represent an excellent model system for investigating the nature and dynamics of functional de novo designed intrinsically disordered proteins.

Revealing the Mechanisms of Protein Disorder and N-Glycosylation in CD44-Hyaluronan Binding Using Molecular Simulation

The extracellular N-terminal hyaluronan binding domain (HABD) of CD44 is a small globular domain that confers hyaluronan (HA) binding functionality to this large transmembrane glycoprotein. When recombinantly expressed by itself, HABD exists as a globular water-soluble protein that retains the capacity to bind HA. This has enabled atomic-resolution structural biology experiments that have revealed the structure of HABD and its binding mode with oligomeric HA. Such experiments have also pointed to an order-to-disorder transition in HABD that is associated with HA binding. However, it had remained unclear how this structural transition was involved in binding since it occurs in a region of HABD distant from the HA-binding site. Furthermore, HABD is known to be N-glycosylated, and such glycosylation can diminish HA binding when the associated N-glycans are capped with sialic acid residues. The intrinsic flexibility of disordered proteins and of N-glycans makes it difficult to apply experimental structural biology approaches to probe the molecular mechanisms of how the order-to-disorder transition and N-glycosylation can modulate HA binding by HABD. We review recent results from molecular dynamics simulations that provide atomic-resolution mechanistic understanding of such modulation to help bridge gaps between existing experimental binding and structural biology data. Findings from these simulations include: Tyr42 may function as a molecular switch that converts the HA-binding site from a low affinity to a high affinity state; in the partially disordered form of HABD, basic amino acids in the C-terminal region can gain sufficient mobility to form direct contacts with bound HA to further stabilize binding; and terminal sialic acids on covalently attached N-glycans can form charge-paired hydrogen bonding interactions with basic amino acids that could otherwise bind to HA, thereby blocking HA binding to glycosylated CD44 HABD.

Multiscale enhanced sampling of intrinsically disordered protein conformations

In a recently developed multiscale enhanced sampling (MSES) technique, topology-based coarse-grained (CG) models are coupled to atomistic force fields to enhance the sampling of atomistic protein conformations. Here, the MSES protocol is refined by designing more sophisticated Hamiltonian/temperature replica exchange schemes that involve additional parameters in the MSES coupling restraint potential, to more carefully control how conformations are coupled between the atomistic and CG models. A specific focus is to derive an optimal MSES protocol for simulating conformational ensembles of intrinsically disordered proteins (IDPs). The efficacy of the refined protocols, referred to as MSES-soft asymptote (SA), was evaluated using two model peptides with various levels of residual helicities. The results show that MSES-SA generates more reversible helix-coil transitions and leads to improved convergence on various ensemble conformational properties. This study further suggests that more detailed CG models are likely necessary for more effective sampling of local conformational transition of IDPs. © 2015 Wiley Periodicals, Inc.

Intrinsically disordered energy landscapes

Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such 'intrinsically disordered' landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an α-helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium.

Modulation of the disordered conformational ensembles of the p53 transactivation domain by cancer-associated mutations

Intrinsically disordered proteins (IDPs) are frequently associated with human diseases such as cancers, and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions. Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles. Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in implicit solvent. The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels. The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively. On the tertiary level, the simulated ensembles are overly compact, even though they appear to recapitulate the overall features of transient long-range contacts qualitatively. A key finding is that, while p53-TAD and its cancer mutants sample a similar set of conformational states, cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP conformations, and at the same time reveals important limitations in the current implicit solvent protein force field that must be sufficiently addressed for reliable description of long-range structural features of the disordered ensembles.

Tumor suppressor p53 is the most frequently mutated protein in human cancers. Clinical studies have suggested that the type of p53 mutation can be linked to cancer prognosis, response to drug treatment, and patient survival. It is thus crucial to understand the molecular basis of p53 inactivation by various types of mutations, so as to understand the biological outcomes and assess potential cancer intervention strategies. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in an implicit solvent protein force field. The calculated ensembles are in quantitative agreement with several types of existing NMR data on the wild-type protein and the K24N mutant. The results suggest that, while all sequences sample a similar set of conformational substates, cancer mutants could introduce both local and long-range structural modulations and in turn perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The study also reveals important limitations in implicit solvent for simulations of disordered proteins like p53-TAD.

Brain expressed and X-linked (Bex) proteins are intrinsically disordered proteins (IDPs) and form new signaling hubs

Intrinsically disordered proteins (IDPs) are abundant in complex organisms. Due to their promiscuous nature and their ability to adopt several conformations IDPs constitute important points of network regulation. The family of Brain Expressed and X-linked (Bex) proteins consists of 5 members in humans (Bex1-5). Recent reports have implicated Bex proteins in transcriptional regulation and signaling pathways involved in neurodegeneration, cancer, cell cycle and tumor growth. However, structural and biophysical data for this protein family is almost non-existent. We used bioinformatics analyses to show that Bex proteins contain long regions of intrinsic disorder which are conserved across all members. Moreover, we confirmed the intrinsic disorder by circular dichroism spectroscopy of Bex1 after expression and purification in E. coli. These observations strongly suggest that Bex proteins constitute a new group of IDPs. Based on these findings, together with the demonstrated promiscuity of Bex proteins and their involvement in different signaling pathways, we propose that Bex family members play important roles in the formation of protein network hubs.

Theoretical perspectives on nonnative interactions and intrinsic disorder in protein folding and binding

The diverse biological functions of intrinsically disordered proteins (IDPs) have markedly raised our appreciation of protein conformational versatility, whereas the existence of energetically favorable yet functional detrimental nonnative interactions underscores the physical limitations of evolutionary optimization. Here we survey recent advances in using biophysical modeling to gain insight into experimentally observed nonnative behaviors and IDP properties. Simulations of IDP interactions to date focus mostly on coupled folding-binding, which follows essentially the same organizing principle as the local-nonlocal coupling mechanism in cooperative folding of monomeric globular proteins. By contrast, more innovative theories of electrostatic and aromatic interactions are needed for the conceptually novel but less-explored 'fuzzy' complexes in which the functionally bound IDPs remain largely disordered.

Molecular insights into dimerization inhibition of c-Maf transcription factor

The Maf protein family belongs to the activator protein 1 (AP-1) superfamily of transcription factors that bind specific DNA target sequences through a basic region and exploit a leucine zipper (LZ) motif for protein-protein interactions leading to homo- or hetero-dimerization. Mafs unique DNA-binding domain contains a highly conserved extended homology region (EHR) that allows to recognize longer DNA sequences than other basic leucine zipper (bZIP) transcription factors. Inspired by the fact that overexpression of Mafs is observed in about 50% of cases of multiple myeloma, a hematological malignant disorder, we undertook a peptide inhibitor approach. The LZ domain of c-Maf, one of large Mafs, was produced by solid phase peptide synthesis. We characterized its secondary structure and dimerization properties, and found that dimerization and folding events are strictly coupled. Moreover, potential peptidic c-Maf dimerization inhibitors were computationally designed and synthesized. These compounds were demonstrated by circular dichroism (CD) spectroscopy and MALDI-TOF mass spectrometry to bind to c-Maf LZ monomers, to drive folding of their partially disordered structure and to efficiently compete with dimerization, suggesting a way for interfering with the function of c-Maf and, more generally, of intrinsically disordered proteins, till now considered undruggable targets.

Specificity and affinity quantification of flexible recognition from underlying energy landscape topography

Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition.

Flexibility in biomolecular recognition is crucial for the function. Flexibility often leads to moderate binding affinity but high binding specificity, challenging the conventional wisdom that high specificity is guaranteed by high affinity. Currently, understanding of the relationship between affinity and specificity in flexible biomolecular recognition is still obscure, even in a qualitative way. By exploring the intrinsic biomolecular recognition energy landscapes, we provided a novel way to quantify the thermodynamic intrinsic specificity by energy landscape topography and kinetic specificity by association rate. We show quantitatively that flexibility decreases binding affinity while increases binding specificity, and the relative changes in affinity and specificity are strongly correlated with the degree of flexibility. Our results show that more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Importantly, we demonstrated that flexibility modulates affinity and specificity through the underlying energy landscape. Our study establishes the quantitative relationship among flexibility, affinity and specificity, bridging the gap between theory and experiments.

Replica exchange with guided annealing for accelerated sampling of disordered protein conformations

We critically examine a recently proposed convective replica exchange (cRE) method for enhanced sampling of protein conformation based on theoretical and numerical analysis. The results demonstrate that cRE and related replica exchange with guided annealing (RE-GA) schemes lead to unbalanced exchange attempt probabilities and break detailed balance whenever the system undergoes slow conformational transitions (relative to the temperature diffusion timescale). Nonetheless, numerical simulations suggest that approximate canonical ensembles can be generated for systems with small conformational transition barriers. This suggests that RE-GA maybe suitable for simulating intrinsically disordered proteins, an important class of newly recognized functional proteins. The efficacy of RE-GA is demonstrated by calculating the conformational ensembles of intrinsically disordered kinase inducible domain protein. The results show that RE-GA helps the protein to escape nonspecific compact states more efficiently and provides several fold speedups in generating converged and largely correct ensembles compared to the standard temperature RE.

CD44 receptor unfolding enhances binding by freeing basic amino acids to contact carbohydrate ligand

The extracellular carbohydrate-binding domain of the Type I transmembrane receptor CD44 is known to undergo affinity switching, where change in conformation leads to enhanced binding of its carbohydrate ligand hyaluronan. Separate x-ray crystallographic and NMR experiments have led to competing explanations, with the former supporting minor conformational changes at the binding site and the latter a major order-to-disorder unfolding transition distant from the binding site. Here, all-atom explicit-solvent molecular dynamics studies employing adaptive biasing force sampling revealed a substantial favorable free-energy change associated with contact formation between the Arg(41) side chain and hyaluronan at the binding site, independent of whether the distant site was ordered or disordered. Analogous computational experiments on Arg(41)Ala mutants showed loss of this favorable free-energy change, consistent with existing experimental data. More provocatively, the simulation data revealed the molecular mechanism by which the order-to-disorder transition enhances hyaluronan binding: in the disordered state, a number of basic residues gain sufficient conformational freedom-lacking in the ordered state-to spontaneously form side-chain contacts with hyaluronan. Mutation of these residues to Ala had been known to decrease binding affinity, but there had previously been no structural explanation, given their lack of proximity to the carbohydrate-binding site in existing structures of the complex.