Deciphering the stereo-specific catalytic mechanisms of cis-epoxysuccinate hydrolases producing L(+)-tartaric acid

Microbial epoxide hydrolases, cis-epoxysuccinate hydrolases (CESHs), have been utilized for commercial production of enantiomerically pure L(+)- and D(-)-tartaric acids for decades. However, the stereo-catalytic mechanism of CESH producing L(+)-tartaric acid (CESH[L]) remains unclear. Herein, the crystal structures of two CESH[L]s in ligand-free, product-complexed, and catalytic intermediate forms were determined. These structures revealed the unique specific binding mode for the mirror-symmetric substrate, an active catalytic triad consisting of Asp-His-Glu, and an arginine providing a proton to the oxirane oxygen to facilitate the epoxide ring-opening reaction, which has been pursued for decades. These results provide the structural basis for the rational engineering of these industrial biocatalysts.

Adaptive Stochastic Optimization to Improve Protein Conformation Sampling

We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 is shown to be able to reveal a high-quality native structure for many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem.

Oligomeric State and Holding Activity of Hsp60

Similar to its bacterial homolog GroEL, Hsp60 in oligomeric conformation is known to work as a folding machine, with the assistance of co-chaperonin Hsp10 and ATP. However, recent results have evidenced that Hsp60 can stabilize aggregation-prone molecules in the absence of Hsp10 and ATP by a different, "holding-like" mechanism. Here, we investigated the relationship between the oligomeric conformation of Hsp60 and its ability to inhibit fibrillization of the Ab40 peptide. The monomeric or tetradecameric form of the protein was isolated, and its effect on beta-amyloid aggregation was separately tested. The structural stability of the two forms of Hsp60 was also investigated using differential scanning calorimetry (DSC), light scattering, and circular dichroism. The results showed that the protein in monomeric form is less stable, but more effective against amyloid fibrillization. This greater functionality is attributed to the disordered nature of the domains involved in subunit contacts.

Conformational multiplicity of bacterial ferric binding protein revealed by small angle x-ray scattering and molecular dynamics calculations

This study combines molecular dynamics (MD) simulations with small angle x-ray scattering (SAXS) measurements to investigate the range of conformations that can be adopted by a pH/ionic strength (IS) sensitive protein and to quantify its distinct populations in solution. To explore how the conformational distribution of proteins may be modified in the environmental niches of biological media, we focus on the periplasmic ferric binding protein A (FbpA) from Haemophilus influenzae involved in the mechanism by which bacteria capture iron from higher organisms. We examine iron-binding/release mechanisms of FbpA in varying conditions simulating its biological environment. While we show that these changes fall within the detectable range for SAXS as evidenced by differences observed in the theoretical scattering patterns calculated from the crystal structure models of apo and holo forms, detection of conformational changes due to the point mutation D52A and changes in ionic strength (IS) from SAXS scattering profiles have been challenging. Here, to reach conclusions, statistical analyses with SAXS profiles and results from different techniques were combined in a complementary fashion. The SAXS data complemented by size exclusion chromatography point to multiple and/or alternative conformations at physiological IS, whereas they are well-explained by single crystallographic structures in low IS buffers. By fitting the SAXS data with unique conformations sampled by a series of MD simulations under conditions mimicking the buffers, we quantify the populations of the occupied substates. We also find that the D52A mutant that we predicted by coarse-grained computational modeling to allosterically control the iron binding site in FbpA, responds to the environmental changes in our experiments with conformational selection scenarios that differ from those of the wild type.

Improved Protein Decoy Selection via Non-Negative Matrix Factorization

A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. Research on decoy selection is active. Clustering-based methods are popular, but they fail to identify good/near-native decoys on datasets where near-native decoys are severely under-sampled by a protein structure prediction method. Reasonable progress is reported by methods that additionally take into account the internal energy of a structure and employ it to identify basins in the energy landscape organizing the multitude of decoys. These methods, however, incur significant time costs for extracting basins from the landscape. In this paper, we propose a novel decoy selection method based on non-negative matrix factorization. We demonstrate that our method outperforms energy landscape-based methods. In particular, the proposed method addresses both the time cost issue and the challenge of identifying good decoys in a sparse dataset, successfully recognizing near-native decoys for both easy and hard protein targets.

Generating tertiary protein structures via interpretable graph variational autoencoders

Motivation

Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Since such opportunistic analogies do not allow capturing highly structured data, current deep models struggle to generate physically realistic tertiary structures.

Results

We present novel deep generative models that build upon the graph variational autoencoder framework. In contrast to existing literature, we represent tertiary structures as 'contact' graphs, which allow us to leverage graph-generative deep learning. Our models are able to capture rich, local and distal constraints and additionally compute disentangled latent representations that reveal the impact of individual latent factors. This elucidates what the factors control and makes our models more interpretable. Rigorous comparative evaluation along various metrics shows that the models, we propose advance the state-of-the-art. While there is still much ground to cover, the work presented here is an important first step, and graph-generative frameworks promise to get us to our goal of unraveling the exquisite structural complexity of protein molecules.

Availability and implementation

Code is available at https://github.com/anonymous1025/CO-VAE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Extending the New Generation of Structure Predictors to Account for Dynamics and Allostery

Recent progress in structure-prediction methods that rely on deep learning suggests that the atomic structure of almost any protein may soon be predictable directly from its amino acid sequence. This much-awaited revolution was driven by substantial improvements in the reliability of methods for inferring the spatial distances between amino acid pairs from an analysis of homologous sequences. Improved reliability has been accompanied, however, by a reduced ability to detect amino acid relationships that are not due to direct spatial contacts, such as those that arise from protein dynamics or allostery. Given the central importance of dynamics and allostery to protein activity, we argue that an important future advance would extend modeling beyond predicting a single static structure. Here, we briefly review some of the developments that have led to the remarkable recent achievement in structure prediction and speculate what methods and sources of information may be leveraged in the future to develop a modeling framework that addresses protein dynamics and allostery.

Coupling Monte Carlo, Variational Implicit Solvation, and Binary Level-Set for Simulations of Biomolecular Binding

We develop a hybrid approach that combines the Monte Carlo (MC) method, a variational implicit-solvent model (VISM), and a binary level-set method for the simulation of biomolecular binding in an aqueous solvent. The solvation free energy for the biomolecular complex is estimated by minimizing the VISM free-energy functional of all possible solute-solvent interfaces that are used as dielectric boundaries. This functional consists of the solute volumetric, solute-solvent interfacial, solute-solvent van der Waals interaction, and electrostatic free energy. A technique of shifting the dielectric boundary is used to accurately predict the electrostatic part of the solvation free energy. Minimizing such a functional in each MC move is made possible by our new and fast binary level-set method. This method is based on the approximation of surface area by the convolution of an indicator function with a compactly supported kernel and is implemented by simple flips of numerical grid cells locally around the solute-solvent interface. We apply our approach to the p53-MDM2 system for which the two molecules are approximated by rigid bodies. Our efficient approach captures some of the poses before the final bound state. All-atom molecular dynamics simulations with most of such poses quickly reach the final bound state. Our work is a new step toward realistic simulations of biomolecular interactions. With further improvement of coarse graining and MC sampling, and combined with other models, our hybrid approach can be used to study the free-energy landscape and kinetic pathways of ligand binding to proteins.

Generative Adversarial Learning of Protein Tertiary Structures

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.

Unsupervised multi-instance learning for protein structure determination

Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity remains popular. However, it is unclear how exactly to exploit the groups of decoys revealed via clustering to select individual structures for prediction. In this paper, we provide an intuitive formulation of the decoy selection problem as an instance of unsupervised multi-instance learning. We address the problem in three stages, first organizing given decoys of a protein molecule into bags, then identifying relevant bags, and finally drawing individual instances from these bags to offer as prediction. We propose both non-parametric and parametric algorithms for drawing individual instances. Our evaluation utilizes two datasets, one benchmark dataset of ensembles of decoys for a varied list of protein molecules, and a dataset of decoy ensembles for targets drawn from recent CASP competitions. A comparative analysis with state-of-the-art methods reveals that the proposed approach outperforms existing methods, thus warranting further investigation of multi-instance learning to advance our treatment of decoy selection.

Conformational Studies of Oligosaccharides

The conformation of a molecule strongly affects its function, as demonstrated for peptides and nucleic acids. This correlation is much less established for carbohydrates, the most abundant organic materials in nature. Recent advances in synthetic and analytical techniques have enabled the study of carbohydrates at the molecular level. Recurrent structural features were identified as responsible for particular biological activities or material properties. In this Minireview, recent achievements in the structural characterization of carbohydrates, enabled by systematic studies of chemically defined oligosaccharides, are discussed. These findings can guide the development of more potent glycomimetics. Synthetic carbohydrate materials by design can be envisioned.

Hemoglobin Dynamics in Solution vis-à-vis Under Confinement: An Electrochemical Perspective

Confining heme protein in silico often leads to beneficial functionalities such as an enhanced electrochemical response from the heme center. This can be harnessed to design effective biosensors for medical diagnostics. Proteins under confinement, surface confinement on the electrode to be precise, have more ordered and monodisperse structure compared to the protein in bulk solution. As the electrochemical response of a protein comes from those protein molecules that are confined within the electrical double layer across the electrode-electrolyte interface, it is expected that restriction of conformational fluctuations of the polymeric protein will help in enhancement of the electrochemical response. This is probably the prima facie reason for electrochemical response enhancement under confinement. We examine the dynamic features of hemoglobin under confinement vis-à-vis that in bulk solution. We use a variety of spectroscopic techniques across a wide time-space window to establish the following facts: (a) hardening of the protein polypeptide backbone, (b) slowing down of protein diffusion, (c) increase in relaxation times in NMR, and (d) slowing down of dielectric relaxation times under confinement. This indicates an overall quenching of protein dynamics when the protein is confined inside silica matrix. Thus, we hypothesize that along with retention of secondary structure, this quenching of dynamics contributes to the enhancement of electrochemical response observed.

Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy

CREB-binding protein (CBP) is a multi-subunit scaffold protein complex in transcription regulation process, binding and interacting with ligands such as mixed-lineage leukemia (MLL) and c-Myb allosterically. Here in this study, we have revisited the concept of allostery in CBP via residue-based interaction energy calculation based on molecular dynamics (MD) simulations. To this end, we conducted MD simulations of KIX:MLL:c-Myb ternary complex, its binary components and kinase-inducible domain (KID) interacting domain (KIX) backbone. Interaction energy profiles and cross correlation analysis were performed and the results indicated that KIX:MLL and KIX:c-Myb:MLL complexes demonstrate significant similarities according to both analysis methods. Two regions in the KIX backbone were apparent from the interaction energy and cross correlation maps that hold a key to allostery phenomena observed in CBP. While one of these regions are related to the ligand binding residues, the other comprises of L₁₂-G₂ loop and α₃ helix regions that have been found to have a significant role in allosteric signal propagation. All in all, residue-based interaction energy calculation method is demonstrated to be a valuable calculation technique for the detection of allosteric signal propagation and ligand interaction regions.

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

Anomaly Detection-Based Recognition of Near-Native Protein Structures

The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.

Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection

Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is active, but here we assess the promise of autoencoders. Motivated by rapid progress in neural network research, we investigate and evaluate autoencoders on yielding linear and nonlinear featurizations of protein tertiary structures. An additional reason we focus on autoencoders as the engine to obtain featurizations is the versatility of their architectures and the ease with which changes to architecture yield linear versus nonlinear features. While open-source neural network libraries, such as Keras, which we employ here, greatly facilitate constructing, training, and evaluating autoencoder architectures and conducting model search, autoencoders have not yet gained popularity in the structure biology community. Here we demonstrate their utility in a practical context. Employing autoencoder-based featurizations, we address the classic problem of decoy selection in protein structure prediction. Utilizing off-the-shelf supervised learning methods, we demonstrate that the featurizations are indeed meaningful and allow detecting active tertiary structures, thus opening the way for further avenues of research.

Building maps of protein structure spaces in template-free protein structure prediction

An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction.

Binding site plasticity in viral PPxY Late domain recognition by the third WW domain of human NEDD4

The recognition of PPxY viral Late domains by the third WW domain of the HECT-E3 ubiquitin ligase NEDD4 (hNEDD4-WW3) is essential for the completion of the budding process of numerous enveloped viruses, including Ebola, Marburg, HTLV1 or Rabies. hNEDD4-WW3 has been validated as a promising target for the development of novel host-oriented broad spectrum antivirals. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. We present here a detailed structural and thermodynamic study of the interactions of hNEDD4-WW3 with viral Late domains combining isothermal titration calorimetry, NMR structural determination and molecular dynamics simulations. Structural and energetic differences in Late domain recognition reveal a highly plastic hNEDD4-WW3 binding site that can accommodate PPxY-containing ligands with varying orientations. These orientations are mostly determined by specific conformations adopted by residues I859 and T866. Our results suggest a conformational selection mechanism, extensive to other WW domains, and highlight the functional relevance of hNEDD4-WW3 domain conformational flexibility at the binding interface, which emerges as a key element to consider in the search for potent and selective inhibitors of therapeutic interest.

Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection

The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, several challenges stand in the wayof leveraging energy landscapes for relating structure and structural dynamics to function. Energylandscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins inthem do not always correspond to stable structural states but are instead the result of inherentinaccuracies in semi-empirical molecular energy functions. Due to these challenges, energeticsis typically ignored in computational approaches addressing long-standing central questions incomputational biology, such as protein decoy selection. In the latter, the goal is to determine over apossibly large number of computationally-generated three-dimensional structures of a protein thosestructures that are biologically-active/native. In recent work, we have recast our attention on theprotein energy landscape and its role in helping us to advance decoy selection. Here, we summarizesome of our successes so far in this direction via unsupervised learning. More importantly, we furtheradvance the argument that the energy landscape holds valuable information to aid and advance thestate of protein decoy selection via novel machine learning methodologies that leverage supervisedlearning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitativeevaluation of how leveraging protein energy landscapes advances an important problem in proteinmodeling. However, the ideas and concepts presented here are generally useful to make discoveriesin studies aiming to relate molecular structure and structural dynamics to function.

Learning Organizations of Protein Energy Landscapes: An Application on Decoy Selection in Template-Free Protein Structure Prediction

The protein energy landscape, which lifts the protein structure space by associating energies with structures, has been useful in improving our understanding of the relationship between structure, dynamics, and function. Currently, however, it is challenging to automatically extract and utilize the underlying organization of an energy landscape to the link structural states it houses to biological activity. In this chapter, we first report on two computational approaches that extract such an organization, one that ignores energies and operates directly in the structure space and another that operates on the energy landscape associated with the structure space. We then describe two complementary approaches, one based on unsupervised learning and another based on supervised learning. Both approaches utilize the extracted organization to address the problem of decoy selection in template-free protein structure prediction. The presented results make the case that learning organizations of protein energy landscapes advances our ability to link structures to biological activity.

The Many Faces of a Dynamic Virion: Implications of Viral Breathing on Flavivirus Biology and Immunogenicity

Flaviviruses are arthropod-borne RNA viruses that are a significant threat to global health due to their widespread distribution, ability to cause severe disease in humans, and capacity for explosive spread following introduction into new regions. Members of this genus include dengue, tick-borne encephalitis, yellow fever, and Zika viruses. Vaccination has been a highly successful means to control flaviviruses, and neutralizing antibodies are an important component of a protective immune response. High-resolution structures of flavivirus structural proteins and virions, alone and in complex with antibodies, provide a detailed understanding of viral fusion mechanisms and virus-antibody interactions. However, mounting evidence suggests these structures provide only a snapshot of an otherwise structurally dynamic virus particle. The contribution of the structural ensemble arising from viral breathing to the biology, antigenicity, and immunity of flaviviruses is discussed, including implications for the development and evaluation of flavivirus vaccines.

Modeling the Tertiary Structure of the Rift Valley Fever Virus L Protein

A tertiary structure governs, to a great extent, the biological activity of a protein in the living cell and is consequently a central focus of numerous studies aiming to shed light on cellular processes central to human health. Here, we aim to elucidate the structure of the Rift Valley fever virus (RVFV) L protein using a combination of in silico techniques. Due to its large size and multiple domains, elucidation of the tertiary structure of the L protein has so far challenged both dry and wet laboratories. In this work, we leverage complementary perspectives and tools from the computational-molecular-biology and bioinformatics domains for constructing, refining, and evaluating several atomistic structural models of the L protein that are physically realistic. All computed models have very flexible termini of about 200 amino acids each, and a high proportion of helical regions. Properties such as potential energy, radius of gyration, hydrodynamics radius, flexibility coefficient, and solvent-accessible surface are reported. Structural characterization of the L protein enables our laboratories to better understand viral replication and transcription via further studies of L protein-mediated protein-protein interactions. While results presented a focus on the RVFV L protein, the following workflow is a more general modeling protocol for discovering the tertiary structure of multidomain proteins consisting of thousands of amino acids.

Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction

Background

Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possible three-dimensional spatial arrangements of the chain of amino acids that constitute a protein molecule is vast and high-dimensional. Exploration of the conformation spaces is performed in a sampling-based manner and is biased by the internal energy that sums atomic interactions. Even state-of-the-art energy functions that quantify such interactions are inherently inaccurate and associate with protein conformation spaces overly rugged energy surfaces riddled with artifact local minima. The response to these challenges in template-free protein structure prediction is to generate large numbers of low-energy conformations (also referred to as decoys) as a way of increasing the likelihood of having a diverse decoy dataset that covers a sufficient number of local minima possibly housing near-native conformations.

Results

In this paper we pursue a complementary approach and propose to directly control the diversity of generated decoys. Inspired by hard optimization problems in high-dimensional and non-linear variable spaces, we propose that conformation sampling for decoy generation is more naturally framed as a multi-objective optimization problem. We demonstrate that mechanisms inherent to evolutionary search techniques facilitate such framing and allow balancing multiple objectives in protein conformation sampling. We showcase here an operationalization of this idea via a novel evolutionary algorithm that has high exploration capability and is also able to access lower-energy regions of the energy landscape of a given protein with similar or better proximity to the known native structure than several state-of-the-art decoy generation algorithms.

Conclusions

The presented results constitute a promising research direction in improving decoy generation for template-free protein structure prediction with regards to balancing of multiple conflicting objectives under an optimization framework. Future work will consider additional optimization objectives and variants of improvement and selection operators to apportion a fixed computational budget. Of particular interest are directions of research that attenuate dependence on protein energy models.

Hydrodynamic Behavior of the Intrinsically Disordered Potyvirus Protein VPg, of the Translation Initiation Factor eIF4E and of their Binary Complex

Protein intrinsic disorder is involved in many biological processes and good experimental models are valuable to investigate its functions. The potyvirus genome-linked protein, VPg, displays many features of an intrinsically disordered protein. The virus cycle requires the formation of a complex between VPg and eIF4E, one of the host translation initiation factors. An in-depth characterization of the hydrodynamic properties of VPg, eIF4E, and of their binary complex VPg-eIF4E was carried out. Two complementary experimental approaches, size-exclusion chromatography and fluorescence anisotropy, which is more resolving and revealed especially suitable when protein concentration is the limiting factor, allowed to estimate monomers compaction upon complex formation. VPg possesses a high degree of hydration which is in agreement with its classification as a partially folded protein in between a molten and pre-molten globule. The natively disordered first 46 amino acids of eIF4E contribute to modulate the protein hydrodynamic properties. The addition of an N-ter His tag decreased the conformational entropy of this intrinsically disordered region. A comparative study between the two tagged and untagged proteins revealed the His tag contribution to proteins hydrodynamic behavior.

Mechanism of actin polymerization revealed by cryo-EM structures of actin filaments with three different bound nucleotides

We used cryo-electron microscopy (cryo-EM) to reconstruct actin filaments with bound AMPPNP (β,γ-imidoadenosine 5'-triphosphate, an ATP analog, resolution 3.1 Å), ADP-Pi (ADP with inorganic phosphate, resolution 3.1 Å), or ADP (resolution 3.6 Å). Subunits in the three filaments have similar backbone conformations, so assembly rather than ATP hydrolysis or phosphate dissociation is responsible for their flattened conformation in filaments. Polymerization increases the rate of ATP hydrolysis by changing the positions of the side chains of Q137 and H161 in the active site. Flattening during assembly also promotes interactions along both the long-pitch and short-pitch helices. In particular, conformational changes in subdomain 3 open up multiple favorable interactions with the DNase-I binding loop in subdomain 2 of the adjacent subunit. Subunits at the barbed end of the filament are likely to be in this favorable conformation, while monomers are not. This difference explains why filaments grow faster at the barbed end than the pointed end. When phosphate dissociates from ADP-Pi-actin through a backdoor channel, the conformation of the C terminus changes so it distorts the DNase binding loop, which allows cofilin binding, and a network of interactions among S14, H73, G74, N111, R177, and G158 rearranges to open the phosphate release site.

Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction

Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

Comparative analysis of mutational robustness of the intrinsically disordered viral protein VPg and of its interactor eIF4E

Conformational intrinsic disorder is a feature present in many virus proteins. Intrinsically disordered regions (IDRs) have weaker structural requirement than ordered regions and mutations in IDRs could have a lower impact on the virus fitness. This could favor its exploration of adaptive solutions. The potyviral protein VPg contains IDRs with determinants for adaptation to its host plant. To experimentally assess whether IDRs are more resistant to mutations than ordered regions, the biologically relevant interaction between mutant libraries of both VPg and the eukaryotic translation initiation factor 4E (eIF4E) and their respective wild type partner was examined using yeast two hybrid assay. Our data shows that VPg is significantly more robust to mutations than eIF4E and as such belongs to a particular class of intrinsically disordered proteins. This result is discussed from the standpoint of IDRs involvement in the virus adaptive processes.

Atomistic Analysis of ToxN and ToxI Complex Unbinding Mechanism

ToxIN is a triangular structure formed by three protein toxins (ToxNs) and three specific noncoding RNA antitoxins (ToxIs). To respond to stimuli, ToxI is preferentially degraded, releasing the ToxN. Thus, the dynamic character is essential in the normal function interactions between ToxN and ToxI. Here, equilibrated molecular dynamics (MD) simulations were performed to study the stability of ToxN and ToxI. The results indicate that ToxI adjusts the conformation of 3' and 5' termini to bind to ToxN. Steered molecular dynamics (SMD) simulations combined with the recently developed thermodynamic integration in 3nD (TI3nD) method were carried out to investigate ToxN unbinding from the ToxIN complex. The potentials of mean force (PMFs) and atomistic pictures suggest the unbinding mechanism as follows: (1) dissociation of the 5' terminus from ToxN, (2) missing the interactions involved in the 3' terminus of ToxI without three nucleotides (G31, A32, and A33), (3) starting to unfold for ToxI, (4) leaving the binding package of ToxN for three nucleotides of ToxI, (5) unfolding of ToxI. This work provides information on the structure-function relationship at the atomistic level, which is helpful for designing new potent antibacterial drugs in the future.

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

Background

The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.

Results

Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.

Conclusions

The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

Substrate-induced conformational change in cytochrome P450 OleP

The regulation of cytochrome P450 activity is often achieved by structural transitions induced by substrate binding. We describe the conformational transition experienced upon binding by the P450 OleP, an epoxygenase involved in oleandomycin biosynthesis. OleP bound to the substrate analog 6DEB crystallized in 2 forms: one with an ensemble of open and closed conformations in the asymmetric unit and another with only the closed conformation. Characterization of OleP-6DEB binding kinetics, also using the P450 inhibitor clotrimazole, unveiled a complex binding mechanism that involves slow conformational rearrangement with the accumulation of a spectroscopically detectable intermediate where 6DEB is bound to open OleP. Data reported herein provide structural snapshots of key precatalytic steps in the OleP reaction and explain how structural rearrangements induced by substrate binding regulate activity.-Parisi, G., Montemiglio, L. C., Giuffrè, A., Macone, A., Scaglione, A., Cerutti, G., Exertier, C., Savino, C., Vallone, B. Substrate-induced conformational change in cytochrome P450 OleP.

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

Optimising minimal building blocks for addressable self-assembly

Addressable structures are characterised by the set of unique components from which they are built and by the specific location that each component occupies. For an addressable structure to self-assemble, its constituent building blocks must be encoded with sufficient information to define their positions with respect to each other and to enable them to navigate to those positions. DNA, with its vast scope for encoding specific interactions, has been successfully used to synthesise addressable systems of several hundred components. In this work we examine the complementary question of the minimal requirements for building blocks to undergo addressable self-assembly driven by a controlled temperature quench. Our testbed is an idealised model of cubic particles patterned with attractive interactions. We introduce a scheme for optimising the interactions using a variant of basin-hopping and a negative design principle. The designed building blocks are tested dynamically in simple target structures to establish how their complexity affects the limits of reliable self-assembly.

The Potyvirus Particle Recruits the Plant Translation Initiation Factor eIF4E by Means of the VPg covalently Linked to the Viral RNA

The viral protein genome-linked (VPg) of potyviruses is a protein covalently linked to the 5' end of viral RNA. It interacts with eIF4E, a component of the cellular translation initiation complex. It has been suggested that the 5' RNA-linked VPg could mimic the cellular mRNA cap, promoting synthesis of viral proteins. Here, we report evidence for recruitment of the plant eIF4E by Lettuce mosaic virus (LMV, potyvirus) particles via the 5' RNA-linked VPg. Analysis of the viral population was performed by enzyme-linked immunosorbent assay-based tests, either with crude extracts of LMV-infected tissues or purified viral particles. In both cases, LMV-VPg and LMV-eIF4E subpopulations could be detected. After reaching a maximum within the first 2 weeks postinoculation, these populations decreased and very few labeled particles were found later than 3 weeks postinoculation. The central domain of VPg (CD-VPg) was found to be exposed at the surface of the particles. Using a purified recombinant lettuce eIF4E and CD-VPg-specific antibodies, we demonstrate that the plant factor binds to the VPg via its central domain. Moreover, the plant eIF4E factor could be imaged at one end of the particles purified from LMV plant extracts, by immunoredox atomic force microscopy coupled to scanning electrochemical microscopy. We discuss the biological significance of these results.

Protein phosphorylation and its role in the regulation of Annexin A2 function

BACKGROUND

Annexin A2 (AnxA2) is a multifunctional protein involved in endocytosis, exocytosis, membrane domain organisation, actin remodelling, signal transduction, protein assembly, transcription and mRNA transport, as well as DNA replication and repair.

SCOPE OF REVIEW

The current knowledge of the role of phosphorylation in the functional regulation of AnxA2 is reviewed. To provide a more comprehensive treatment of this topic, we also address in depth the phosphorylation process in general and discuss its possible conformational effects. Furthermore, we discuss the apparent limitations of the methods used to investigate phosphoproteins, as exemplified by the study of AnxA2.

MAJOR CONCLUSIONS

AnxA2 is subjected to complex regulation by post-translational modifications affecting its cellular functions, with Ser11, Ser25 and Tyr23 representing important phosphorylation sites. Thus, Ser phosphorylation of AnxA2 is involved in the recruitment and docking of secretory granules, the regulation of its association with S100A10, and sequestration of perinuclear, translationally inactive mRNP complexes. By contrast, Tyr phosphorylation of AnxA2 regulates its role in actin dynamics and increases its association with endosomal compartments. Modification of its three main phosphorylation sites is not sufficient to discriminate between its numerous functions. Thus, fine-tuning of AnxA2 function is mediated by the joint action of several post-translational modifications.

GENERAL SIGNIFICANCE

AnxA2 participates in malignant cell transformation, and its overexpression and/or phosphorylation is associated with cancer progression and metastasis. Thus, tight regulation of AnxA2 function is an integral aspect of cellular homeostasis. The presence of AnxA2 in cancer cell-derived exosomes, as well as the potential regulation of exosomal AnxA2 by phosphorylation or other PTMs, are topics of great interest.

The Sampling of Conformational Dynamics in Ambient-Temperature Crystal Structures of Arginine Kinase

Arginine kinase provides a model for functional dynamics, studied through crystallography, enzymology, and nuclear magnetic resonance. Structures are now solved, at ambient temperature, for the transition state analog (TSA) complex. Analysis of quasi-rigid sub-domain displacements show that differences between the two TSA structures average about 5% of changes between substrate-free and TSA forms, and they are nearly co-linear. Small backbone hinge rotations map to sites that also flex on substrate binding. Anisotropic atomic displacement parameters (ADPs) are refined using rigid-body TLS constraints. Consistency between crystal forms shows that they reflect intrinsic molecular properties more than crystal lattice effects. In many regions, the favored directions of thermal/static displacement are appreciably correlated with movements on substrate binding. Correlation between ADPs and larger substrate-associated movements implies that the latter approximately follow paths of low-energy intrinsic motions.

FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.

The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

The interaction with gold suppresses fiber-like conformations of the amyloid β (16-22) peptide

Inorganic surfaces and nanoparticles can accelerate or inhibit the fibrillation process of proteins and peptides, including the biomedically relevant amyloid β peptide. However, the microscopic mechanisms that determine such an effect are still poorly understood. By means of large-scale, state-of-the-art enhanced sampling molecular dynamics simulations, here we identify an interaction mechanism between the segments 16-22 of the amyloid β peptide, known to be fibrillogenic by itself, and the Au(111) surface in water that leads to the suppression of fiber-like conformations from the peptide conformational ensemble. Moreover, thanks to advanced simulation analysis techniques, we characterize the conformational selection vs. induced fit nature of the gold effect. Our results disclose an inhibition mechanism that is rooted in the details of the microscopic peptide-surface interaction rather than in general phenomena such as peptide sequestration from the solution.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Conformational selection and induced fit for RNA polymerase and RNA/DNA hybrid backtracked recognition

RNA polymerase catalyzes transcription with a high fidelity. If DNA/RNA mismatch or DNA damage occurs downstream, a backtracked RNA polymerase can proofread this situation. However, the backtracked mechanism is still poorly understood. Here we have performed multiple explicit-solvent molecular dynamics (MD) simulations on bound and apo DNA/RNA hybrid to study backtracked recognition. MD simulations at room temperature suggest that specific electrostatic interactions play key roles in the backtracked recognition between the polymerase and DNA/RNA hybrid. Kinetics analysis at high temperature shows that bound and apo DNA/RNA hybrid unfold via a two-state process. Both kinetics and free energy landscape analyses indicate that bound DNA/RNA hybrid folds in the order of DNA/RNA contracting, the tertiary folding and polymerase binding. The predicted Φ-values suggest that C7, G9, dC12, dC15, and dT16 are key bases for the backtracked recognition of DNA/RNA hybrid. The average RMSD values between the bound structures and the corresponding apo ones and Kolmogorov-Smirnov (KS) P-test analyses indicate that the recognition between DNA/RNA hybrid and polymerase might follow an induced fit mechanism for DNA/RNA hybrid and conformation selection for polymerase. Furthermore, this method could be used to relative studies of specific recognition between nucleic acid and protein.

The Solution Structure and Dynamics of Full-length Human Cerebral Dopamine Neurotrophic Factor and Its Neuroprotective Role against α-Synuclein Oligomers

Cerebral dopamine neurotrophic factor (CDNF) is a promising therapeutic agent for Parkinson disease. As such, there has been great interest in studying its mode of action, which remains unknown. The three-dimensional crystal structure of the N terminus (residues 9-107) of CDNF has been determined, but there have been no published structural studies on the full-length protein due to proteolysis of its C-terminal domain, which is considered intrinsically disordered. An improved purification protocol enabled us to obtain active full-length CDNF and to determine its three-dimensional structure in solution. CDNF contains two well folded domains (residues 10-100 and 111-157) that are linked by a loop of intermediate flexibility. We identified two surface patches on the N-terminal domain that were characterized by increased conformational dynamics that should allow them to embrace active sites. One of these patches is formed by residues Ser-33, Leu-34, Ala-66, Lys-68, Ile-69, Leu-70, Ser-71, and Glu-72. The other includes a flexibly disordered N-terminal tail (residues 1-9), followed by the N-terminal portion of α-helix 1 (residues Cys-11, Glu-12, Val-13, Lys-15, and Glu-16) and residue Glu-88. The surface of the C-terminal domain contains two conserved active sites, which have previously been identified in mesencephalic astrocyte-derived neurotrophic factor, a CDNF paralog, which corresponds to its intracellular mode of action. We also showed that CDNF was able to protect dopaminergic neurons against injury caused by α-synuclein oligomers. This advises its use against physiological damages caused by α-synuclein oligomers, as observed in Parkinson disease and several other neurodegenerative diseases.

How the flexibility of human histone deacetylases influences ligand binding: an overview

Over the past decade, human histone deacetylases (HDACs) have become interesting as therapeutic targets because of the benefits that their modulation might provide in aging-related disorders. Recently, studies using crystallography and computational chemistry have provided information on the structure and conformational changes related to HDAC-mediated recognition events. Through the description of the key mass and one-off movements observed in metal-dependent HDACs, here we highlight the impact of flexibility on drug-binding affinity and specificity. The collected information will be useful for not only a better understanding of the biological functions of HDACs, but also the conception of new selective binders.

A game of numbers: the stoichiometry of antibody-mediated neutralization of flavivirus infection

The humoral response contributes to the protection against viral pathogens. Although antibodies have the potential to inhibit viral infections via several mechanisms, an ability to neutralize viruses directly may be particularly important. Neutralizing antibody titers are commonly used as predictors of protection from infection, especially in the context of vaccine responses and immunity. Despite the simplicity of the concept, how antibody binding results in virus inactivation is incompletely understood despite decades of research. Flaviviruses have been an attractive system in which to seek a structural and quantitative understanding of how antibody interactions with virions modulate infection because of the contribution of antibodies to both protection and pathogenesis. This review will present a stoichiometric model of antibody-mediated neutralization of flaviviruses and discuss how these concepts can inform the development of vaccines and antibody-based therapeutics.

Crosstalk between signaling pathways provided by single and multiple protein phosphorylation sites

Cellular fate depends on the spatiotemporal separation and integration of signaling processes that can be provided by phosphorylation events. In this study, we identify the crucial points in signaling crosstalk that can be triggered by discrete phosphorylation events on a single target protein. We integrated the data on individual human phosphosites with the evidence on their corresponding kinases, the functional consequences of phosphorylation on activity of the target protein and corresponding pathways. Our results show that there is a substantial fraction of phosphosites that can play critical roles in crosstalk between alternative and redundant pathways and regulatory outcome of phosphorylation can be linked to a type of phosphorylated residue. These regulatory phosphosites can serve as hubs in the signal flow and their functional roles are directly connected to their specific properties. Namely, phosphosites with similar regulatory functions are phosphorylated by the same kinases and participate in regulation of similar biochemical pathways. Such sites are more likely to cluster in sequence and space unlike sites with antagonistic outcomes of their phosphorylation on a target protein. In addition, we found that in silico phosphorylation of sites with similar functional consequences has comparable outcomes on a target protein stability. An important role of phosphorylation sites in biological crosstalk is evident from the analysis of their evolutionary conservation.

Dynamic conformational change regulates the protein-DNA recognition: an investigation on binding of a Y-family polymerase to its target DNA

Protein-DNA recognition is a central biological process that governs the life of cells. A protein will often undergo a conformational transition to form the functional complex with its target DNA. The protein conformational dynamics are expected to contribute to the stability and specificity of DNA recognition and therefore may control the functional activity of the protein-DNA complex. Understanding how the conformational dynamics influences the protein-DNA recognition is still challenging. Here, we developed a two-basin structure-based model to explore functional dynamics in Sulfolobus solfataricus DNA Y-family polymerase IV (DPO4) during its binding to DNA. With explicit consideration of non-specific and specific interactions between DPO4 and DNA, we found that DPO4-DNA recognition is comprised of first 3D diffusion, then a short-range adjustment sliding on DNA and finally specific binding. Interestingly, we found that DPO4 is under a conformational equilibrium between multiple states during the binding process and the distributions of the conformations vary at different binding stages. By modulating the strength of the electrostatic interactions, the flexibility of the linker, and the conformational dynamics in DPO4, we drew a clear picture on how DPO4 dynamically regulates the DNA recognition. We argue that the unique features of flexibility and conformational dynamics in DPO4-DNA recognition have direct implications for low-fidelity translesion DNA synthesis, most of which is found to be accomplished by the Y-family DNA polymerases. Our results help complete the description of the DNA synthesis process for the Y-family polymerases. Furthermore, the methods developed here can be widely applied for future investigations on how various proteins recognize and bind specific DNA substrates.

Protein-DNA recognition is crucial for many key biological processes in cells. Protein often undergoes large-scale conformational change during DNA recognition. However, the physical and global understanding of flexible protein-DNA binding is still challenging. Here, we developed a theoretical approach to investigate binding of a Y-family DNA polymerase to its target DNA during the DNA synthesis process. The results of electrostatic-controlled multi-step DNA binding process accompanied with multi-state conformational transition of protein occurring throughout are in remarkable agreement with experiments. During the process of protein-DNA recognition, the flexibility is found to facilitate both the conformational transition of protein (intra-chain dynamics) and DNA binding (inter-chain dynamics) simultaneously. Therefore, we provided a quantitative description of protein-DNA binding mechanism that flexibility or conformational change regulates DNA recognition dynamically, leading to high efficiency and specificity of function for protein-DNA recognition.