Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles

Perspectives on solution-based small angle X-ray scattering for protein and biological macromolecule structural biology

Small-angle X-ray scattering (SAXS) is used to extract structural information from a wide variety of non-crystalline samples in different fields (e.g., materials science, physics, chemistry, and biology). This review provides an overview of SAXS as applied to structural biology, specifically for proteins and other biomacromolecules in solution with an emphasis on extracting key structural parameters and the interpretation of SAXS data using a diverse array of techniques. These techniques cover aspects of building and assessing models to describe data measured from monodispersed and ideal dilute samples through to more complicated structurally polydisperse systems. Ab initio modelling, rigid body modelling as well as normal-mode analysis, molecular dynamics, mixed component and structural ensemble modelling are discussed. Dealing with polydispersity both physically in terms of component separation as well as approaching the analysis and modelling of data of mixtures and evolving systems are described, including methods for data decomposition such as single value decomposition/principle component analysis and evolving factor analysis. This review aims to highlight that solution SAXS, with the cohort of developments in data analysis and modelling, is well positioned to build upon the traditional 'single particle view' foundation of structural biology to take the field into new areas for interpreting the structures of proteins and biomacromolecules as population-states and dynamic structural systems.

Conformational dependence of chemical shifts in the proline rich region of TAU protein

Nuclear magnetic resonance (NMR) is an important method for structure elucidation of proteins, as it is an easily accessible and well understood method. To characterize intrinsically disordered proteins (IDPs) using computational models it is often necessary to analyze and integrate calculated observables with measurements derived from solution NMR experiments. In this case study, we investigate whether and which chemical shifts of the proline-rich region of Tau protein (residues 210-240) offer information about the conformational state to distinguish two different microscopic conformers. Using multiple computational methods, the chemical shifts of these two conformationally distinct structures are calculated. The different methods are compared regarding their ability to compute chemical shifts that are sensitive to conformational change. The analysis of the data shows significant differences between the available methods and gives suggestions for an improved pathway for ensemble reweighting. Nevertheless, the variation in the chemical shifts which are predicted for configurations that are commonly considered to belong to the same conformation is such that this obscures a comparison between distinct conformations. Conformational sensitivity is found for up to ∼26% of calculated chemical shifts. It is found to be unrelated to the atom element and has a minor relationship with the change in the corresponding ϕ dihedral angle.

IDRWalker: A Random Walk Based Tool for Generating Intrinsically Disordered Regions in Large Protein Complexes

Intrinsically disordered regions (IDRs), which may be functionally important, are common in proteins. However, the structures of IDRs are often missing due to their highly dynamic nature. In the study of IDRs, integrative modeling combining computational simulations and experimental data is a common approach, for which initial structures of the IDRs need to be built. However, applying this method to large protein complexes is challenging because existing structure generation tools are sometimes unsuitable for IDRs in large systems. To facilitate convenient and rapid structure generation of IDRs in large protein complexes, we developed a computational tool named IDRWalker based on self-avoiding random walks. Three protein complexes were used to illustrate the efficiency of the tool, and it was found that IDRs in more than 800 chains of the nuclear pore complex could be generated in minutes. These structures of large protein complexes with added IDRs can be further used to run computational simulations for integrative modeling.

Are Protein Conformational Ensembles in Agreement with Experimental Data? A Geometrical Interpretation of the Problem

The conformational variability of biological macromolecules can play an important role in their biological function. Therefore, understanding conformational variability is expected to be key for predicting the behavior of a particular molecule in the context of organism-wide studies. Several experimental methods have been developed and deployed for accessing this information, and computational methods are continuously updated for the profitable integration of different experimental sources. The outcome of this endeavor is conformational ensembles, which may vary significantly in properties and composition when different ensemble reconstruction methods are used, and this raises the issue of comparing the predicted ensembles against experimental data. In this article, we discuss a geometrical formulation to provide a framework for understanding the agreement of an ensemble prediction to the experimental observations.

Biomolecular dynamics in the 21st century

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.

SAXS/MC studies of the mixed-folded protein Cdt1 reveal monomeric, folded over conformations

Cdt1 is a protein critical for DNA replication licensing and is well-established to be a binding partner of the minichromosome maintenance (MCM) complex. Cdt1 has also been demonstrated to have an emerging, "moonlighting" role at the kinetochore via direct binding to microtubules and to the Ndc80 complex. However, it is not known how the structure and conformations of Cdt1 could allow for these multiple, completely unique sets of protein complexes. And while there exist multiple robust methods to study entirely folded or entirely unfolded proteins, structure-function studies of combined, mixed folded/disordered proteins remain challenging. It this work, we employ multiple orthogonal biophysical and computational techniques to provide a detailed structural characterization of human Cdt1 92-546. DSF and DSCD show both folded winged helix (WH) domains of Cdt1 are relatively unstable. CD and NMR show the N-terminal and the linker regions are intrinsically disordered. Using DLS and SEC-MALS, we show that Cdt1 is polydisperse, monomeric at high concentrations, and without any apparent inter-molecular self-association. SEC-SAXS of the monomer in solution enabled computational modeling of the protein in silico. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble. Using experimental SAXS data, we filtered for conformations which did and did not fit our data. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with our SAXS data. The best fit models have the N-terminal and linker regions extended into solution and the two folded domains close to each other in apparent "folded over" conformations. The best fit Cdt1 conformations are consistent with a function as a scaffold protein which may be sterically blocked without the presence of binding partners. Our studies also provide a template for combining experimental and computational biophysical techniques to study mixed-folded proteins.

Insight into structural biophysics from solution X-ray scattering

The current challenges of structural biophysics include determining the structure of large self-assembled complexes, resolving the structure of ensembles of complex structures and their mass fraction, and unraveling the dynamic pathways and mechanisms leading to the formation of complex structures from their subunits. Modern synchrotron solution X-ray scattering data enable simultaneous high-spatial and high-temporal structural data required to address the current challenges of structural biophysics. These data are complementary to crystallography, NMR, and cryo-TEM data. However, the analysis of solution scattering data is challenging; hence many different analysis tools, listed in the SAS Portal (http://smallangle.org/), were developed. In this review, we start by briefly summarizing classical X-ray scattering analyses providing insight into fundamental structural and interaction parameters. We then describe recent developments, integrating simulations, theory, and advanced X-ray scattering modeling, providing unique insights into the structure, energetics, and dynamics of self-assembled complexes. The structural information is essential for understanding the underlying physical chemistry principles leading to self-assembled supramolecular architectures and computational structural refinement.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Benchmarking of force fields to characterize the intrinsically disordered R2-FUS-LC region

Intrinsically Disordered Proteins (IDPs) play crucial roles in numerous diseases like Alzheimer's and ALS by forming irreversible amyloid fibrils. The effectiveness of force fields (FFs) developed for globular proteins and their modified versions for IDPs varies depending on the specific protein. This study assesses 13 FFs, including AMBER and CHARMM, by simulating the R2 region of the FUS-LC domain (R2-FUS-LC region), an IDP implicated in ALS. Due to the flexibility of the region, we show that utilizing multiple measures, which evaluate the local and global conformations, and combining them together into a final score are important for a comprehensive evaluation of force fields. The results suggest c36m2021s3p with mTIP3p water model is the most balanced FF, capable of generating various conformations compatible with known ones. In addition, the mTIP3P water model is computationally more efficient than those of top-ranked AMBER FFs with four-site water models. The evaluation also reveals that AMBER FFs tend to generate more compact conformations compared to CHARMM FFs but also more non-native contacts. The top-ranking AMBER and CHARMM FFs can reproduce intra-peptide contacts but underperform for inter-peptide contacts, indicating there is room for improvement.

Whole-cell bioconversion using non-Leloir transglycosylation reactions: a review

Microbial biocatalysts are evolving technological tools for glycosylation research in food, feed and pharmaceuticals. Advances in bioengineered Leloir and non-Leloir carbohydrate-active enzymes allow for whole-cell biocatalysts to curtail production costs of purified enzymes while enhancing glucan synthesis through continued enzyme expression. Unlike sugar nucleotide-dependent Leloir glycosyltransferases, non-Leloir enzymes require inexpensive sugar donors and can be designed to match the high value, yield and selectivity of the former. This review addresses the current state of bacterial cell-based production of glucans and glycoconjugates via transglycosylation, and describes how alterations made to microbial hosts to surpass purified enzymes as the preferred mode of catalysis are steadily being acquired through genetic engineering, rational design and process optimization. A comprehensive exploration of relevant literature has been summarized to describe whole-cell biocatalysis in non-Leloir glycosylation reactions with various donors and acceptors, and the characterization, application and latest developments in the optimization of their use.

IDP Force Fields Applied to Model PPII-Rich 33-mer Gliadin Peptides

The 33-mer gliadin peptide and its deamidated metabolite, 33-mer DGP, are the immunodominant peptides responsible for the adaptive immune response in celiac disease (CD). CD is a complex autoimmune chronic disorder triggered by gluten ingestion that affects the small intestine and affects ∼1% of the global population. The 33-mers are polyproline II-rich (PPII) and intrinsically disordered peptides (IDPs), whose structures remain elusive. We sampled the conformational ensembles of both 33-mer peptides via molecular dynamics simulations employing two force fields (FFs) (Amber ff03ws and Amber ff99SB-disp) specifically validated for other IDPs. Our results show that both FFs allow the extensive exploration of the conformational landscape, which was not possible with the standard FF GROMOS53A6 reported before. Clustering analysis of the trajectories showed that the five largest clusters (78-88% of the total structures) present elongated, semielongated, and curved conformations in both FFs. Large average radius of gyration and solvent-exposed surfaces characterized these structures. While the structures sampled are similar, the Amber ff99SB-disp trajectories explored folded conformations with a higher probability. In addition, PPII secondary structure was preserved throughout the trajectories (58-73%) together with a non-negligible content of β structures (11-23%), in agreement with previous experimental results. This work represents the initial step in studying further the interaction of these peptides with other biologically relevant molecules, which could lead to finally disclose the molecular events that lead to CD.

Simple and Effective Conformational Sampling Strategy for Intrinsically Disordered Proteins Using the UNRES Web Server

Intrinsically disordered proteins (IDPs) contain more charged amino acids than folded proteins, resulting in a lack of hydrophobic core(s) and a tendency to adopt rapidly interconverting structures rather than well-defined structures. The structural heterogeneity of IDPs, encoded by the amino acid sequence, is closely related to their unique roles in biological pathways, which require them to interact with different binding partners. Recently, Robustelli and co-workers have demonstrated that a balanced all-atom force field can be used to sample heterogeneous structures of disordered proteins ( Proc. Natl. Acad. Sci. U.S.A. 2018, 115, E4758-E4766). However, such a solution requires extensive computational resources, such as Anton supercomputers. Here, we propose a simple and effective solution to sample the conformational space of IDPs using a publicly available web server, namely, the UNited-RESidue (UNRES) web server. Our proposed solution requires no investment in computational resources and no prior knowledge of UNRES. UNRES Replica Exchange Molecular Dynamics (REMD) simulations were carried out on a set of eight disordered proteins at temperatures spanning from 270 to 430 K. Utilizing the latest UNRES force field designed for structured proteins, with proper selections of temperatures, we were able to produce comparable results to all-atom force fields as reported in work done by Robustelli and co-workers. In addition, NMR observables and the radius of gyration calculated from UNRES ensembles were directly compared with the experimental data to further evaluate the accuracy of the UNRES model at all temperatures. Our results suggest that carrying out the UNRES simulations at optimal temperatures using the UNRES web server can be a good alternative to sample heterogeneous structures of IDPs.

Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling

The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.

Illuminating Intrinsically Disordered Proteins with Integrative Structural Biology

Intense study of intrinsically disordered proteins (IDPs) did not begin in earnest until the late 1990s when a few groups, working independently, convinced the community that these 'weird' proteins could have important functions. Over the past two decades, it has become clear that IDPs play critical roles in a multitude of biological phenomena with prominent examples including coordination in signaling hubs, enabling gene regulation, and regulating ion channels, just to name a few. One contributing factor that delayed appreciation of IDP functional significance is the experimental difficulty in characterizing their dynamic conformations. The combined application of multiple methods, termed integrative structural biology, has emerged as an essential approach to understanding IDP phenomena. Here, we review some of the recent applications of the integrative structural biology philosophy to study IDPs.

Combining NMR, SAXS and SANS to characterize the structure and dynamics of protein complexes

Understanding the structure and dynamics of biological macromolecules is essential to decipher the molecular mechanisms that underlie cellular functions. The description of structure and conformational dynamics often requires the integration of complementary techniques. In this review, we highlight the utility of combining nuclear magnetic resonance (NMR) spectroscopy with small angle scattering (SAS) to characterize these challenging biomolecular systems. NMR can assess the structure and conformational dynamics of multidomain proteins, RNAs and biomolecular complexes. It can efficiently provide information on interaction surfaces, long-distance restraints and relative domain orientations at residue-level resolution. Such information can be readily combined with high-resolution structural data available on subcomponents of biomolecular assemblies. Moreover, NMR is a powerful tool to characterize the dynamics of biomolecules on a wide range of timescales, from nanoseconds to seconds. On the other hand, SAS approaches provide global information on the size and shape of biomolecules and on the ensemble of all conformations present in solution. Therefore, NMR and SAS provide complementary data that are uniquely suited to investigate dynamic biomolecular assemblies. Here, we briefly review the type of data that can be obtained by both techniques and describe different approaches that can be used to combine them to characterize biomolecular assemblies. We then provide guidelines on which experiments are best suited depending on the type of system studied, ranging from fully rigid complexes, dynamic structures that interconvert between defined conformations and systems with very high structural heterogeneity.

Structural and Dynamic Effects of PTEN C-Terminal Tail Phosphorylation

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor gene encodes a tightly regulated dual-specificity phosphatase that serves as the master regulator of PI3K/AKT/mTOR signaling. The carboxy-terminal tail (CTT) is key to regulation and harbors multiple phosphorylation sites (Ser/Thr residues 380-385). CTT phosphorylation suppresses the phosphatase activity by inducing a stable, closed conformation. However, little is known about the mechanisms of phosphorylation-induced CTT-deactivation dynamics. Using explicit solvent microsecond molecular dynamics simulations, we show that CTT phosphorylation leads to a partially collapsed conformation, which alters the secondary structure of PTEN and induces long-range conformational rearrangements that encompass the active site. The active site rearrangements prevent localization of PTEN to the membrane, precluding lipid phosphatase activity. Notably, we have identified phosphorylation-induced allosteric coupling between the interdomain region and a hydrophobic site neighboring the active site in the phosphatase domain. Collectively, the results provide a mechanistic understanding of CTT phosphorylation dynamics and reveal potential druggable allosteric sites in a previously believed clinically undruggable protein.

SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions

Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.

Evaluation of implicit solvent models in molecular dynamics simulation of α-Synuclein

We report conventional and accelerated molecular dynamics simulations of α-Synuclein, designed to assess performance of using different starting conformation, solvation environment and force field combination. Backbone and sidechain chemical shifts, radius of gyration, presence of β-hairpin structures in KTK(E/Q)GV repeats and secondary structure percentages were used to evaluate how variations in forcefield, solvation model and simulation protocol provide results that correlate with experimental findings. We show that with suitable choice of forcefield and solvent, ff03ws and OBC implicit model, respectively, acceptable reproduction of experimental data on size and secondary structure is obtained by both conventional and accelerated MD. In contrast to the implicit solvent model, simulations in explicit TIP4P/2005 solvent do not properly represent size or secondary structure of α-Synuclein.Communicated by Ramaswamy H. Sarma.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

How well does molecular simulation reproduce environment-specific conformations of the intrinsically disordered peptides PLP, TP2 and ONEG?

Understanding the conformational ensembles of intrinsically disordered proteins and peptides (IDPs) in their various biological environments is essential for understanding their mechanisms and functional roles in the proteome, leading to a greater knowledge of, and potential treatments for, a broad range of diseases. To determine whether molecular simulation is able to generate accurate conformational ensembles of IDPs, we explore the structural landscape of the PLP peptide (an intrinsically disordered region of the proteolipid membrane protein) in aqueous and membrane-mimicking solvents, using replica exchange with solute scaling (REST2), and examine the ability of four force fields (ff14SB, ff14IDPSFF, CHARMM36 and CHARMM36m) to reproduce literature circular dichroism (CD) data. Results from variable temperature (VT) 1H and Rotating frame Overhauser Effect SpectroscopY (ROESY) nuclear magnetic resonance (NMR) experiments are also presented and are consistent with the structural observations obtained from the simulations and CD. We also apply the optimum simulation protocol to TP2 and ONEG (a cell-penetrating peptide (CPP) and a negative control peptide, respectively) to gain insight into the structural differences that may account for the observed difference in their membrane-penetrating abilities. Of the tested force fields, we find that CHARMM36 and CHARMM36m are best suited to the study of IDPs, and accurately predict a disordered to helical conformational transition of the PLP peptide accompanying the change from aqueous to membrane-mimicking solvents. We also identify an α-helical structure of TP2 in the membrane-mimicking solvents and provide a discussion of the mechanistic implications of this observation with reference to the previous literature on the peptide. From these results, we recommend the use of CHARMM36m with the REST2 protocol for the study of environment-specific IDP conformations. We believe that the simulation protocol will allow the study of a broad range of IDPs that undergo conformational transitions in different biological environments.

Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor

Class B G protein-coupled receptors (GPCRs) are important targets in the treatment of metabolic syndrome and diabetes. Although multiple structures of class B GPCRs-G protein complexes have been elucidated, the detailed activation mechanism of the receptors remains unclear. Here, we combine Gaussian accelerated molecular dynamics simulations and Markov state models (MSM) to investigate the activation mechanism of a canonical class B GPCR, human glucagon receptor-GCGR, including the negative allosteric modulator-bound inactive state, the agonist glucagon-bound active state, and both glucagon- and Gs-bound fully active state. The free-energy landscapes of GCGR show the conformational ensemble consisting of three activation-associated states: inactive, active, and fully active. The structural analysis indicates the high dynamics of GCGR upon glucagon binding with both active and inactive conformations in the ensemble. Significantly, the H8 and TM6 exhibits distinct features from the inactive to the active states. The additional simulations demonstrate the role of H8 in the recruitment of Gs. Gs binding presents a crucial function of stabilizing the glucagon binding site and MSM highlights the absolute requirement of Gs to help the GCGR reach the fully active state. Together, our results reveal the detailed activation mechanism of GCGR from the view of conformational dynamics.

A General Small-Angle X-ray Scattering-Based Screening Protocol for Studying Physical Stability of Protein Formulations

A fundamental step in developing a protein drug is the selection of a stable storage formulation that ensures efficacy of the drug and inhibits physiochemical degradation or aggregation. Here, we designed and evaluated a general workflow for screening of protein formulations based on small-angle X-ray scattering (SAXS). Our SAXS pipeline combines automated sample handling, temperature control, and fast data analysis and provides protein particle interaction information. SAXS, together with different methods including turbidity analysis, dynamic light scattering (DLS), and SDS-PAGE measurements, were used to obtain different parameters to provide high throughput screenings. Using a set of model proteins and biopharmaceuticals, we show that SAXS is complementary to dynamic light scattering (DLS), which is widely used in biopharmaceutical research and industry. We found that, compared to DLS, SAXS can provide a more sensitive measure for protein particle interactions, such as protein aggregation and repulsion. Moreover, we show that SAXS is compatible with a broader range of buffers, excipients, and protein concentrations and that in situ SAXS provides a sensitive measure for long-term protein stability. This workflow can enable future high-throughput analysis of proteins and biopharmaceuticals and can be integrated with well-established complementary physicochemical analysis pipelines in (biopharmaceutical) research and industry.

How multisite phosphorylation impacts the conformations of intrinsically disordered proteins

Phosphorylation of intrinsically disordered proteins (IDPs) can produce changes in structural and dynamical properties and thereby mediate critical biological functions. How phosphorylation effects intrinsically disordered proteins has been studied for an increasing number of IDPs, but a systematic understanding is still lacking. Here, we compare the collapse propensity of four disordered proteins, Ash1, the C-terminal domain of RNA polymerase (CTD2’), the cytosolic domain of E-Cadherin, and a fragment of the p130Cas, in unphosphorylated and phosphorylated forms using extensive all-atom molecular dynamics (MD) simulations. We find all proteins to show V-shape changes in their collapse propensity upon multi-site phosphorylation according to their initial net charge: phosphorylation expands neutral or overall negatively charged IDPs and shrinks positively charged IDPs. However, force fields including those tailored towards and commonly used for IDPs overestimate these changes. We find quantitative agreement of MD results with SAXS and NMR data for Ash1 and CTD2’ only when attenuating protein electrostatic interactions by using a higher salt concentration (e.g. 350 mM), highlighting the overstabilization of salt bridges in current force fields. We show that phosphorylation of IDPs also has a strong impact on the solvation of the protein, a factor that in addition to the actual collapse or expansion of the IDP should be considered when analyzing SAXS data. Compared to the overall mild change in global IDP dimension, the exposure of active sites can change significantly upon phosphorylation, underlining the large susceptibility of IDP ensembles to regulation through post-translational modifications.

Intrinsically disordered proteins (IDPs) are a class of proteins that lack secondary and tertiary structures and instead explore a broad conformational ensemble. Their functions, from transcriptional regulation to signal transmission, are tightly regulated. IDPs are subject of extensive reversible post-translational modifications (PTMs), such as phosphorylation, methylation and glycosylation. Among these PTMs, phosphorylation is one of the most common and important PTMs. However, the mechanism of how phosphorylation affects the conformations and functions of IDPs remains unclear. To answer this question, we have performed extensive all-atom molecular dynamics simulations for four representative IDPs: Ash1, E-Cadherin, CTD2’ and p130Cas in their unphosphorylated and phosphorylated forms. Our results showed that all IDPs undergo a mild change upon multi-site phosphorylation, which is V-shaped: phosphorylation moderately expands neutral or overall negatively charged IDPs and shrinks positively charged IDPs. More importantly, in two of these IDPs, only two biologically relevant phosphorylation sites suffice to render the adjacent negatively charged active site significantly more exposed to the environment, which implies a higher probability to interact with other binding partners.

How to assess the structural dynamics of transcription factors by integrating sparse NMR and EPR constraints with molecular dynamics simulations

We review recent advances in modeling structural ensembles of transcription factors from nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopic data, integrated with molecular dynamics (MD) simulations. We focus on approaches that confirm computed conformational ensembles by sparse constraints obtained from magnetic resonance. This combination enables the deduction of functional and structural protein models even if nuclear Overhauser effects (NOEs) are too scarce for conventional structure determination. We highlight recent insights into the folding-upon-DNA binding transitions of intrinsically disordered transcription factors that could be assessed using such integrative approaches.

Comment on the Optimal Parameters to Derive Intrinsically Disordered Protein Conformational Ensembles from Small-Angle X-ray Scattering Data Using the Ensemble Optimization Method

The Ensemble Optimization Method (EOM) is a popular approach to describe small-angle X-ray scattering (SAXS) data from highly disordered proteins. The EOM algorithm selects subensembles of coexisting states from large pools of randomized conformations to fit the SAXS data. Based on the unphysical bimodal radius of gyration (R_g) distribution of conformations resulting from the EOM analysis, a recent article (Fagerberg et al. J. Chem. Theory Comput. 2019, 15 (12), 6968-6983) concluded that this approach inadequately described the SAXS data measured for human Histatin 5 (Hst5), a peptide with antifungal properties. Using extensive experimental and synthetic data, we explored the origin of this observation. We found that the one-bead-per-residue coarse-grained representation with averaged scattering form factors (provided in the EOM as an add-on to represent disordered missing loops or domains) may not be appropriate for EOM analyses of scattering data from short (below 50 residues) proteins/peptides. The method of choice for these proteins is to employ atomistic models (e.g., from molecular dynamics simulations) to sample the protein conformational landscape. As a convenient alternative, we have also improved the coarse-grained approach by introducing amino acid specific form factors in the calculations. We also found that, for small proteins, the search for relatively large subensembles of 20-50 conformers (as implemented in the original EOM version) more adequately describes the conformational space sampled in solution than the procedures optimizing the ensemble size. Our observations have been added as recommendations into the information for EOM users to promote the proper utilization of the program for ensemble-based modeling of SAXS data for all types of disordered systems.

Full structural ensembles of intrinsically disordered proteins from unbiased molecular dynamics simulations

Molecular dynamics (MD) simulation is widely used to complement ensemble-averaged experiments of intrinsically disordered proteins (IDPs). However, MD often suffers from limitations of inaccuracy. Here, we show that enhancing the sampling using Hamiltonian replica-exchange MD (HREMD) led to unbiased and accurate ensembles, reproducing small-angle scattering and NMR chemical shift experiments, for three IDPs of varying sequence properties using two recently optimized force fields, indicating the general applicability of HREMD for IDPs. We further demonstrate that, unlike HREMD, standard MD can reproduce experimental NMR chemical shifts, but not small-angle scattering data, suggesting chemical shifts are insufficient for testing the validity of IDP ensembles. Surprisingly, we reveal that despite differences in their sequence, the inter-chain statistics of all three IDPs are similar for short contour lengths (< 10 residues). The results suggest that the major hurdle of generating an accurate unbiased ensemble for IDPs has now been largely overcome.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

A Switch between Two Intrinsically Disordered Conformational Ensembles Modulates the Active Site of a Basic-Helix-Loop-Helix Transcription Factor

We report a conformational switch between two distinct intrinsically disordered subensembles within the active site of a transcription factor. This switch highlights an evolutionary benefit conferred by the high plasticity of intrinsically disordered domains, namely, their potential to dynamically sample a heterogeneous conformational space housing multiple states with tailored properties. We focus on proto-oncogenic basic-helix-loop-helix (bHLH)-type transcription factors, as these play key roles in cell regulation and function. Despite intense research efforts, the understanding of structure-function relations of these transcription factors remains incomplete as they feature intrinsically disordered DNA-interaction domains that are difficult to characterize, theoretically as well as experimentally. Here we characterize the structural dynamics of the intrinsically disordered region DNA-binding site of the vital MYC-associated transcription factor X (MAX). Integrating nuclear magnetic resonance (NMR) measurements, molecular dynamics (MD) simulations, and electron paramagnetic resonance (EPR) measurements, we show that, in the absence of DNA, the binding site of the free MAX₂ homodimer samples two intrinsically disordered conformational subensembles. These feature distinct structural properties: one subensemble consists of a set of highly flexible and spatially extended conformers, while the second features a set of "hinged" conformations. In this latter ensemble, the disordered N-terminal tails of MAX₂ fold back along the dimer, forming transient long-range contacts with the HLH-region and thereby exposing the DNA binding site to the solvent. The features of these divergent substates suggest two mechanisms by which protein conformational dynamics in MAX₂ might modulate DNA-complex formation: by enhanced initial recruitment of free DNA ligands, as a result of the wider conformational space sampled by the extended ensemble, and by direct exposure of the binding site and the corresponding strong electrostatic attractions presented while in the hinged conformations.

Comprehensive Structural and Thermodynamic Analysis of Prefibrillar WT α-Synuclein and Its G51D, E46K, and A53T Mutants by a Combination of Small-Angle X-ray Scattering and Variational Bayesian Weighting

The in solution synchrotron small-angle X-ray scattering SAXS technique has been used to investigate an intrinsically disordered protein (IDP) related to Parkinson's disease, the α-synuclein (α-syn), in prefibrillar diluted conditions. SAXS experiments have been performed as a function of temperature and concentration on the wild type (WT) and on the three pathogenic mutants G51D, E46K, and A53T. To identify the conformers that populate WT α-syn and the pathogenic mutants in prefibrillar conditions, scattering data have been analyzed by a new variational bayesian weighting method (VBWSAS) based on an ensemble of conformers, which includes unfolded monomers, trimers, and tetramers, both in helical-rich and strand-rich forms. The developed VBWSAS method uses a thermodynamic scheme to account for temperature and concentration effects and considers long-range protein-protein interactions in the framework of the random phase approximation. The global analysis of the whole set of data indicates that WT α-syn is mostly present as unfolded monomers and trimers (helical-rich trimers at low T and strand-rich trimers at high T), but not tetramers, as previously derived by several studies. On the contrary, different conformer combinations characterize mutants. In the α-syn G51D mutant, the most abundant aggregates at all the temperatures are strand-rich tetramers. Strand-rich tetramers are also the predominant forms in the A53T mutant, but their weight decreases with temperature. Only monomeric conformers, with a preference for the ones with the smallest sizes, are present in the E46K mutant. The derived conformational behavior then suggests a different availability of species prone to aggregate, depending on mutation, temperature, and concentration and accounting for the different neurotoxicity of α-syn variants. Indeed, this approach may be of pivotal importance to describe conformational and aggregational properties of other IDPs.

Structural ensemble and biological activity of DciA intrinsically disordered region

DciA is a newly discovered bacterial protein involved in loading the replicative helicase DnaB onto DNA at the initiation step of chromosome replication. Its three-dimensional structure is composed of a folded N-terminal domain (residues 1-111) resembling K Homology domains and a long disordered C-terminal tail (residues 112-157) which structure-activity relationship remains to be elucidated. In the present study on Vibrio cholerae DciA, we emphasize the importance of its disordered region to load DnaB onto DNA using surface plasmon resonance (SPR) and isothermal titration microcalorimetry (ITC). Then we characterize the conformational ensemble of the full-length protein using a combination of circular dichroism (CD), small angle X-ray scattering (SAXS), and molecular dynamics (MD) simulations. The atomic-level structural ensemble generated by MD simulations is in very good agreement with SAXS data. From initial conformations of the C-terminal tail without any secondary structure, our simulations bring to light several transient helical structures in this segment, which might be molecular recognition features (MoRFs) for the binding to DnaB and its recruitment and loading onto DNA.

Common Functions of Disordered Proteins across Evolutionary Distant Organisms

Intrinsically disordered proteins and regions typically lack a well-defined structure and thus fall outside the scope of the classic sequence–structure–function relationship. Hence, classic sequence- or structure-based bioinformatic approaches are often not well suited to identify homology or predict the function of unknown intrinsically disordered proteins. Here, we give selected examples of intrinsic disorder in plant proteins and present how protein function is shared, altered or distinct in evolutionary distant organisms. Furthermore, we explore how examining the specific role of disorder across different phyla can provide a better understanding of the common features that protein disorder contributes to the respective biological mechanism.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.