Biochemistry. Integrative structural biology

Empowering the molecular ruler techniques with unnatural base pair system to explore conformational dynamics of flaviviral RNAs

RNA's inherent flexibility and dynamics pose great challenges to characterize its structure and dynamics using conventional techniques including X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy. Three complementary molecular ruler techniques, the electron paramagnetic resonance (EPR) spectroscopy, X-ray scattering interferometry (XSI) and Förster resonance energy transfer (FRET) which measure intramolecular and intermolecular pair-wise distance distributions in the nanometer range in a solution, have become increasingly popular and been widely used to explore RNA structure and dynamics. The prerequisites for successful application of such techniques are to achieve site-specific labeling of RNAs with spin labels, fluorescent tags, or gold nanoparticles, respectively, which are however, challenging, especially to large RNAs (generally >200 nts). Here, we briefly review the basics of these molecular rulers, how the NaM-TPT3 unnatural base pair system empower them, and their applications to explore conformational dynamics of large RNAs, especially in the context of flavivirus RNA genome.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

Evaluation of a Commercial TIMS-Q-TOF Platform for Native Mass Spectrometry

Mass-spectrometry based assays in structural biology studies measure either intact or digested proteins. Typically, different mass spectrometers are dedicated for such measurements: those optimized for rapid analysis of peptides or those designed for high molecular weight analysis. A commercial trapped ion mobility-quadrupole-time-of-flight (TIMS-Q-TOF) platform is widely utilized for proteomics and metabolomics, with ion mobility providing a separation dimension in addition to liquid chromatography. The ability to perform high-quality native mass spectrometry of protein complexes, however, remains largely uninvestigated. Here, we evaluate a commercial TIMS-Q-TOF platform for analyzing noncovalent protein complexes by utilizing the instrument's full range of ion mobility, MS, and MS/MS (both in-source activation and collision cell CID) capabilities. The TIMS analyzer is able to be tuned gently to yield collision cross sections of native-like complexes comparable to those previously reported on various instrument platforms. In-source activation and collision cell CID were robust for both small and large complexes. TIMS-CID was performed on protein complexes streptavidin (53 kDa), avidin (68 kDa), and cholera toxin B (CTB, 58 kDa). Complexes pyruvate kinase (237 kDa) and GroEL (801 kDa) were beyond the trapping capabilities of the commercial TIMS analyzer, but TOF mass spectra could be acquired. The presented results indicate that the commercial TIMS-Q-TOF platform can be used for both omics and native mass spectrometry applications; however, modifications to the commercial RF drivers for both the TIMS analyzer and quadrupole (currently limited to m/z 3000) are necessary to mobility analyze protein complexes greater than about 60 kDa.

Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles

A substantial portion of various organisms' proteomes comprises intrinsically disordered proteins (IDPs) that lack a defined three-dimensional structure. These IDPs exhibit a diverse array of conformations, displaying remarkable spatiotemporal heterogeneity and exceptional conformational flexibility. Characterizing the structure or structural ensemble of IDPs presents significant conceptual and methodological challenges owing to the absence of a well-defined native structure. While databases such as the Protein Ensemble Database (PED) provide IDP ensembles obtained through a combination of experimental data and molecular modeling, the absence of reaction coordinates poses challenges in comprehensively understanding pertinent aspects of the system. In this study, we leverage the energy landscape visualization method (JCTC, 6482, 2019) to scrutinize four IDP ensembles sourced from PED. ELViM, a methodology that circumvents the need for a priori reaction coordinates, aids in analyzing the ensembles. The specific IDP ensembles investigated are as follows: two fragments of nucleoporin (NUL: 884-993 and NUS: 1313-1390), yeast sic 1 N-terminal (1-90), and the N-terminal SH3 domain of Drk (1-59). Utilizing ELViM enables the comprehensive validation of ensembles, facilitating the detection of potential inconsistencies in the sampling process. Additionally, it allows for identifying and characterizing the most prevalent conformations within an ensemble. Moreover, ELViM facilitates the comparative analysis of ensembles obtained under diverse conditions, thereby providing a powerful tool for investigating the functional mechanisms of IDPs.

Encoding prior knowledge in ensemble refinement

The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we propose how to set the exchange rate a priori to properly balance this trade-off. We focus on gentle ensemble refinement, where the difference between the potential energy surfaces of the reference and refined models is small on a thermal scale. By relating the variance of this energy difference to the Kullback-Leibler divergence between the respective Boltzmann distributions, one can encode prior knowledge about energy uncertainties, i.e., force-field errors, in the exchange rate. The energy uncertainty is defined in the space of observables and depends on their type and number and on the thermodynamic state. We highlight the relation of gentle refinement to free energy perturbation theory. A balanced encoding of prior knowledge increases the quality and transparency of ensemble refinement. Our findings extend to non-Boltzmann distributions, where the uncertainty in energy becomes an uncertainty in information.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Integrative spatiotemporal map of nucleocytoplasmic transport

The Nuclear Pore Complex (NPC) facilitates rapid and selective nucleocytoplasmic transport of molecules as large as ribosomal subunits and viral capsids. It is not clear how key emergent properties of this transport arise from the system components and their interactions. To address this question, we constructed an integrative coarse-grained Brownian dynamics model of transport through a single NPC, followed by coupling it with a kinetic model of Ran-dependent transport in an entire cell. The microscopic model parameters were fitted to reflect experimental data and theoretical information regarding the transport, without making any assumptions about its emergent properties. The resulting reductionist model is validated by reproducing several features of transport not used for its construction, such as the morphology of the central transporter, rates of passive and facilitated diffusion as a function of size and valency, in situ radial distributions of pre-ribosomal subunits, and active transport rates for viral capsids. The model suggests that the NPC functions essentially as a virtual gate whose flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to diffusion through the pore. Importantly, this core functionality is greatly enhanced by several key design features, including 'fuzzy' and transient interactions, multivalency, redundancy in the copy number of FG nucleoporins, exponential coupling of transport kinetics and thermodynamics in accordance with the transition state theory, and coupling to the energy-reliant RanGTP concentration gradient. These design features result in the robust and resilient rate and selectivity of transport for a wide array of cargo ranging from a few kilodaltons to megadaltons in size. By dissecting these features, our model provides a quantitative starting point for rationally modulating the transport system and its artificial mimics.

RNA structure determination: From 2D to 3D

RNA molecules serve a wide range of functions that are closely linked to their structures. The basic structural units of RNA consist of single- and double-stranded regions. In order to carry out advanced functions such as catalysis and ligand binding, certain types of RNAs can adopt higher-order structures. The analysis of RNA structures has progressed alongside advancements in structural biology techniques, but it comes with its own set of challenges and corresponding solutions. In this review, we will discuss recent advances in RNA structure analysis techniques, including structural probing methods, X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and small-angle X-ray scattering. Often, a combination of multiple techniques is employed for the integrated analysis of RNA structures. We also survey important RNA structures that have been recently determined using various techniques.

Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Modeling of flexible membrane-bound biomolecular complexes for solution small-angle scattering

Recent advances in protein expression protocols, sample handling, and experimental set up of small-angle scattering experiments have allowed users of the technique to structurally investigate biomolecules of growing complexity and structural disorder. Notable examples include intrinsically disordered proteins, multi-domain proteins and membrane proteins in suitable carrier systems. Here, we outline a modeling scheme for calculating the scattering profiles from such complex samples. This kind of modeling is necessary for structural information to be refined from the corresponding data. The scheme bases itself on a hybrid of classical form factor based modeling and the well-known spherical harmonics-based formulation of small-angle scattering amplitudes. Our framework can account for flexible domains alongside other structurally elaborate components of the molecular system in question. We demonstrate the utility of this modeling scheme through a recent example of a structural model of the growth hormone receptor membrane protein in a phospholipid bilayer nanodisc which is refined against experimental SAXS data. Additionally we investigate how the scattering profiles from the complex would appear under different scattering contrasts. For each contrast situation we discuss what structural information is contained and the related consequences for modeling of the data.

A special issue of Essays in Biochemistry on structural mass spectrometry

Mass spectrometry (MS) is now established as an analytical tool to interrogate the structure and dynamics of proteins and their assemblies. An array of MS-based technologies has been developed, with each providing unique information pertaining to protein structure, and forming the heart of integrative structural biology studies. This special issue includes a collection of review articles that discuss both established and emerging structural MS methodologies, along with examples of how these technologies are being deployed to interrogate protein structure and function. Combined, this collection highlights the immense potential of the structural MS toolkit in the study of molecular mechanisms underpinning cellular homeostasis and disease.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

Illuminating Intrinsically Disordered Proteins with Integrative Structural Biology

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

Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET

Pulsed electron-electron double resonance spectroscopy (PELDOR/DEER) and single-molecule Förster resonance energy transfer spectroscopy (smFRET) are frequently used to determine conformational changes, structural heterogeneity, and inter probe distances in biological macromolecules. They provide qualitative information that facilitates mechanistic understanding of biochemical processes and quantitative data for structural modelling. To provide a comprehensive comparison of the accuracy of PELDOR/DEER and smFRET, we use a library of double cysteine variants of four proteins that undergo large-scale conformational changes upon ligand binding. With either method, we use established standard experimental protocols and data analysis routines to determine inter-probe distances in the presence and absence of ligands. The results are compared to distance predictions from structural models. Despite an overall satisfying and similar distance accuracy, some inconsistencies are identified, which we attribute to the use of cryoprotectants for PELDOR/DEER and label-protein interactions for smFRET. This large-scale cross-validation of PELDOR/DEER and smFRET highlights the strengths, weaknesses, and synergies of these two important and complementary tools in integrative structural biology.

Conformational heterogeneity of UCAAUC RNA oligonucleotide from molecular dynamics simulations, SAXS, and NMR experiments

We describe the conformational ensemble of the single-stranded r(UCAAUC) oligonucleotide obtained using extensive molecular dynamics (MD) simulations and Rosetta's FARFAR2 algorithm. The conformations observed in MD consist of A-form-like structures and variations thereof. These structures are not present in the pool generated using FARFAR2. By comparing with available nuclear magnetic resonance (NMR) measurements, we show that the presence of both A-form-like and other extended conformations is necessary to quantitatively explain experimental data. To further validate our results, we measure solution X-ray scattering (SAXS) data on the RNA hexamer and find that simulations result in more compact structures than observed from these experiments. The integration of simulations with NMR via a maximum entropy approach shows that small modifications to the MD ensemble lead to an improved description of the conformational ensemble. Nevertheless, we identify persisting discrepancies in matching experimental SAXS data.

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

PrISM: Precision for Integrative Structural Models

MOTIVATION

A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.

RESULTS

We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models.

AVAILABILITY

PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Strategies for the Production of Molecular Animations

Molecular animations play an increasing role in scientific visualisation and science communication. They engage viewers through non-fictional, documentary type storytelling and aim at advancing the audience. Every scene of a molecular animation is to be designed to secure clarity. To achieve this, knowledge on design principles from various design fields is essential. The relevant principles help to draw attention, guide the eye, establish relationships, convey dynamics and/or trigger a reaction. The tools of general graphic design are used to compose a signature frame, those of cinematic storytelling and user interface design to choreograph the relative movement of characters and cameras. Clarity in a scientific visualisation is reached by simplification and abstraction where the choice of the adequate representation is of great importance. A large set of illustration styles is available to chose the appropriate detail level but they are constrained by the availability of experimental data. For a high-quality molecular animation, data from different sources can be integrated, even filling the structural gaps to show a complete picture of the native biological situation. For maintaining scientific authenticity it is good practice to mark use of artistic licence which ensures transparency and accountability. The design of motion requires knowledge from molecule kinetics and kinematics. With biological macromolecules, four types of motion are most relevant: thermal motion, small and large conformational changes and Brownian motion. The principles of dynamic realism should be respected as well as the circumstances given in the crowded cellular environment. Ultimately, consistent complexity is proposed as overarching principle for the production of molecular animations and should be achieved between communication objective and abstraction/simplification, audience expertise and scientific complexity, experiment and representation, characters and environment as well as structure and motion representation.

Integrative structure determination reveals functional global flexibility for an ultra-multimodular arabinanase

AbnA is an extracellular GH43 α-L-arabinanase from Geobacillus stearothermophilus, a key bacterial enzyme in the degradation and utilization of arabinan. We present herein its full-length crystal structure, revealing the only ultra-multimodular architecture and the largest structure to be reported so far within the GH43 family. Additionally, the structure of AbnA appears to contain two domains belonging to new uncharacterized carbohydrate-binding module (CBM) families. Three crystallographic conformational states are determined for AbnA, and this conformational flexibility is thoroughly investigated further using the "integrative structure determination" approach, integrating molecular dynamics, metadynamics, normal mode analysis, small angle X-ray scattering, dynamic light scattering, cross-linking, and kinetic experiments to reveal large functional conformational changes for AbnA, involving up to ~100 Å movement in the relative positions of its domains. The integrative structure determination approach demonstrated here may apply also to the conformational study of other ultra-multimodular proteins of diverse functions and structures.

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Looking through the Lens of the Ribosome Biogenesis Evolutionary History: Possible Implications for Archaeal Phylogeny and Eukaryogenesis

Our understanding of microbial diversity and its evolutionary relationships has increased substantially over the last decade. Such an understanding has been greatly fueled by culture-independent metagenomics analyses. However, the outcome of some of these studies and their biological and evolutionary implications, such as the origin of the eukaryotic lineage from the recently discovered archaeal Asgard superphylum, is debated. The sequences of the ribosomal constituents are amongst the most used phylogenetic markers. However, the functional consequences underlying the analysed sequence diversity and their putative evolutionary implications are essentially not taken into consideration. Here, we propose to exploit additional functional hallmarks of ribosome biogenesis to help disentangle competing evolutionary hypotheses. Using selected examples, such as the multiple origins of halophily in archaea or the evolutionary relationship between the Asgard archaea and Eukaryotes, we illustrate and discuss how function-aware phylogenetic framework can contribute to refining our understanding of archaeal phylogeny and the origin of eukaryotic cells.

Vivarium: an interface and engine for integrative multiscale modeling in computational biology

MOTIVATION

This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists.

RESULTS

Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales.

AVAILABILITY AND IMPLEMENTATION

The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Small, but powerful and attractive: 19F in biomolecular NMR

Nuclear magnetic resonance (NMR) spectroscopy is a versatile tool for probing structure, dynamics, folding, and interactions at atomic resolution. While naturally occurring magnetically active isotopes, such as ¹H, ¹³C, or ¹⁵N, are most commonly used in biomolecular NMR, with ¹⁵N and ¹³C isotopic labeling routinely employed at the present time, ¹⁹F is a very attractive and sensitive alternative nucleus, which offers rich information on biomolecules in solution and in the solid state. This perspective summarizes the unique benefits of solution and solid-state ¹⁹F NMR spectroscopy for the study of biological systems. Particular focus is on the most recent studies and on future unique and important potential applications of fluorine NMR methodology.

A Crosslinking Mass Spectrometry Protocol for the Structural Analysis of Microtubule-Associated Proteins

Microtubule-associated proteins (MAPs) engage microtubules (MTs) to regulate both the MT state and wide variety of cytoskeletal functions. A comprehensive understanding of MAPs function requires the structural characterization of physical contacts MAPs make with other proteins, particularly when engaged with the microtubule (MT) lattice. Most of the interaction between MAPs and MTs evade classical structural determination techniques, as the interactions can be both heterogenous and sub-stoichiometric. Crosslinking mass spectrometry (XL-MS) can aid in MAP-MT structure analysis by providing a wealth of residue-based distance restraints. This protocol provides an XL-MS workflow for accurate and unbiased sampling of an equilibrated MAP-MT interaction, involving modifications to the preparation and validation of a MAP-MT construct suitable for crosslinking with fast-sampling heterobifunctional crosslinkers. The distance restrains obtained by this protocol can be used to generate accurate models assembled with an integrative structural modeling approach.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Ten things I `hate' about refinement

Macromolecular refinement is an optimization process that aims to produce the most likely macromolecular structural model in the light of experimental data. As such, macromolecular refinement is one of the most complex optimization problems in wide use. Macromolecular refinement programs have to deal with the complex relationship between the parameters of the atomic model and the experimental data, as well as a large number of types of prior knowledge about chemical structure. This paper draws attention to areas of unfinished business in the field of macromolecular refinement. In it, we describe ten refinement topics that we think deserve attention and discuss directions leading to macromolecular refinement software that would make the best use of modern computer resources to meet the needs of structural biologists of the twenty-first century.

A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies

Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision.

Identification and characterization of metamorphic proteins: Current and future perspectives

Proteins that can reversibly alternate between distinctly different folds under native conditions are described as being metamorphic. The "metamorphome" is the collection of all metamorphic proteins in the proteome, but it remains unknown the extent to which the proteome is populated by this class of proteins. We propose that uncovering the metamorphome will require a synergy of computational screening of protein sequences to identify potential metamorphic behavior and validation through experimental techniques. This perspective discusses computational and experimental approaches that are currently used to predict and characterize metamorphic proteins as well as the need for developing improved methodologies. Since metamorphic proteins act as molecular switches, understanding their properties and behavior could lead to novel applications of these proteins as sensors in biological or environmental contexts.

Conformational dynamics of a multidomain protein by neutron scattering and computational analysis

The flexible conformations of a multidomain protein are responsible for its biological functions. Although MurD, a 47-kDa protein that consists of three domains, sequentially changes its domain conformation from an open form to a closed form through a semiclosed form in its enzymatic reaction, the domain dynamics in each conformation remains unclear. In this study, we verify the conformational dynamics of MurD in the corresponding three states (apo and ATP- and inhibitor-bound states) with a combination of small-angle x-ray and neutron scattering (SAXS and SANS), dynamic light scattering (DLS), neutron backscattering (NBS), neutron spin echo (NSE) spectroscopy, and molecular dynamics (MD) simulations. Applying principal component analysis of the MD trajectories, twisting and open-closed domain modes are identified as the major collective coordinates. The deviations of the experimental SAXS profiles from the theoretical calculations based on the known crystal structures become smaller in the ATP-bound state than in the apo state, and a further decrease is evident upon inhibitor binding. These results suggest that domain motions of the protein are suppressed step by step of each ligand binding. The DLS and NBS data yield collective and self-translational diffusion constants, respectively, and we used them to extract collective domain motions in nanometer and nanosecond scales from the NSE data. In the apo state, MurD shows both twisting and open-closed domain modes, whereas an ATP binding suppresses twisting domain motions, and a further reduction of open-closed mode is seen in the inhibitor-binding state. These observations are consistent with the structure modifications measured by the small-angle scattering as well as the MD simulations. Such changes in the domain dynamics associated with the sequential enzymatic reactions should be related to the affinity and reaction efficiency with a ligand that binds specifically to each reaction state.

An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling

Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.

Characterization of an A3G-VifHIV-1-CRL5-CBFβ Structure Using a Cross-linking Mass Spectrometry Pipeline for Integrative Modeling of Host-Pathogen Complexes

Structural analysis of host-pathogen protein complexes remains challenging, largely due to their structural heterogeneity. Here, we describe a pipeline for the structural characterization of these complexes using integrative structure modeling based on chemical cross-links and residue-protein contacts inferred from mutagenesis studies. We used this approach on the HIV-1 Vif protein bound to restriction factor APOBEC3G (A3G), the Cullin-5 E3 ring ligase (CRL5), and the cellular transcription factor Core Binding Factor Beta (CBFβ) to determine the structure of the (A3G-Vif-CRL5-CBFβ) complex. Using the MS-cleavable DSSO cross-linker to obtain a set of 132 cross-links within this reconstituted complex along with the atomic structures of the subunits and mutagenesis data, we computed an integrative structure model of the heptameric A3G-Vif-CRL5-CBFβ complex. The structure, which was validated using a series of tests, reveals that A3G is bound to Vif mostly through its N-terminal domain. Moreover, the model ensemble quantifies the dynamic heterogeneity of the A3G C-terminal domain and Cul5 positions. Finally, the model was used to rationalize previous structural, mutagenesis and functional data not used for modeling, including information related to the A3G-bound and unbound structures as well as mapping functional mutations to the A3G-Vif interface. The experimental and computational approach described here is generally applicable to other challenging host-pathogen protein complexes.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Order and disorder-An integrative structure of the full-length human growth hormone receptor

Because of its small size (70 kilodalton) and large content of structural disorder (>50%), the human growth hormone receptor (hGHR) falls between the cracks of conventional high-resolution structural biology methods. Here, we study the structure of the full-length hGHR in nanodiscs with small-angle x-ray scattering (SAXS) as the foundation. We develop an approach that combines SAXS, x-ray diffraction, and NMR spectroscopy data obtained on individual domains and integrate these through molecular dynamics simulations to interpret SAXS data on the full-length hGHR in nanodiscs. The hGHR domains reorient freely, resulting in a broad structural ensemble, emphasizing the need to take an ensemble view on signaling of relevance to disease states. The structure provides the first experimental model of any full-length cytokine receptor in a lipid membrane and exemplifies how integrating experimental data from several techniques computationally may access structures of membrane proteins with long, disordered regions, a widespread phenomenon in biology.

Surface-Induced Dissociation of Anionic vs Cationic Native-Like Protein Complexes

Characterizing protein-protein interactions, stoichiometries, and subunit connectivity is key to understanding how subunits assemble into biologically relevant, multisubunit protein complexes. Native mass spectrometry (nMS) has emerged as a powerful tool to study protein complexes due to its low sample consumption and tolerance for heterogeneity. In nMS, positive mode ionization is routinely used and charge reduction, through the addition of solution additives, is often used, as the resulting lower charge states are often considered more native-like. When fragmented by surface-induced dissociation (SID), charge reduced complexes often give increased structural information over their "normal-charged" counterparts. A disadvantage of solution phase charge reduction is that increased adduction, and hence peak broadening, is often observed. Previous studies have shown that protein complexes ionized using negative mode generally form lower charge states relative to positive mode. Here we demonstrate that the lower charged protein complex anions activated by surface collisions fragment in a manner consistent with their solved structures, hence providing substructural information. Negative mode ionization in ammonium acetate offers the advantage of charge reduction without the peak broadening associated with solution phase charge reduction additives and provides direct structural information when coupled with SID. SID of 20S human proteasome (a 28-mer comprised of four stacked heptamer rings in an αββα formation), for example, provides information on both substructure (e.g., splitting into a 7α ring and the corresponding ββα 21-mer, and into α dimers and trimers to provide connectivity around the 7 α ring) and proteoform information on monomers.

Integrative analysis reveals unique structural and functional features of the Smc5/6 complex

Structural maintenance of chromosomes (SMC) complexes are critical chromatin modulators. In eukaryotes, the cohesin and condensin SMC complexes organize chromatin, while the Smc5/6 complex directly regulates DNA replication and repair. The molecular basis for the distinct functions of Smc5/6 is poorly understood. Here, we report an integrative structural study of the budding yeast Smc5/6 holo-complex using electron microscopy, cross-linking mass spectrometry, and computational modeling. We show that the Smc5/6 complex possesses several unique features, while sharing some architectural characteristics with other SMC complexes. In contrast to arm-folded structures of cohesin and condensin, Smc5 and Smc6 arm regions do not fold back on themselves. Instead, these long filamentous regions interact with subunits uniquely acquired by the Smc5/6 complex, namely the Nse2 SUMO ligase and the Nse5/Nse6 subcomplex, with the latter also serving as a linchpin connecting distal parts of the complex. Our 3.0-Å resolution cryoelectron microscopy structure of the Nse5/Nse6 core further reveals a clasped-hand topology and a dimeric interface important for cell growth. Finally, we provide evidence that Nse5/Nse6 uses its SUMO-binding motifs to contribute to Nse2-mediated sumoylation. Collectively, our integrative study identifies distinct structural features of the Smc5/6 complex and functional cooperation among its coevolved unique subunits.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

From integrative structural biology to cell biology

Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.

Integrative structural modeling of a multidomain polo-like kinase

Polo-like kinase 1 (PLK1) is a key regulator and coordinator for mitotic signaling that contains two major functional units of a kinase domain (KD) and a polo-box domain (PBD). While individual domain structures of the KD and the PBD are known, how they interact and assemble into a functional complex remains an open question. The structural model from the KD-PBD-Map205PBM heterotrimeric crystal structure of zebrafish PLK1 represents a major step in understanding the KD and the PBD interactions. However, how these two domains interact when connected by a linker in the full length PLK1 needs further investigation. By integrating different sources of structural data from small-angle X-ray scattering, hydroxyl radical protein footprinting, and computational sampling, here we report an overall architecture for PLK1 multidomain assembly between the KD and the PBD. Our model revealed that the KD uses its C-lobe to interact with the PBD via the site near the phosphopeptide binding site in its auto-inhibitory state in solution. Disruption of this auto-inhibition via site-directed mutagenesis at the KD-PBD interface increases its kinase activity, supporting the functional role of KD-PBD interactions predicted for regulating the PLK1 kinase function. Our results indicate that the full length human PLK1 takes dynamic structures with a variety of domain-domain interfaces in solution.

Genetic interaction mapping informs integrative structure determination of protein complexes

Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

John PC, Bomble YJ. Advances in integrative structural biology: Towards understanding protein complexes in their cellular context

Microorganisms rely on protein interactions to transmit signals, react to stimuli, and grow. One of the best ways to understand these protein interactions is through structural characterization. However, in the past, structural knowledge was limited to stable, high-affinity complexes that could be crystallized. Recent developments in structural biology have revolutionized how protein interactions are characterized. The combination of multiple techniques, known as integrative structural biology, has provided insight into how large protein complexes interact in their native environment. In this mini-review, we describe the past, present, and potential future of integrative structural biology as a tool for characterizing protein interactions in their cellular context.

Capturing Peptide-GPCR Interactions and Their Dynamics

Many biological functions of peptides are mediated through G protein-coupled receptors (GPCRs). Upon ligand binding, GPCRs undergo conformational changes that facilitate the binding and activation of multiple effectors. GPCRs regulate nearly all physiological processes and are a favorite pharmacological target. In particular, drugs are sought after that elicit the recruitment of selected effectors only (biased ligands). Understanding how ligands bind to GPCRs and which conformational changes they induce is a fundamental step toward the development of more efficient and specific drugs. Moreover, it is emerging that the dynamic of the ligand–receptor interaction contributes to the specificity of both ligand recognition and effector recruitment, an aspect that is missing in structural snapshots from crystallography. We describe here biochemical and biophysical techniques to address ligand–receptor interactions in their structural and dynamic aspects, which include mutagenesis, crosslinking, spectroscopic techniques, and mass-spectrometry profiling. With a main focus on peptide receptors, we present methods to unveil the ligand–receptor contact interface and methods that address conformational changes both in the ligand and the GPCR. The presented studies highlight a wide structural heterogeneity among peptide receptors, reveal distinct structural changes occurring during ligand binding and a surprisingly high dynamics of the ligand–GPCR complexes.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

Structural basis of CD4 downregulation by HIV-1 Nef

The HIV-1 Nef protein suppresses multiple immune surveillance mechanisms to promote viral pathogenesis and is an attractive target for the development of novel therapeutics. A key function of Nef is to remove the CD4 receptor from the cell surface by hijacking clathrin- and adaptor protein complex 2 (AP2)-dependent endocytosis. However, exactly how Nef does this has been elusive. Here, we describe the underlying mechanism as revealed by a 3.0-Å crystal structure of a fusion protein comprising Nef and the cytoplasmic domain of CD4 bound to the tetrameric AP2 complex. An intricate combination of conformational changes occurs in both Nef and AP2 to enable CD4 binding and downregulation. A pocket on Nef previously identified as crucial for recruiting class I MHC is also responsible for recruiting CD4, revealing a potential approach to inhibit two of Nef's activities and sensitize the virus to immune clearance.

Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions

Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.

Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions

A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.

Natural allosteric modulators and their biological targets: molecular signatures and mechanisms

Covering: 2008 to 2018Over the last decade more than two hundred single natural products were confirmed as natural allosteric modulators (alloNPs) of proteins. The compounds are presented and discussed with the support of a chemical space, constructed using a principal component analysis (PCA) of molecular descriptors from chemical compounds of distinct databases. This analysis showed that alloNPs are dispersed throughout the majority of the chemical space defined by natural products in general. Moreover, a cluster of alloNPs was shown to occupy a region almost devoid of allosteric modulators retrieved from a dataset composed mainly of synthetic compounds, further highlighting the importance to explore the entire natural chemical space for probing allosteric mechanisms. The protein targets which alloNPs bind to comprised 81 different proteins, which were classified into 5 major groups, with enzymes, in particular hydrolases, being the main representative group. The review also brings a critical interpretation on the mechanisms by which alloNPs display their molecular action on proteins. In the latter analysis, alloNPs were classified according to their final effect on the target protein, resulting in 3 major categories: (i) local alteration of the orthosteric site; (ii) global alteration in protein dynamics that change function; and (iii) oligomer stabilisation or protein complex destabilisation via protein-protein interaction in sites distant from the orthosteric site. G-protein coupled receptors (GPCRs), which use a combination of the three types of allosteric regulation found, were also probed by natural products. In summary, the natural allosteric modulators reviewed herein emphasise their importance for exploring alternative chemotherapeutic strategies, potentially pushing the boundaries of the druggable space of pharmacologically relevant drug targets.

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.