Finding Our Way in the Dark Proteome

A curated rotamer library for common post-translational modifications of proteins

MOTIVATION

Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However, much of protein function is modulated beyond the translated sequence through the introduction of post-translational modifications (PTMs).

RESULTS

In this work, we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro, Rosetta, and AlphaFold3 in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions.

AVAILABILITY AND IMPLEMENTATION

The codes for dihedral angle computations and library creation are available at https://github.com/THGLab/ptm_sc.git.

A Curated Rotamer Library for Common Post-Translational Modifications of Proteins

Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However much of protein function is modulated beyond the translated sequence through thFiguree introduction of post-translational modifications (PTMs). In this work we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro and Rosetta in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions.

Molecular simulations integrated with experiments for probing the interaction dynamics and binding mechanisms of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) exploit their plasticity to deploy a rich panoply of soft interactions and binding phenomena. Advances in tailoring molecular simulations for IDPs combined with experimental cross-validation offer an atomistic view of the mechanisms that control IDP binding, function, and dysfunction. The emerging theme is that unbound IDPs autonomously form transient local structures and self-interactions that determine their binding behavior. Recent results have shed light on whether and how IDPs fold, stay disordered or drive condensation upon binding; how they achieve binding specificity and select among competing partners. The disorder-binding paradigm is now being proactively used by researchers to target IDPs for rational drug design and engineer molecular responsive elements for biosensing applications.

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.

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

The Role of Transient Intermediate Structures in the Unfolding of the Trp-Cage Fast-Folding Protein: Generating Ensembles from Time-Resolved X-ray Solution Scattering with Genetic Algorithms

The Trp-cage miniprotein is one of the smallest systems to exhibit a stable secondary structure and fast-folding dynamics, serving as an apt model system to study transient intermediates with both experimental and computational analyses. Previous spectroscopic characterizations that have been done on Trp-cage have inferred a single stable intermediate on a pathway from folded to unfolded basins. We aim to bridge the understanding of Trp-cage structural folding dynamics on microsecond-time scales, by utilizing time-resolved X-ray solution scattering to probe the temperature-induced unfolding pathway. Our results indicate the formation of a conformationally extended intermediate on the time scale of 1 μs, which undergoes complete unfolding within 5 μs. We further investigated the atomistic structural details of the unfolding pathway using a genetic algorithm to generate ensemble model fits to the scattering profiles. This analysis paves the way for direct benchmarking of theoretical models of protein folding ensembles produced with molecular dynamics simulations.

NMR insights into dynamic, multivalent interactions of intrinsically disordered regions: from discrete complexes to condensates

The spatial and temporal organization of interactions between proteins underlie the regulation of most cellular processes. The requirement for such interactions to be specific predisposes a view that protein-protein interactions are relatively static and are formed through the stable complementarity of the interacting partners. A growing body of reports indicate, however, that many interactions lead to fuzzy complexes with an ensemble of conformations in dynamic exchange accounting for the observed binding. Here, we discuss how NMR has facilitated the characterization of these discrete, dynamic complexes and how such characterization has aided the understanding of dynamic, condensed phases of phase-separating proteins with exchanging multivalent interactions.

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

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

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

Protein Dynamics to Define and Refine Disordered Protein Ensembles

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R₂ relaxation rates and Lipari-Szabo order parameters (S² values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

β-Synuclein: An Enigmatic Protein with Diverse Functionality

α-Synuclein (αS) is a small, unstructured, presynaptic protein expressed in the brain. Its aggregated form is a major component of Lewy bodies, the large proteinaceous deposits in Parkinson's disease. The closely related protein, β-Synuclein (βS), is co-expressed with αS. In vitro, βS acts as a molecular chaperone to inhibit αS aggregation. As a result of this assignation, βS has been largely understudied in comparison to αS. However, recent reports suggest that βS promotes neurotoxicity, implying that βS is involved in other cellular pathways with functions independent of αS. Here, we review the current literature pertaining to human βS in order to understand better the role of βS in homeostasis and pathology. Firstly, the structure of βS is discussed. Secondly, the ability of βS to (i) act as a molecular chaperone; (ii) regulate synaptic function, lipid binding, and the nigrostriatal dopaminergic system; (iii) mediate apoptosis; (iv) participate in protein degradation pathways; (v) modulate intracellular metal levels; and (vi) promote cellular toxicity and protein aggregation is explored. Thirdly, the P123H and V70M mutations of βS, which are associated with dementia with Lewy bodies, are discussed. Finally, the importance of post-translational modifications on the structure and function of βS is reviewed. Overall, it is concluded that βS has both synergistic and antagonistic interactions with αS, but it may also possess important cellular functions independent of αS.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

Pöllänen M, Johansson A, Permi P, Ihalin R. Decreased temperature increases the expression of a disordered bacterial late embryogenesis abundant (LEA) protein that enhances natural transformation

Late embryogenesis abundant (LEA) proteins are important players in the management of responses to stressful conditions, such as drought, high salinity, and changes in temperature. Many LEA proteins do not have defined three-dimensional structures, so they are intrinsically disordered proteins (IDPs) and are often highly hydrophilic. Although LEA-like sequences have been identified in bacterial genomes, the functions of bacterial LEA proteins have been studied only recently. Sequence analysis of outer membrane interleukin receptor I (BilRI) from the oral pathogen Aggregatibacter actinomycetemcomitans indicated that it shared sequence similarity with group 3/3b/4 LEA proteins. Comprehensive nuclearcgq magnetic resonance (NMR) studies confirmed its IDP nature, and expression studies in A. actinomycetemcomitans harboring a red fluorescence reporter protein-encoding gene revealed that bilRI promoter expression was increased at decreased temperatures. The amino acid backbone of BilRI did not stimulate either the production of reactive oxygen species from human leukocytes or the production of interleukin-6 from human macrophages. Moreover, BilRI-specific IgG antibodies could not be detected in the sera of A. actinomycetemcomitans culture-positive periodontitis patients. Since the bilRI gene is located near genes involved in natural competence (i.e., genes associated with the uptake of extracellular (eDNA) and its incorporation into the genome), we also investigated the role of BilRI in these events. Compared to wild-type cells, the ΔbilRI mutants showed a lower transformation efficiency, which indicates either a direct or indirect role in natural competence. In conclusion, A. actinomycetemcomitans might express BilRI, especially outside the host, to survive under stressful conditions and improve its transmission potential.

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales─picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure-function relationships require atomistic knowledge of the populated states in the ensemble. In this Perspective, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.

Thermodynamic Models for Assembly of Intrinsically Disordered Protein Hubs with Multiple Interaction Partners

Prevalent in diverse protein interactomes, intrinsically disordered proteins or regions (IDPs or IDRs) often drive assembly of higher-order macromolecular complexes, using multiple target-binding motifs. Such IDP hubs are suggested to process various cellular signals and coordinate relevant biological processes. However, the mechanism of assembly and functional regulation of IDP hubs remains elusive due to the challenges in dissecting their intricate protein-protein interaction networks. Here we present basic thermodynamic models for the assembly of simple IDP hubs with multiple target proteins, constructing partition functions from fundamental binding parameters. We combined these basic functions to develop advanced thermodynamic models to analyze the assembly of the Nup153 hubs interacting with multiple karyopherin β1 (Kap) molecules, critical components of nucleocytoplasmic transport. The thermodynamic analysis revealed a complex organization of the Kap binding sites within the C-terminal IDR of Nup153 including a high-affinity 1:1 interaction site and a series of low-affinity sites for binding of multiple Kaps with negative cooperativity. The negative cooperativity arises from the overlapping nature of the low-affinity sites where Kap occupies multiple dipeptide motifs. The quantitative dissection culminated in construction of the Nup153 hub ensemble, elucidating how distribution among various Kap-bound states is modulated by Kap concentration and competing nuclear proteins. In particular, the Kap occupancy of the IDR can be fine-tuned by varying the location of competition within the overlapping sites, suggesting coupling of specific nuclear processes to distinct transport activities. In general, our results demonstrate the feasibility and a potential mechanism for manifold regulation of IDP functions by diverse cellular signals.

Highly accurate protein structure prediction for the human proteome

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

Analysis of the dark proteome of Chandipura virus reveals maximum propensity for intrinsic disorder in phosphoprotein

Chandipura virus (CHPV, a member of the Rhabdoviridae family) is an emerging pathogen that causes rapidly progressing influenza-like illness and acute encephalitis often leading to coma and death of the human host. Given several CHPV outbreaks in Indian sub-continent, recurring sporadic cases, neurological manifestation, and high mortality rate of this infection, CHPV is gaining global attention. The 'dark proteome' includes the whole proteome with special emphasis on intrinsically disordered proteins (IDP) and IDP regions (IDPR), which are proteins or protein regions that lack unique (or ordered) three-dimensional structures within the cellular milieu. These proteins/regions, however, play a number of vital roles in various biological processes, such as cell cycle regulation, control of signaling pathways, etc. and, therefore, are implicated in many human diseases. IDPs and IPPRs are also abundantly found in many viral proteins enabling their multifunctional roles in the viral life cycles and their capability to highjack various host systems. The unknown abundance of IDP and IDPR in CHPV, therefore, prompted us to analyze the dark proteome of this virus. Our analysis revealed a varying degree of disorder in all five CHPV proteins, with the maximum level of intrinsic disorder propensity being found in Phosphoprotein (P). We have also shown the flexibility of P protein using extensive molecular dynamics simulations up to 500 ns (ns). Furthermore, our analysis also showed the abundant presence of the disorder-based binding regions (also known as molecular recognition features, MoRFs) in CHPV proteins. The identification of IDPs/IDPRs in CHPV proteins suggests that their disordered regions may function as potential interacting domains and may also serve as novel targets for disorder-based drug designs.

Progressive Phosphorylation Modulates the Self-Association of a Variably Modified Histone H3 Peptide

Protein phosphorylation is a key regulatory mechanism in eukaryotic cells. In the intrinsically disordered histone tails, phosphorylation is often a part of combinatorial post-translational modifications and an integral part of the “histone code” that regulates gene expression. Here, we study the association between two histone H3 tail peptides modified to different degrees, using fully atomistic molecular dynamics simulations. Assuming that the initial conformations are either α-helical or fully extended, we compare the propensity of the two peptides to associate with one another when both are unmodified, one modified and the other unmodified, or both modified. The simulations lead to the identification of distinct inter- and intramolecular interactions in the peptide dimer, highlighting a prominent role of a fine-tuned phosphorylation rheostat in peptide association. Progressive phosphorylation appears to modulate peptide charge, inducing strong and specific intermolecular interactions between the monomers, which do not result in the formation of amorphous or ordered aggregates, as documented by experimental evidence derived from Circular Dichroism and NMR spectroscopy. However, upon complete saturation of positive charges by phosphate groups, this effect is reversed: intramolecular interactions prevail and dimerization of zero-charge peptides is markedly reduced. These findings underscore the role of phosphorylation thresholds in the dynamics of intrinsically disordered proteins. Phosphorylation rheostats might account for the divergent effects of histone modifications on the modulation of chromatin structure.

Facile tethering of stable and unstable proteins for optical tweezers experiments

Single-molecule force spectroscopy with optical tweezers has emerged as a powerful tool for dissecting protein folding. The requirement to stably attach "molecular handles" to specific points in the protein of interest by preparative biochemical techniques is a limiting factor in applying this methodology, especially for large or unstable proteins that are difficult to produce and isolate. Here, we present a streamlined approach for creating stable and specific attachments using autocatalytic covalent tethering. The high specificity of coupling allowed us to tether ribosome-nascent chain complexes, demonstrating its suitability for investigating complex macromolecular assemblies. We combined this approach with cell-free protein synthesis, providing a facile means of preparing samples for single-molecule force spectroscopy. The workflow eliminates the need for biochemical protein purification during sample preparation for single-molecule measurements, making structurally unstable proteins amenable to investigation by this powerful single-molecule technique. We demonstrate the capabilities of this approach by carrying out pulling experiments with an unstructured domain of elongation factor G that had previously been refractory to analysis. Our approach expands the pool of proteins amenable to folding studies, which should help to reduce existing biases in the currently available set of protein folding models.

Flexibility of Oxidized and Reduced States of the Chloroplast Regulatory Protein CP12 in Isolation and in Cell Extracts

In the chloroplast, Calvin–Benson–Bassham enzymes are active in the reducing environment created in the light by electrons from the photosystems. In the dark, these enzymes are inhibited, mainly caused by oxidation of key regulatory cysteine residues. CP12 is a small protein that plays a role in this regulation with four cysteine residues that undergo a redox transition. Using amide-proton exchange with solvent, measured by nuclear magnetic resonance (NMR) and mass-spectrometry, we confirmed that reduced CP12 is intrinsically disordered. Using real-time NMR, we showed that the oxidation of the two disulfide bridges is simultaneous. In oxidized CP12, the C₂₃–C₃₁ pair is in a region that undergoes a conformational exchange in the NMR-intermediate timescale. The C₆₆–C₇₅ pair is in the C-terminus that folds into a stable helical turn. We confirmed that these structural states exist in a physiologically relevant environment: a cell extract from Chlamydomonas reinhardtii. Consistent with these structural equilibria, the reduction is slower for the C₆₆–C₇₅ pair than for the C₂₃–C₃₁ pair. The redox mid-potentials for the two cysteine pairs differ and are similar to those found for glyceraldehyde 3-phosphate dehydrogenase and phosphoribulokinase, consistent with the regulatory role of CP12.

Integrating an Enhanced Sampling Method and Small-Angle X-Ray Scattering to Study Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) have been paid more and more attention over the past decades because they are involved in a multitude of crucial biological functions. Despite their functional importance, IDPs are generally difficult to investigate because they are very flexible and lack stable structures. Computer simulation may serve as a useful tool in studying IDPs. With the development of computer software and hardware, computational methods, such as molecular dynamics (MD) simulations, are popularly used. However, there is a sampling problem in MD simulations. In this work, this issue is investigated using an IDP called unique long region 11 (UL11), which is the conserved outer tegument component from herpes simplex virus 1. After choosing a proper force field and water model that is suitable for simulating IDPs, integrative modeling by combining an enhanced sampling method and experimental data like small-angle X-ray scattering (SAXS) is utilized to efficiently sample the conformations of UL11. The simulation results are in good agreement with experimental data. This work may provide a general protocol to study structural ensembles of IDPs.

Configurational Entropy of Folded Proteins and Its Importance for Intrinsically Disordered Proteins

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction-the connection to configurational entropy-and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell

Intrinsic disorder can be found in all proteomes of all kingdoms of life and in viruses, being particularly prevalent in the eukaryotes. We conduct a comprehensive analysis of the intrinsic disorder in the human proteins while mapping them into 24 compartments of the human cell. In agreement with previous studies, we show that human proteins are significantly enriched in disorder relative to a generic protein set that represents the protein universe. In fact, the fraction of proteins with long disordered regions and the average protein-level disorder content in the human proteome are about 3 times higher than in the protein universe. Furthermore, levels of intrinsic disorder in the majority of human subcellular compartments significantly exceed the average disorder content in the protein universe. Relative to the overall amount of disorder in the human proteome, proteins localized in the nucleus and cytoskeleton have significantly increased amounts of disorder, measured by both high disorder content and presence of multiple long intrinsically disordered regions. We empirically demonstrate that, on average, human proteins are assigned to 2.3 subcellular compartments, with proteins localized to few subcellular compartments being more disordered than the proteins that are localized to many compartments. Functionally, the disordered proteins localized in the most disorder-enriched subcellular compartments are primarily responsible for interactions with nucleic acids and protein partners. This is the first-time disorder is comprehensively mapped into the human cell. Our observations add a missing piece to the puzzle of functional disorder and its organization inside the cell.

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.

Conformational Preferences of an Intrinsically Disordered Protein Domain: A Case Study for Modern Force Fields

Molecular simulations of intrinsically disordered proteins (IDPs) are challenging because they require sampling a very large number of relevant conformations, corresponding to a multitude of shallow minima in a flat free energy landscape. However, in the presence of a binding partner, the free energy landscape of an IDP can be dominated by few deep minima. This characteristic imposes high demands on the accuracy of the force field used to describe the molecular interactions. Here, as a model system for an IDP that is unstructured in solution but folds upon binding to a structured interaction partner, the transactivation domain of c-Myb was studied both in the unbound (free) form and when bound to the KIX domain. Six modern biomolecular force fields were systematically tested and compared in terms of their ability to describe the structural ensemble of the IDP. The protein force field/water model combinations included in this study are AMBER ff99SB-disp with its corresponding water model that was derived from TIP4P-D, CHARMM36m with TIP3P, ff15ipq with SPC/E_b, ff99SB*-ILDNP with TIP3P and TIP4P-D, and FB15 with TIP3P-FB water. Comparing the results from REST2-enhanced sampling simulations with experimental CD spectra and secondary chemical shifts reveals that the ff99SB-disp force field can realistically capture the broad and mildly helical structural ensemble of free c-Myb. The structural ensembles yielded by CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 are also mildly helical; however, each of these force fields can be assigned a specific subset of c-Myb residues for which the simulations could not reproduce the experimental secondary chemical shifts. In addition, microsecond-timescale MD simulations of the KIX/c-Myb complex show that most force fields used preserve a stable helix fold of c-Myb in the complex. Still, all force fields predict a KIX/c-Myb complex interface that differs slightly from the structures provided by NMR because several NOE-derived distances between KIX and c-Myb were exceeded in the simulations. Taken together, the ff99SB-disp force field in the first place but also CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 can be suitable choices for future simulation studies of the coupled folding and binding mechanism of the KIX/c-Myb complex and potentially also other IDPs.

Towards an understanding of the role of intrinsic protein disorder on plant adaptation to environmental challenges

Intrinsic protein disorder is an interesting structural feature where fully functional proteins lack a three-dimensional structure in solution. In this work, we estimated the relative content of intrinsic protein disorder in 96 plant proteomes including monocots and eudicots. In this analysis, we found variation in the relative abundance of intrinsic protein disorder among these major clades; the relative level of disorder is higher in monocots than eudicots. In turn, there is an inverse relationship between the degree of intrinsic protein disorder and protein length, with smaller proteins being more disordered. The relative abundance of amino acids depends on intrinsic disorder and also varies among clades. Within the nucleus, intrinsically disordered proteins are more abundant than ordered proteins. Intrinsically disordered proteins are specialized in regulatory functions, nucleic acid binding, RNA processing, and in response to environmental stimuli. The implications of this on plants' responses to their environment are discussed.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

Conformational editing of intrinsically disordered protein by α-methylation

Intrinsically disordered proteins (IDPs) constitute a large portion of “Dark Proteome” – difficult to characterize or yet to be discovered protein structures. Here we used conformationally constrained α-methylated amino acids to bias the conformational ensemble in the free unstructured activation domain of transcriptional coactivator ACTR. Different sites and patterns of substitutions were enabled by chemical protein synthesis and led to distinct populations of α-helices. A specific substitution pattern resulted in a substantially higher binding affinity to nuclear coactivator binding domain (NCBD) of CREB-binding protein, a natural binding partner of ACTR. The first X-ray structure of the modified ACTR domain - NCBD complex visualized a unique conformation of ACTR and confirmed that the key α-methylated amino acids are localized within α-helices in the bound state. This study demonstrates a strategy for characterization of individual conformational states of IDPs.

Control of protein conformation was achieved for intrinsically disordered protein by incorporation of α-methylated amino acids.

Interactions between the Intrinsically Disordered Regions of hnRNP-A2 and TDP-43 Accelerate TDP-43's Conformational Transition

Most biological functions involve protein-protein interactions. Our understanding of these interactions is based mainly on those of structured proteins, because encounters between intrinsically disordered proteins (IDPs) or proteins with intrinsically disordered regions (IDRs) are much less studied, regardless of the fact that more than half eukaryotic proteins contain IDRs. RNA-binding proteins (RBPs) are a large family whose members almost all have IDRs in addition to RNA binding domains. These IDRs, having low sequence similarity, interact, but structural details on these interactions are still lacking. Here, using the IDRs of two RBPs (hnRNA-A2 and TDP-43) as a model, we demonstrate that the rate at which TDP-43's IDR undergoes the neurodegenerative disease related α-helix-to-β-sheet transition increases in relation to the amount of hnRNP-A2's IDR that is present. There are more than 1500 RBPs in human cells and most of them have IDRs. RBPs often join the same complexes to regulate genes. In addition to the structured RNA-recognition motifs, our study demonstrates a general mechanism through which RBPs may regulate each other's functions through their IDRs.

Discriminating changes in protein structure using tyrosine conjugation

Chemical modification of proteins has been crucial in engineering protein-based therapies, targeted biopharmaceutics, molecular probes, and biomaterials. Here, we explore the use of a conjugation-based approach to sense alternative conformational states in proteins. Tyrosine has both hydrophobic and hydrophilic qualities, thus allowing it to be positioned at protein surfaces, or binding interfaces, or to be buried within a protein. Tyrosine can be conjugated with 4-phenyl-3H-1,2,4-triazole-3,5(4H)-dione (PTAD). We hypothesized that individual protein conformations could be distinguished by labeling tyrosine residues in the protein with PTAD. We conjugated tyrosine residues in a well-folded protein, bovine serum albumin (BSA), and quantified labeled tyrosine with liquid chromatography with tandem mass spectrometry. We applied this approach to alternative conformations of BSA produced in the presence of urea. The amount of PTAD labeling was found to relate to the depth of each tyrosine relative to the protein surface. This study demonstrates a new use of tyrosine conjugation using PTAD as an analytic tool able to distinguish the conformational states of a protein.

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.

Structural and functional analysis of "non-smelly" proteins

Cysteine and aromatic residues are major structure-promoting residues. We assessed the abundance, structural coverage, and functional characteristics of the "non-smelly" proteins, i.e., proteins that do not contain cysteine residues (C-depleted) or cysteine and aromatic residues (CFYWH-depleted), across 817 proteomes from all domains of life. The analysis revealed that although these proteomes contained significant levels of the C-depleted proteins, with prokaryotes being significantly more enriched in such proteins than eukaryotes, the CFYWH-depleted proteins were relatively rare, accounting for about 0.05% of proteomes. Furthermore, CFYWH-depleted proteins were virtually never found in PDB. Depletion in cysteine and in aromatic residues was associated with the substantially increased intrinsic disorder levels across all domains of life. Archaeal and eukaryotic organisms with higher levels of the C-depleted proteins were shown to have higher levels of the intrinsic disorder and lower levels of structural coverage. We also showed that the "non-smelly" proteins typically did not independently fold into monomeric structures, and instead, they fold by interacting with nucleic acids as constituents of the ribosome and nucleosome complexes. They were shown to be involved in translation, transcription, nucleosome assembly, transmembrane transport, and protein folding functions, all of which are known to be associated with the intrinsic disorder. Our data suggested that, in general, structure of monomeric proteins is crucially dependent on the presence of cysteine and aromatic residues.

DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome.

Advances in studying protein disorder with solid-state NMR

Solution NMR is a key tool to study intrinsically disordered proteins (IDPs), whose importance for biological function is widely accepted. However, disordered proteins are not limited to solution and are also found in non-soluble systems such as fibrils and membrane proteins. In this Trends article, I will discuss how solid-state NMR can be used to study disorder in non-soluble proteins. Techniques based on dipolar couplings can study static protein disorder which either occurs naturally as e.g. in spider silk or can be induced by freeze trapping IDPs or unfolded proteins. In this case, structural ensembles are directly reflected by a static distribution of dihedral angels that can be determined by the distribution of chemical shifts or other methods. Techniques based on J-couplings can detect dynamic protein disorder under MAS. In this case, only average chemical shifts are measured but disorder can be characterized with a variety of data including secondary chemical shifts, relaxation rates, paramagnetic relaxation enhancements, or residual dipolar couplings. I describe both technical aspects and examples of solid-state NMR on protein disorder and end the article with a discussion of challenges and opportunities of this emerging field.

Torches, Candles, Lamps, Lanterns, Flashlights, Spotlights, Night Vision Goggles… You Need Them All to See in Darkness

Articles assembled in the second part of this Special Issue describe some experimental and computational approaches for the structural and functional characterization of intrinsically disordered proteins. Since these tools represent specialized gear for the focused analysis of various aspects of dark proteome, they can be viewed as torches, candles, lamps, lanterns, flashlights, spotlights, night vision goggles, and other means needed to see in darkness.

HK97 gp74 Possesses an α-Helical Insertion in the ββα Fold That Affects Its Metal Binding, cos Site Digestion, and In Vivo Activities

The last gene in the genome of the bacteriophage HK97 encodes gp74, an HNH endonuclease. HNH motifs contain two conserved His residues and an invariant Asn residue, and they adopt a ββα structure. gp74 is essential for phage head morphogenesis, likely because gp74 enhances the specific endonuclease activity of the HK97 terminase complex. Notably, the ability of gp74 to enhance the terminase-mediated cleavage of the phage cos site requires an intact HNH motif in gp74. Mutation of H82, the conserved metal-binding His residue in the HNH motif, to Ala abrogates gp74-mediated stimulation of terminase activity. Here, we present nuclear magnetic resonance (NMR) studies demonstrating that gp74 contains an α-helical insertion in the Ω-loop, which connects the two β-strands of the ββα fold, and a disordered C-terminal tail. NMR data indicate that the Ω-loop insert makes contacts to the ββα fold and influences the ability of gp74 to bind divalent metal ions. Further, the Ω-loop insert and C-terminal tail contribute to gp74-mediated DNA digestion and to gp74 activity in phage morphogenesis. The data presented here enrich our molecular-level understanding of how HNH endonucleases enhance terminase-mediated digestion of the cos site and contribute to the phage replication cycle.IMPORTANCE This study demonstrates that residues outside the canonical ββα fold, namely, the Ω-loop α-helical insert and a disordered C-terminal tail, regulate the activity of the HNH endonuclease gp74. The increased divalent metal ion binding when the Ω-loop insert is removed compared to reduced cos site digestion and phage formation indicates that the Ω-loop insert plays multiple regulatory roles. The data presented here provide insights into the molecular basis of the involvement of HNH proteins in phage DNA packing.

Disorder-Order Transitions in Conformational Selection of a Peptide by Ebola Virus Nucleoprotein

This study presents parallel-tempering lattice Monte Carlo simulations based on the side-chain-only (SICHO) model for calculating the conformational landscape of a 28-residue intrinsically disordered peptide extracted from the Ebola virus protein VP35. The central issue is the applicability of the SICHO potential energy function and in general coarse-grained (CG) representations of intermediate resolution for modeling large-scale conformational heterogeneity that includes both folded and unstructured peptide states. Crystallographic data shows that the peptide folds in a 4₁₀-helix-turn-3₁₀-helix topology upon complex formation with the Ebola virus nucleoprotein, whereas in isolation, the peptide transitions to a disordered conformational ensemble as observed in circular dichroism experiments. The simulation reveals a potential of mean force that displays conformational diversity along the helix-forming reaction coordinate consistent with disorder-order transitions, yet unexpectedly the bound topology is poorly sampled, and a population shift to an unstructured state incurs a significant free-energy penalty. Applying an elastic network interpolation model suggests a hybrid binding mechanism through conformational selection of the 4₁₀-helix followed by an induced fit of the 3₁₀-helix. A comparison of the CG model with previously reported all-atom CHARMM-based simulations highlights a lattice-based approach that is computationally fast and with the correct parameterization yields good resolution to modeling conformational plasticity.

Understanding the penetrance of intrinsic protein disorder in rotavirus proteome

Rotavirus is a major cause of severe acute gastroenteritis in the infants and young children. The past decade has evidenced the role of intrinsically disordered proteins/regions (IDPs)/(IDPRs) in viral and other diseases. In general, (IDPs)/(IDPRs) are considered as dynamic conformational ensembles that devoid of a specific 3D structure, being associated with various important biological phenomena. Viruses utilize IDPs/IDPRs to survive in harsh environments, to evade the host immune system, and to highjack and manipulate host cellular proteins. The role of IDPs/IDPRs in Rotavirus biology and pathogenicity are not assessed so far, therefore, we have designed this study to deeply look at the penetrance of intrinsic disorder in rotavirus proteome consisting 12 proteins encoded by 11 segments of viral genome. Also, for all human rotaviral proteins, we have deciphered molecular recognition features (MoRFs), which are disorder based binding sites in proteins. Our study shows the wide spread of intrinsic disorder in several rotavirus proteins, primarily the nonstructural proteins NSP3, NSP4, and NSP5 that are involved in viral replication, translation, viroplasm formation and/or maturation. This study may serve as a primer for understanding the role of IDPs/MoRFs in rotavirus biology, design of alternative therapeutic strategies, and development of disorder-based drugs.

Intrinsically disordered regions regulate the activities of ATP binding cassette transporters

ATP binding cassette (ABC) proteins are a large family of membrane proteins present in all kingdoms of life. These multi-domain proteins are comprised, at minimum, of two membrane-spanning domains (MSD1, MSD2) and two cytosolic nucleotide binding domains (NBD1, NBD2). ATP binding and hydrolysis at the NBDs enables ABC proteins to actively transport solutes across membranes, regulate activities of other proteins, or function as channels. Like most eukaryotic membrane proteins, ABC proteins contain intrinsically disordered regions (IDRs). These conformationally dynamic regions in ABC proteins possess residual structure, are sites of phosphorylation, and mediate protein-protein interactions. Here, we review the role of IDRs in regulating ABC protein activity.

Probing the Proteomics Dark Regions by VAILase Cleavage at Aliphatic Amino Acids

Proteomics emerges from the protein identification to protein functional elucidation, which depends to a large extent on the characterization of protein sequences. However, a large part of proteome sequences remains unannotated due to the limitation in proteolytic digestion by golden standard protease trypsin. Herein, we demonstrated that a cyanobacterial protease VAILase could specifically cleave at the short-chain aliphatic amino acids valine, alanine, leucine, isoleucine and threonine with cleavage specificity about 92% in total for proteomic analysis. The unique features of VAILase cleavage facilitate the characterization of most proteins and exhibit high complementarity to trypsin, and 22% of the covered sequences by VAILase are unique. VAILase can greatly improve the coverages of sequences with abundant aliphatic residues that are usually dark regions in conventional proteomic analysis, such as the transmembrane regions within anion exchanger 1 and photosystem II.

DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server

Computational predictions of the intrinsic disorder and its functions are instrumental to facilitate annotation for the millions of unannotated proteins. However, access to these predictors is fragmented and requires substantial effort to find them and to collect and combine their results. The DEPICTER (DisorderEd PredictIon CenTER) server provides first-of-its-kind centralized access to 10 popular disorder and disorder function predictions that cover protein and nucleic acids binding, linkers, and moonlighting regions. It automates the prediction process, runs user-selected methods on the server side, visualizes the results, and outputs all predictions in a consistent and easy-to-parse format. DEPICTER also includes two accurate consensus predictors of disorder and disordered protein binding. Empirical tests on an independent (low similarity) benchmark dataset reveal that the computational tools included in DEPICTER generate accurate predictions that are significantly better than the results secured using sequence alignment. The DEPICTER server is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER/.

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

The sequence-structure-function paradigm of proteins has been revolutionized by the discovery of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In contrast to traditional ordered proteins, IDPs/IDRs are unstructured under physiological conditions. The absence of well-defined three-dimensional structures in the free state of IDPs/IDRs is fundamental to their function. Folding upon binding is an important mode of molecular recognition for IDPs/IDRs. While great efforts have been devoted to investigating the complex structures and binding kinetics and affinities, our knowledge on the binding mechanisms of IDPs/IDRs remains very limited. Here, we review recent advances on the binding mechanisms of IDPs/IDRs. The structures and kinetic parameters of IDPs/IDRs can vary greatly, and the binding mechanisms can be highly dependent on the structural properties of IDPs/IDRs. IDPs/IDRs can employ various combinations of conformational selection and induced fit in a binding process, which can be templated by the target and/or encoded by the IDP/IDR. Further studies should provide deeper insights into the molecular recognition of IDPs/IDRs and enable the rational design of IDP/IDR binding mechanisms in the future.

Biochemical Characterization of a Novel α/β-Hydrolase/FSH from the White Shrimp Litopenaeus vannamei

(1) Background: Lipases and esterases are important enzymes that share the α/β hydrolase fold. The activity and cellular localization are important characteristics to understand the role of such enzymes in an organism. (2) Methods: Bioinformatic and biochemical tools were used to describe a new α/β hydrolase from a Litopenaeus vannamei transcriptome (LvFHS for Family Serine Hydrolase). (3) Results: The enzyme was obtained by heterologous overexpression in Escherichia coli and showed hydrolytic activity towards short-chain lipid substrates and high affinity to long-chain lipid substrates. Anti-LvFHS antibodies were produced in rabbit that immunodetected the LvFSH enzyme in several shrimp tissues. (4) Conclusions: The protein obtained and analyzed was an α/β hydrolase with esterase and lipase-type activity towards long-chain substrates up to 12 carbons; its immunodetection in shrimp tissues suggests that it has an intracellular localization, and predicted roles in energy mobilization and signal transduction.

Accuracy of protein-level disorder predictions

Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.

Probing the Basis of α-Synuclein Aggregation by Comparing Simulations to Single-Molecule Experiments

Intrinsically disordered proteins often play an important role in protein aggregation. However, it is challenging to determine the structures and interactions that drive the early stages of aggregation because they are transient and obscured in a heterogeneous mixture of disordered states. Even computational methods are limited because the lack of ordered structure makes it difficult to ensure that the relevant conformations are sampled. We address these challenges by integrating atomistic simulations with high-resolution single-molecule measurements reported previously, using the measurements to help discern which parts of the disordered ensemble of structures in the simulations are most probable while using the simulations to identify residues and interactions that are important for oligomer stability. This approach was applied to α-synuclein, an intrinsically disordered protein that aggregates in the context of Parkinson's disease. We simulated single-molecule pulling experiments on dimers, the minimal oligomer, and compared them to force spectroscopy measurements. Force-extension curves were simulated starting from a set of 66 structures with substantial structured content selected from the ensemble of dimer structures generated at zero force via Monte Carlo simulations. The pattern of contour length changes as the structures unfolded through intermediate states was compared to the results from optical trapping measurements on the same dimer to discern likely structures occurring in the measurements. Simulated pulling curves were generally consistent with experimental data but with a larger number of transient intermediates. We identified an ensemble of β-rich dimer structures consistent with the experimental data from which dimer interfaces could be deduced. These results suggest specific druggable targets in the structural motifs of α-synuclein that may help prevent the earliest steps of oligomerization.

Structural and Aggregation Features of a Human κ-Casein Fragment with Antitumor and Cell-Penetrating Properties

Intrinsically disordered proteins play a central role in dynamic regulatory and assembly processes in the cell. Recently, a human κ-casein proteolytic fragment called lactaptin (8.6 kDa) was found to induce apoptosis of human breast adenocarcinoma MCF-7 and MDA-MB-231 cells with no cytotoxic activity toward normal cells. Earlier, we had designed some recombinant analogs of lactaptin and compared their biological activity. Among these analogs, RL2 has the highest antitumor activity, but the amino acid residues and secondary structures that are responsible for RL2's activity remain unclear. To elucidate the structure-activity relations of RL2, we studied the structural and aggregation features of this fairly large intrinsically disordered fragment of human milk κ-casein by a combination of physicochemical methods: NMR, paramagnetic relaxation enhancement (PRE), Electron Paramagnetic Resonance (EPR), circular dichroism, dynamic light scattering, atomic force microscopy, and a cytotoxic activity assay. It was found that in solution, RL2 exists as stand-alone monomeric particles and large aggregates. Whereas the disulfide-bonded homodimer turned out to be more prone to assembly into large aggregates, the monomer predominantly forms single particles. NMR relaxation analysis of spin-labeled RL2 showed that the RL2 N-terminal region, which is essential not only for multimerization of the peptide but also for its proapoptotic action on cancer cells, is more ordered than its C-terminal counterpart and contains a site with a propensity for α-helical secondary structure.