pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins

Reference Data Set for Circular Dichroism Spectroscopy Comprised of Validated Intrinsically Disordered Protein Models

Circular dichroism (CD) spectroscopy is an analytical technique that measures the wavelength-dependent differential absorbance of circularly polarized light and is applicable to most biologically important macromolecules, such as proteins, nucleic acids, and carbohydrates. It serves to characterize the secondary structure composition of proteins, including intrinsically disordered proteins, by analyzing their recorded spectra. Several computational tools have been developed to interpret protein CD spectra. These methods have been calibrated and tested mostly on globular proteins with well-defined structures, mainly due to the lack of reliable reference structures for disordered proteins. It is therefore still largely unclear how accurately these computational methods can determine the secondary structure composition of disordered proteins. Here, we provide such a required reference data set consisting of model structural ensembles and matching CD spectra for eight intrinsically disordered proteins. Using this set of data, we have assessed the accuracy of several published CD prediction and secondary structure estimation tools, including our own CD analysis package, SESCA. Our results show that for most of the tested methods, their accuracy for disordered proteins is generally lower than for globular proteins. In contrast, SESCA, which was developed using globular reference proteins, but was designed to be applicable to disordered proteins as well, performs similarly well for both classes of proteins. The new reference data set for disordered proteins should allow for further improvement of all published methods.

PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins

The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.

The Anthocyanidin Peonidin Interferes with an Early Step in the Fibrillation Pathway of α-Synuclein and Modulates It toward Amorphous Aggregates

Parkinson's disease (PD) is characterized by progressive degeneration of the dopaminergic neurons in the brain, accompanied by the accumulation of proteinaceous inclusions, Lewy bodies (LB), mainly comprised of alpha synuclein (α-syn) aggregates. The heterogeneity and the transient nature of the intermediate species formed in the α-syn fibrillation pathway have made it difficult to develop an effective therapeutic intervention. Therefore, any therapeutic molecule that could prevent as well as treat PD would be of great interest. Anthocyanidins are natural flavonoid compounds that have been shown to have neuroprotective properties and to modulate factors that cause neuronal death. Herein, we have explored the modulation and inhibition of α-syn fibrillation by the anthocyanidins cyanidin, delphinidin, and peonidin using a number of biophysical and structural tools. α-Syn fibrillation monitored using thioflavin T (ThT) fluorescence and light scattering suggested concentration dependent inhibition of α-syn fibrillation by all the three anthocyanidins. While cyanidin and delphinidin induced the formation of oligomers and small fibrillar structures of α-syn, respectively, peonidin led to the formation of amorphous aggregates, as observed by Atomic Force Microscopy (AFM). Peonidin proved to be most effective of the three anthocyanidins toward alleviating cell toxicity of SH-SY5Y neuroblastoma cells at concentrations where α-synuclein fibrillation was completely suppressed. Hence, the inhibition mechanism of peonidin was further explored by studying its interaction with α-syn using titration calorimetry and molecular docking. The results show its weak binding (in mM range) to the NAC region of α-syn through hydrogen bonding interactions. Also, circular dichroism and Raman spectroscopy revealed the structural aspects of peonidin-induced α-syn amorphous aggregates showing alpha helical structures with exposed Phe and Tyr regions. Due to the neuroprotective nature of peonidin, the findings reported here are significant and can be further explored toward developing a modifying therapy that could address both disease onset as well as the progression of PD.

Fuzzy Drug Targets: Disordered Proteins in the Drug-Discovery Realm

Intrinsically disordered proteins (IDPs) and regions (IDRs) form a large part of the eukaryotic proteome. Contrary to the structure-function paradigm, the disordered proteins perform a myriad of functions in vivo. Consequently, they are involved in various disease pathways and are plausible drug targets. Unlike folded proteins, that have a defined structure and well carved out drug-binding pockets that can guide lead molecule selection, the disordered proteins require alternative drug-development methodologies that are based on an acceptable picture of their conformational ensemble. In this review, we discuss various experimental and computational techniques that contribute toward understanding IDP "structure" and describe representative pursuances toward IDP-targeting drug development. We also discuss ideas on developing rational drug design protocols targeting IDPs.

In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials

RNA–protein interactions are crucial for diverse biological processes. In prokaryotes, RNA–protein interactions enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements through crRNA-mediated nuclease activities of Cas proteins. Thanks to the programmability and specificity of CRISPR-Cas systems, CRISPR-based antimicrobials have the potential to be repurposed as new types of antibiotics. Unlike traditional antibiotics, these CRISPR-based antimicrobials can be designed to target specific bacteria and minimize detrimental effects on the human microbiome during antibacterial therapy. In this study, we explore the potential of CRISPR-based antimicrobials by optimizing the RNA–protein interactions of crRNAs and Cas13 proteins. CRISPR-Cas13 systems are unique as they degrade specific foreign RNAs using the crRNA template, which leads to non-specific RNase activities and cell cycle arrest. We show that a high proportion of the Cas13 systems have no colocalized CRISPR arrays, and the lack of direct association between crRNAs and Cas proteins may result in suboptimal RNA–protein interactions in the current tools. Here, we investigate the RNA–protein interactions of the Cas13-based systems by curating the validation dataset of Cas13 protein and CRISPR repeat pairs that are experimentally validated to interact, and the candidate dataset of CRISPR repeats that reside on the same genome as the currently known Cas13 proteins. To find optimal CRISPR-Cas13 interactions, we first validate the 3-D structure prediction of crRNAs based on their experimental structures. Next, we test a number of RNA–protein interaction programs to optimize the in silico docking of crRNAs with the Cas13 proteins. From this optimized pipeline, we find a number of candidate crRNAs that have comparable or better in silico docking with the Cas13 proteins of the current tools. This study fully automatizes the in silico optimization of RNA–protein interactions as an efficient preliminary step for designing effective CRISPR-Cas13-based antimicrobials.

More is simpler: Decomposition of ligand-binding affinity for proteins being disordered

In statistical mechanics, it is well known that the huge number of degrees of freedom does not complicate the problem as it seems, but actually greatly simplifies the analysis (e.g., to give a Boltzmann distribution). Here, we reveal that the ensemble averaging from the vast conformations of intrinsically disordered proteins (IDPs) greatly simplifies the nature of binding affinity, which can be reliably decomposed into a sum of the ligandability of IDP and the capacity of ligand. Such an unexpected regularity is applied to facilitate the virtual screening upon IDPs. It also provides essential insight in understanding the specificity difference between IDPs and conventional ordered proteins since the specificity is caused by deviation from the baseline behavior of protein-ligand binding.

Insights into Membrane Curvature Sensing and Membrane Remodeling by Intrinsically Disordered Proteins and Protein Regions

Cellular membranes are highly dynamic in shape. They can rapidly and precisely regulate their shape to perform various cellular functions. The protein’s ability to sense membrane curvature is essential in various biological events such as cell signaling and membrane trafficking. As they are bound, these curvature-sensing proteins may also change the local membrane shape by one or more curvature driving mechanisms. Established curvature-sensing/driving mechanisms rely on proteins with specific structural features such as amphipathic helices and intrinsically curved shapes. However, the recent discovery and characterization of many proteins have shattered the protein structure–function paradigm, believing that the protein functions require a unique structural feature. Typically, such structure-independent functions are carried either entirely by intrinsically disordered proteins or hybrid proteins containing disordered regions and structured domains. It is becoming more apparent that disordered proteins and regions can be potent sensors/inducers of membrane curvatures. In this article, we outline the basic features of disordered proteins and regions, the motifs in such proteins that encode the function, membrane remodeling by disordered proteins and regions, and assays that may be employed to investigate curvature sensing and generation by ordered/disordered proteins.

Predicting Protein Conformational Disorder and Disordered Binding Sites

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

Unveiling the structure of the primary caseinate particle using small-angle X-ray scattering and simulation methodologies

The low-resolution structure of casein (CN) clusters in sodium caseinate (NaCas) solution and its conformational dynamics were obtained by size-exclusion chromatography (SEC), analytical ultracentrifugation (AUC), small-angle X-ray scattering (SAXS), and molecular dynamics (MD) simulations. The results of sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and native PAGE revealed that the casein clusters consisted predominantly of α- and β-CN complexes, and a trace amount of κ-CN. The AUC analysis indicated that the casein clusters were composed of 34.6% of casein monomers, 19.2%, 20.4%, and 25.8% of complexes with molar weight (M_w) of ~50.3, ~70.6, and ~133 kDa, respectively. The volume fractions of components in casein clusters were quantified as 64.3% of α_s1-β-α_s2-CN, 22.3% of α_s1-CN, 8.5% of α_s2-CN, and 4.4% of α_s1-α_s2-CN, respectively. The ensemble optimization method (EOM) gave a fitting result where α_s1-β-α_s2-CN species coexisted in ~35.3% under compact conformation and ~64.7% in elongated conformation in solution. The three-dimensional structures of α_s1-β-α_s2-CN from EOM showed a good overlay on the casein clusters ab initio model obtained from DAMMIN and DAMMIX program. MD simulations revealed that α_s1-β-α_s2-CN underwent a conformational change from the elongated state into the compact state within the initial 200 ns of simulations. The addition of nonionic surfactants affected little the backbone-to-backbone interactions in the formation of the casein clusters. We propose that α_s1-CN, β-CN, α_s2-CN, and κ-CN associated in consecutive steps into casein clusters, and a trace of κ-CN may be located at the surface of the assemblies limiting the growth of casein aggregates.

Identification and characterization of the cell division protein MapZ from Streptococcus suis

Streptococcus suis, an emerging zoonotic pathogen, causes invasive diseases in pigs, including sepsis, meningitis, endocarditis, pneumonia, and arthritis. Importantly, similar pathologies are reported in human S. suis infections. In previous work, the locus SSU0375 of S. suis strain P1.7 had been identified as a conditionally essential gene by intrathecal experimental infection of pigs with a transposon library of S. suis. This study aimed to identify the function of the corresponding gene product. Bioinformatics analysis and homology modeling revealed sequence and structural homologies with the Streptococcus pneumoniae mid-cell-anchored protein Z (MapZ) that is involved in cell division in different bacterial species. Indeed, depletion of this locus in S. suis strain 10 revealed a growth defect as compared to the wild type. Electron microscopy analysis of the corresponding mutant demonstrated morphological growth defects as compared to the wild-type strain, including an irregular cell shape and size as well as mispositioned division septa. Light microscopy and subsequent quantitative image analysis confirmed these morphological alterations. In the genetic rescue strain, the wild-type phenotype was completely restored. In summary, we proposed that SSU0375 or the corresponding locus in strain 10 encode for a S. suis MapZ homolog that guides septum positioning as evidenced for other members of the Streptococci family.

AlphaFold and Implications for Intrinsically Disordered Proteins

Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the "structures" of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.

The sequence-ensemble relationship in fuzzy protein complexes

Intrinsically disordered proteins (IDPs) interact with globular proteins through a variety of mechanisms, resulting in the structurally heterogeneous ensembles known as fuzzy complexes. While there exists a reasonable comprehension on how IDP sequence determines the unbound IDP ensemble, little is known about what shapes the structural characteristics of IDPs bound to their targets. Using a statistical thermodynamic model, we show that the target-bound ensembles are determined by a simple code that combines the IDP sequence and the distribution of IDP-target interaction hotspots. These two parameters define the conformational space of target-bound IDPs and rationalize the observed structural heterogeneity of fuzzy complexes. The presented model successfully reproduces the dynamical signatures of target-bound IDPs from the NMR relaxation experiments as well as the changes of interaction affinity and the IDP helicity induced by mutations. The model explains how the target-bound IDP ensemble adapts to mutations in order to achieve an optimal balance between conformational freedom and interaction energy. Taken together, the presented sequence-ensemble relationship of fuzzy complexes explains the different manifestations of IDP disorder in folding-upon-binding processes.

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.

Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database

The Protein Ensemble Database (PED; https://proteinensemble.org/) is the major repository of conformational ensembles of intrinsically disordered proteins (IDPs). Conformational ensembles of IDPs are primarily provided by their authors or occasionally collected from literature, and are subsequently deposited in PED along with the corresponding structured, manually curated metadata. The modeling of conformational ensembles usually relies on experimental data from small-angle X-ray scattering (SAXS), fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics (MD) simulations, or a combination of these techniques. The growing number of scientific studies based on these data, along with the astounding and swift progress in the field of protein intrinsic disorder, has required a significant update and upgrade of PED, first published in 2014. To this end, the database was entirely renewed in 2020 and now has a dedicated team of biocurators providing manually curated descriptions of the methods and conditions applied to generate the conformational ensembles and for checking consistency of the data. Here, we present a detailed description on how to explore PED with its protein pages and experimental pages, and how to interpret entries of conformational ensembles. We describe how to efficiently search conformational ensembles deposited in PED by means of its web interface and API. We demonstrate how to make sense of the PED protein page and its associated experimental entry pages with reference to the yeast Sic1 use case. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Performing a search in PED Support Protocol 1: Programmatic access with the PED API Basic Protocol 2: Interpreting the protein page and the experimental entry page-the Sic1 use case Support Protocol 2: Downloading options Support Protocol 3: Understanding the validation report-the Sic1 use case Basic Protocol 3: Submitting new conformational ensembles to PED Basic Protocol 4: Providing feedback in PED.

"Protein" no longer means what it used to

Every biologist knows that the word protein describes a group of macromolecules essential to sustain life on Earth. As biologists, we are invariably trained under a protein paradigm established since the early twentieth century. However, in recent years, the term protein unveiled itself as an euphemism to describe the overwhelming heterogeneity of these compounds. Most of our current studies are targeted on carefully selected subsets of proteins, but we tend to think and write about these as representative of the whole population. Here we discuss how seeking for universal definitions and general rules in any arbitrarily segmented study would be misleading about the conclusions. Of course, it is not our purpose to discourage the use of the word protein. Instead, we suggest to embrace the extended universe of proteins to reach a deeper understanding of their full potential, realizing that the term encompasses a group of molecules very heterogeneous in terms of size, shape, chemistry and functions, i.e. the term protein no longer means what it used to.

CheSPI: chemical shift secondary structure population inference

NMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI provides predictions for up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

Reinforcement learning to boost molecular docking upon protein conformational ensemble

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

MEDUSA: Prediction of Protein Flexibility from Sequence

Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins

The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.

MobiDB: intrinsically disordered proteins in 2021

The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.

MMM: Integrative ensemble modeling and ensemble analysis

Proteins and their complexes can be heterogeneously disordered. In ensemble modeling of such systems with restraints from several experimental techniques the following problems arise: (a) integration of diverse restraints obtained on different samples under different conditions; (b) estimation of a realistic ensemble width; (c) sufficient sampling of conformational space; (d) representation of the ensemble by an interpretable number of conformers; (e) recognition of weak order with site resolution. Here, I introduce several tools that address these problems, focusing on utilization of distance distribution information for estimating ensemble width. The RigiFlex approach integrates such information with high-resolution structures of ordered domains and small-angle scattering data. The EnsembleFit module provides moderately sized ensembles by fitting conformer populations and discarding conformers with low population. EnsembleFit balances the loss in fit quality upon combining restraint subsets from different techniques. Pair correlation analysis for residues and local compaction analysis help in feature detection. The RigiFlex pipeline is tested on data simulated from the structure 70 kDa protein-RNA complex RsmE/RsmZ. It recovers this structure with ensemble width and difference from ground truth both being on the order of 4.2 Å. EnsembleFit reduces the ensemble of the proliferating-cell-nuclear-antigen-associated factor p15PAF from 4,939 to 75 conformers while maintaining good fit quality of restraints. Local compaction analysis for the PaaA2 antitoxin from E. coli O157 revealed correlations between compactness and enhanced residual dipolar couplings in the original NMR restraint set.

Cu2+, Ca2+, and methionine oxidation expose the hydrophobic α-synuclein NAC domain

α-Synuclein is an intrinsically disordered protein whose aggregation is related to Parkinson's disease and other neurodegenerative disorders. Metal cations are one of the main factors affecting the propensity of α-synuclein to aggregate, either by directly binding to it or by catalyzing the production of reactive oxygen species that oxidize it. His50, Asp121 and several additional C-terminal α-synuclein residues are binding sites for numerous metal cations, while methionine sulfoxidation occurs readily on this protein under oxidative stress conditions. Molecular dynamics simulations are an excellent tool to obtain a microscopic picture of how metal binding or methionine sulfoxidation alter the conformational preferences of α-synuclein and, hence, its aggregation propensity. In this work, we report the first coarse-grained molecular dynamics study comparing the conformational ensembles of the native protein, the protein bound to either Cu²⁺ or Ca²⁺ at its main binding sites, and the methionine-sulfoxidized protein. Our results suggest that these events alter the transient α-synuclein intramolecular contacts, inducing a greater solvent exposure of its hydrophobic, aggregation-prone NAC domain, in full agreement with a recent experimental study on Ca²⁺ binding. Moreover, metal-binding residues directly participate in the long-range contacts that shield this domain and regulate α-synuclein aggregation. These results provide a molecular-level rationalization of the enhanced fibrillation experimentally observed in the presence of Cu²⁺ or Ca²⁺ and the oligomerization induced by methionine sulfoxidation.

Unraveling the NaCl Concentration Effect on the First Stages of α-Synuclein Aggregation

Intraneuronal aggregation of the intrinsically disordered protein α-synuclein is at the core of Parkinson's disease and related neurodegenerative disorders. Several reports show that the concentration of salts in the medium heavily affects its aggregation rate and fibril morphology, but a characterization of the individual monomeric conformations underlying these effects is still lacking. In this work, we have applied our α-synuclein-optimized coarse-grained molecular dynamics approach to decipher the structural features of the protein monomer under a range of NaCl concentrations (0.0-1.0 M). The results show that key intramolecular contacts between the terminal domains are lost at intermediate concentrations (leading to extended conformations likely to fibrillate), but recovered at high concentrations (leading to compact conformations likely to evolve toward amorphous aggregates). The pattern of direct interactions of the terminal α-synuclein domains with Na⁺ and Cl^- ions plays a key role in explaining this effect. Our results are consistent with a recent study reporting a fibrillation enhancement at moderate NaCl concentrations but an inhibition at higher concentrations. The present work will contribute to improving our understanding of the structural features of monomeric α-synuclein, determining its NaCl-induced fibrillation propensity and the molecular basis of synucleinopathies, necessary for the future development of disease-halting therapies.

Intrinsically Disordered Protein Ensembles Shape Evolutionary Rates Revealing Conformational Patterns

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs.

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.

Ensemble-Based Thermodynamics of the Fuzzy Binding between Intrinsically Disordered Proteins and Small-Molecule Ligands

In contrast to the "lock-and-key" model underlying the long-term success of structural biology and rational drug design, intrinsically disordered proteins (IDPs) exist in an ensemble of highly heterogeneous conformations even after binding with small-molecule ligands. It remains controversial how to characterize the thermodynamics of such fuzzy interactions. Here, we derive an ensemble-based thermodynamic framework to analyze the apparent affinity between IDPs and ligands. It is shown that the apparent affinity is related to the interaction free energy between the individual conformation and ligand in a way similar to Jarzynski's equality in nonequilibrium statistics. The oncoprotein c-Myc is adopted as an example to demonstrate the related properties, for example, the distribution of conformation-ligand interaction free energy, the entropic contribution from the ensemble, the conformation shift under ligand binding, and how to control the error under a limited number of sampled conformations.

Software Requirements for the Analysis and Interpretation of Native Ion Mobility Mass Spectrometry Data

The past few years have seen a dramatic increase in applications of native mass and ion mobility spectrometry, especially for the study of proteins and protein complexes. This increase has been catalyzed by the availability of commercial instrumentation capable of carrying out such analyses. As in most fields, however, the software to process the data generated from new instrumentation lags behind. Recently, a number of research groups have started addressing this by developing software, but further improvements are still required in order to realize the full potential of the data sets generated. In this perspective, we describe practical aspects as well as challenges in processing native mass spectrometry (MS) and ion mobility-MS data sets and provide a brief overview of currently available tools. We then set out our vision of future developments that would bring the community together and lead to the development of a common platform to expedite future computational developments, provide standardized processing approaches, and serve as a location for the deposition of data for this emerging field. This perspective has been written by members of the European Cooperation in Science and Technology Action on Native MS and Related Methods for Structural Biology (EU COST Action BM1403) as an introduction to the software tools available in this area. It is intended to serve as an overview for newcomers and to stimulate discussions in the community on further developments in this field, rather than being an in-depth review. Our complementary perspective (http://dx.doi.org/10.1021/acs.analchem.9b05791) focuses on computational approaches used in this field.

Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet

Intrinsically-disordered protein (IDP) characterization was an amazing change of paradigm in our classical sequence-structure-function theory. Moreover, IDPs are over-represented in major disease pathways and are now often targeted using small molecules for therapeutic purposes. This has had created a complex continuum from order-that encompasses rigid and flexible regions-to disorder regions; the latter being not accessible through classical crystallographic methodologies. In X-ray structures, the notion of order is dictated by access to resolved atom positions, providing rigidity and flexibility information with low and high experimental B-factors, while disorder is associated with the missing (non-resolved) residues. Nonetheless, some rigid regions can be found in disorder regions. Using ensembles of IDPs, their local conformations were analyzed in the light of a structural alphabet. An entropy index derived from this structural alphabet allowed us to propose a continuum of states from rigidity to flexibility and finally disorder. In this study, the analysis was extended to comparing these results to disorder predictions, underlying a limited correlation, and so opening new ideas to characterize and predict disorder.

Pseudo-Improper-Dihedral Model for Intrinsically Disordered Proteins

We present a new coarse-grained C_α-based protein model with a nonradial multibody pseudo-improper-dihedral potential that is transferable, time-independent, and suitable for molecular dynamics. It captures the nature of backbone and side-chain interactions between amino acid residues by adapting a simple improper dihedral term for a one-bead-per-residue model. It is parameterized for intrinsically disordered proteins and applicable to simulations of such proteins and their assemblies on millisecond time scales.

Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R _h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

Distance-Based Metrics for Comparing Conformational Ensembles of Intrinsically Disordered Proteins

Intrinsically disordered proteins are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons because their conformational diversity precludes optimal superimposition of the atomic coordinates necessary for deriving common similarity measures such as the root mean-square deviation of these coordinates. Here, we introduce superimposition-free metrics that are based on computing matrices of the Cα-Cα distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the root mean-square difference between the medians of the Cα-Cα distance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of intrinsically disordered proteins derived using experimental restraints or by molecular simulations and for proteins containing both structured and disordered regions.

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.

Data set of intrinsically disordered proteins analysed at a local protein conformation level

Intrinsic Disorder Proteins (IDPs) have become a hot topic since their characterisation in the 90s. The data presented in this article are related to our research entitled "A structural entropy index to analyse local conformations in Intrinsically Disordered Proteins" published in Journal of Structural Biology [1]. In this study, we quantified, for the first time, continuum from rigidity to flexibility and finally disorder. Non-disordered regions were also highlighted in the ensemble of disordered proteins. This work was done using the Protein Ensemble Database (PED), which is a useful database collecting series of protein structures considered as IDPs. The data set consists of a collection of cleaned protein files in classical pdb format that can be readily used as an input with most automatic analysis software. The accompanying data include the coding of all structural information in terms of a structural alphabet, namely Protein Blocks (PBs). An entropy index derived from PBs that allows apprehending the continuum between protein rigidity to flexibility to disorder is included, with information from secondary structure assignment, protein accessibility and prediction of disorder from the sequences. The data may be used for further structural bioinformatics studies of IDPs. It can also be used as a benchmark for evaluating disorder prediction methods.

New technologies to analyse protein function: an intrinsic disorder perspective

Functions of intrinsically disordered proteins do not require structure. Such structure-independent functionality has melted away the classic rigid "lock and key" representation of structure-function relationships in proteins, opening a new page in protein science, where molten keys operate on melted locks and where conformational flexibility and intrinsic disorder, structural plasticity and extreme malleability, multifunctionality and binding promiscuity represent a new-fangled reality. Analysis and understanding of this new reality require novel tools, and some of the techniques elaborated for the examination of intrinsically disordered protein functions are outlined in this review.

The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.

Expression and Purification of an Intrinsically Disordered Protein

Intrinsically disordered proteins (IDPs) describe a group of proteins that do not have a regular tertiary structure and typically have very little ordered secondary structure. Despite not following the biochemical dogma of "structure determines function" and "function determines structure," IDPs have been identified as having numerous biological functions. We describe here the steps to express and purify the intrinsically disordered stress response protein, Late embryogenesis abundant protein 3-2 from Arabidopsis thaliana (AtLEA 3-2), with ¹⁵N and ¹³C isotopes in E. coli, although the protocol can be adapted for any IDP with or without isotopic labeling. The atlea 3-2 gene has been cloned into the pET-SUMO vector that in addition to the SUMO portion encodes an N-terminal hexahistidine sequence (His-tag). This vector allows for the SUMO-AtLEA 3-2 fusion protein to be purified using Ni-affinity chromatography and, through the use of ubiquitin-like-specific protease 1 (Ulp1, a SUMO protease), results in an AtLEA 3-2 with a native N-terminus. We also describe the expression and purification of Ulp1 itself.

Computing, Analyzing, and Comparing the Radius of Gyration and Hydrodynamic Radius in Conformational Ensembles of Intrinsically Disordered Proteins

The level of compaction of an intrinsically disordered protein may affect both its physical and biological properties, and can be probed via different types of biophysical experiments. Small-angle X-ray scattering (SAXS) probe the radius of gyration (R_g) whereas pulsed-field-gradient nuclear magnetic resonance (NMR) diffusion, fluorescence correlation spectroscopy, and dynamic light scattering experiments can be used to determine the hydrodynamic radius (R_h). Here we show how to calculate R_g and R_h from a computationally generated conformational ensemble of an intrinsically disordered protein. We further describe how to use a Bayesian/Maximum Entropy procedure to integrate data from SAXS and NMR diffusion experiments, so as to derive conformational ensembles in agreement with those experiments.

Modulation of p53 Transactivation Domain Conformations by Ligand Binding and Cancer-Associated Mutations

Intrinsically disordered proteins (IDPs) are important functional proteins, and their deregulation are linked to numerous human diseases including cancers. Understanding how disease-associated mutations or drug molecules can perturb the sequence-disordered ensemble-function-disease relationship of IDPs remains challenging, because it requires detailed characterization of the heterogeneous structural ensembles of IDPs. In this work, we combine the latest atomistic force field a99SB-disp, enhanced sampling technique replica exchange with solute tempering, and GPU-accelerated molecular dynamics simulations to investigate how four cancer-associated mutations, K24N, N29K/N30D, D49Y, and W53G, and binding of an anti-cancer molecule, epigallocatechin gallate (EGCG), modulate the disordered ensemble of the transactivation domain (TAD) of tumor suppressor p53. Through extensive sampling, in excess of 1.0 μs per replica, well-converged structural ensembles of wild-type and mutant p53-TAD as well as WT p53-TAD in the presence of EGCG were generated. The results reveal that mutants could induce local structural changes and affect secondary structural properties. Interestingly, both EGCG binding and N29K/N30D could also induce long-range structural reorganizations and lead to more compact structures that could shield key binding sites of p53-TAD regulators. Further analysis reveals that the effects of EGCG binding are mainly achieved through nonspecific interactions. These observations are generally consistent with on-going NMR studies and binding assays. Our studies suggest that induced conformational collapse of IDPs may be a general mechanism for shielding functional sites, thus inhibiting recognition of their targets. The current study also demonstrates that atomistic simulations provide a viable approach for studying the sequence-disordered ensemble-function-disease relationships of IDPs and developing new drug design strategies targeting regulatory IDPs.

Dynamic conformational flexibility and molecular interactions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are highly flexible and undergo disorder to order transition upon binding. They are highly abundant in human proteomes and play critical roles in cell signaling and regulatory processes. This review mainly focuses on the dynamics of disordered proteins including their conformational heterogeneity, protein-protein interactions, and the phase transition of biomolecular condensates that are central to various biological functions. Besides, the role of RNA-mediated chaperones in protein folding and stability of IDPs were also discussed. Finally, we explored the dynamic binding interface of IDPs as novel therapeutic targets and the effect of small molecules on their interactions.

Extensive tests and evaluation of the CHARMM36IDPSFF force field for intrinsically disordered proteins and folded proteins

Intrinsically disordered proteins (IDPs) have received increasing attention in recent studies due to their structural heterogeneity and critical biological functions. To fully understand the structural properties and determine accurate ensembles of IDPs, molecular dynamics (MD) simulation was widely used to sample diverse conformations and reveal the structural dynamics. However, the classical state-of-the-art force fields perform well for folded proteins while being unsatisfactory for the simulations of disordered proteins reported in many previous studies. Thus, improved force fields were developed to precisely describe both folded proteins and disordered proteins. Preliminary tests show that our newly developed CHARMM36IDPSFF (C36IDPSFF) force field can well reproduce the experimental observables of several disordered proteins, but more tests on different types of proteins are needed to further evaluate the performance of C36IDPSFF. Here, we extensively simulate short peptides, disordered proteins, and fast-folding proteins as well as folded proteins, and compare the simulated results with the experimental observables. The simulation results show that C36IDPSFF could substantially reproduce the experimental observables for most of the tested proteins but some limitations are also found in the radius of gyration of large disordered proteins and the stability of fast-folding proteins. This force field will facilitate large scale studies of protein structural dynamics and functions using MD simulations.

Interaction between Copper Chaperone Atox1 and Parkinson's Disease Protein α-Synuclein Includes Metal-Binding Sites and Occurs in Living Cells

Alterations in copper ion homeostasis appear coupled to neurodegenerative disorders, but mechanisms are unknown. The cytoplasmic copper chaperone Atox1 was recently found to inhibit amyloid formation in vitro of α-synuclein, the amyloidogenic protein in Parkinson's disease. As α-synuclein may have copper-dependent functions, and free copper ions promote α-synuclein amyloid formation, it is important to characterize the Atox1 interaction with α-synuclein on a molecular level. Here we applied solution-state nuclear magnetic resonance spectroscopy, with isotopically labeled α-synuclein and Atox1, to define interaction regions in both proteins. The α-synuclein interaction interface includes the whole N-terminal part up to Gln24; in Atox1, residues around the copper-binding cysteines (positions 11-16) are mostly perturbed, but additional effects are also found for residues elsewhere in both proteins. Because α-synuclein is N-terminally acetylated in vivo, we established that Atox1 also inhibits amyloid formation of this variant in vitro, and proximity ligation in human cell lines demonstrated α-synuclein-Atox1 interactions in situ. Thus, this interaction may provide the direct link between copper homeostasis and amyloid formation in vivo.

Water hydrogen uptake in biomolecules detected via nuclear magnetic phosphorescence

We introduce a new symmetry-based method for structural investigations of areas surrounding water-exchanging hydrogens in biomolecules by liquid-state nuclear magnetic resonance spectroscopy. Native structures of peptides and proteins can be solved by NMR with fair resolution, with the notable exception of labile hydrogen sites. The reason why biomolecular structures often remain elusive around exchangeable protons is that the dynamics of their exchange with the solvent hampers the observation of their signals. The new spectroscopic method we report allows to locate water-originating hydrogens in peptides and proteins via their effect on nuclear magnetic transitions similar to electronic phosphorescence, long-lived coherences. The sign of long-lived coherences excited in coupled protons can be switched by the experimenter. The different effect of water-exchanging hydrogens on long-lived coherences with opposed signs allows to pinpoint the position of these labile hydrogen atoms in the molecular framework of peptides and proteins.

An intrinsically disordered proteins community for ELIXIR

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled “An intrinsically disordered protein user community proposal for ELIXIR” held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders.

Using Small-Angle Scattering Data and Parametric Machine Learning to Optimize Force Field Parameters for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10–15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.