Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops

Comparative analysis of structural dynamics and allosteric mechanisms of RecA/Rad51 family proteins: Integrated atomistic MD simulation and network-based analysis

Homologous recombination plays a key role in double-strand break repair, stalled replication fork repair, and meiosis. The RecA/Rad51 family recombinases catalyze the DNA strand invasion reaction that occurs during homologous recombination. However, the high sequence differences between homologous groups have hindered the thoroughly studies of this ancient protein family. The dynamic mechanisms of the family, particularly at the residual level, remain poorly understood. In this work, five representative RecA/Rad51 recombinase family members from all major kingdoms of living organisms: prokaryotes, eukaryotes, archaea, and viruses, were selected to explore the molecular mechanisms behind their conserved biological significance. A variety of techniques, including all-atom molecular dynamics simulation, perturbation response scanning, and protein structure network analysis, were used to examine the flexibility and correlation of protein domains, distribution of sensors and effectors and conserved hub residues. Furthermore, the potential communication routes between the ATP-binding region and the DNA-binding region of each recombinase were identified. Our results demonstrate the conserved molecular dynamics of these recombinases in the early stage of homologous recombination, including cooperative motions between regions, conserved sensing and effecting functional residue distribution, and conserved hub residues. Meanwhile, the unique ATP-DNA communication routes of each recombinase was also revealed. These results provide new insights into the mechanism of RecA/Rad51 family proteins, and provide new theoretical guidance for the development of allosteric inhibitors and the application of RecA/Rad51 family proteins.

Dynamic correlations: exact and approximate methods for mutual information

MOTIVATION

Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions.

RESULTS

Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths.

AVAILABILITY AND IMPLEMENTATION

Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.

ARID3a from the ARID family: structure, role in autoimmune diseases and drug discovery

The AT-rich interaction domain (ARID) family of DNA-binding proteins is a group of transcription factors and chromatin regulators with a highly conserved ARID domain that recognizes specific AT-rich DNA sequences. Dysfunction of ARID family members has been implicated in various human diseases including cancers and intellectual disability. Among them, ARID3a has gained increasing attention due to its potential involvement in autoimmunity. In this article we provide an overview of the ARID family, focusing on the structure and biological functions of ARID3a. It explores the role of ARID3a in autoreactive B cells and its contribution to autoimmune diseases such as systemic lupus erythematosus and primary biliary cholangitis. Furthermore, we also discuss the potential for drug discovery targeting ARID3a and present a plan for future research in this field.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Computational Insights into the Dynamic Structural Features and Binding Characteristics of Recombinase UvsX Compared with RecA

RecA family recombinases are the core enzymes in the process of homologous recombination, and their normal operation ensures the stability of the genome and the healthy development of organisms. The UvsX protein from bacteriophage T4 is a member of the RecA family recombinases and plays a central role in T4 phage DNA repair and replication, which provides an important model for the biochemistry and genetics of DNA metabolism. UvsX shares a high degree of structural similarity and function with RecA, which is the most deeply studied member of the RecA family. However, the detailed molecular mechanism of UvsX has not been resolved. In this study, a comprehensive all-atom molecular dynamics simulation of the UvsX protein dimer complex was carried out in order to investigate the conformational and binding properties of UvsX in combination with ATP and DNA, and the simulation of RecA was synchronized with the property comparison learning for UvsX. This study confirmed the highly conserved molecular structure characteristics and catalytic centers of RecA and UvsX, and also discovered differences in regional conformation, volatility and the ability to bind DNA between the two proteins at different temperatures, which would be helpful for the subsequent understanding and application of related recombinases.

Dynamics and Allosteric Information Pathways of Unphosphorylated c-Cbl

Human c-Cbl is a RING-type ligase and plays a central role in the protein degradation cascade. To elucidate its conformational changes related to substrate binding, we performed molecular dynamics simulations of different variants/states of c-Cbl for a cumulative time of 68 μs. Our simulations demonstrate that before the substrate binds, the RING domain samples a broad set of conformational states at a biologically relevant salt concentration, including the closed, partially open, and fully open states, whereas substrate binding leads to a restricted conformational sampling. Phe378 and the C-terminal region play an essential role in stabilizing the partially open state. To visualize the allosteric signal transmission pathways from the substrate-binding site to the 40 Å apart RING domain and identify the critical residues for allostery, we have created a subgraph from the optimal and suboptimal paths. Redundant paths are seen in the SH2 domain where the substrate binds, while the major bottlenecks are found at the junction between the SH2 domain and the linker helix region as well as that between the SH2 domain and the 4H bundle. These bottlenecks separate the paths into two overall routes. The nodes/residues at the bottlenecks on the subgraph are considered allosteric hot spots. This subgraph approach provides a general tool for network visualization and determination of critical residues for allostery. The structurally and allosterically critical residues identified in our work are testable and would provide valuable insights into the emerging strategies for drug discovery, such as targeted protein degradation.

Structure and DNA binding analysis of AT-rich interaction domain present in human BAF-B specific subunit BAF250b

BAF250b and its paralog BAF250a are the DNA-binding central hub proteins present in BAF-B and BAF-A classes of SWI/SNF chromatin-remodeling complexes. BAF250b contains an AT-rich interaction domain (ARID) and C-terminal BAF250_C domain, and it is found mutated in several cancers. ARID is a conserved helix-turn-helix motif-containing DNA-binding domain present in several eukaryotic proteins. The ARID of BAF250b has been proposed to play roles in recruiting SWI/SNF to the target gene promoters for their activation. BAF250b ARID structures had been deposited in the protein data bank by a structural genomics consortium. However, it is not well-studied for its DNA-binding and solution dynamic properties. Here, we report complete backbone NMR resonance assignments of human BAF250b ARID. NMR chemical shifts and the backbone dynamics showed that the solution structure of the protein matched the reported crystal structures. The structure and chemical shift indexing revealed the presence of a short β-sheet in the DNA-binding region of BAF250b ARID that was absent in the structure of its paralog BAF250a ARID. NMR chemical shift perturbations identified DNA-binding residues and revealed the DNA-binding interface on BAF250b ARID. NMR data-driven HADDOCK models of BAF250b ARID - DNA complexes revealed its plausible mode of DNA-binding. Isothermal titration calorimetry experiments showed that BAF250b ARID interacts with DNA sequences with moderate affinities like BAF250a ARID. However, distinct thermodynamic signatures were observed for binding of BAF250a ARID and BAF250b ARID to AT-rich DNA sequence, suggesting that subtle sequence and structural differences in these two proteins influence their DNA-binding.

Structures and nucleic acid-binding preferences of the eukaryotic ARID domain

The DNA-binding AT-rich interactive domain (ARID) exists in a wide range of proteins throughout eukaryotic kingdoms. ARID domain-containing proteins are involved in manifold biological processes, such as transcriptional regulation, cell cycle control and chromatin remodeling. Their individual domain composition allows for a sub-classification within higher mammals. ARID is categorized as binder of double-stranded AT-rich DNA, while recent work has suggested ARIDs as capable of binding other DNA motifs and also recognizing RNA. Despite a broad variability on the primary sequence level, ARIDs show a highly conserved fold, which consists of six α-helices and two loop regions. Interestingly, this minimal core domain is often found extended by helices at the N- and/or C-terminus with potential roles in target specificity and, subsequently function. While high-resolution structural information from various types of ARIDs has accumulated over two decades now, there is limited access to ARID-DNA complex structures. We thus find ourselves left at the beginning of understanding ARID domain target specificities and the role of accompanying domains. Here, we systematically summarize ARID domain conservation and compare the various types with a focus on their structural differences and DNA-binding preferences, including the context of multiple other motifs within ARID domain containing proteins.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

Lipid Rafts Interaction of the ARID3A Transcription Factor with EZRIN and G-Actin Regulates B-Cell Receptor Signaling

Several diseases originate via dysregulation of the actin cytoskeleton. The ARID3A/Bright transcription factor has also been implicated in malignancies, primarily those derived from hematopoietic lineages. Previously, we demonstrated that ARID3A shuttles between the nucleus and the plasma membrane, where it localizes within lipid rafts. There it interacts with components of the B-cell receptor (BCR) to reduce its ability to transmit downstream signaling. We demonstrate here that a direct component of ARID3A-regulated BCR signal strength is cortical actin. ARID3A interacts with actin exclusively within lipid rafts via the actin-binding protein EZRIN, which confines unstimulated BCRs within lipid rafts. BCR ligation discharges the ARID3A-EZRIN complex from lipid rafts, allowing the BCR to initiate downstream signaling events. The ARID3A-EZRIN interaction occurs almost exclusively within unpolymerized G-actin, where EZRIN interacts with the multifunctional ARID3A REKLES domain. These observations provide a mechanism by which a transcription factor directly regulates BCR signaling via linkage to the actin cytoskeleton with consequences for B-cell-related neoplasia.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy

Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.

Conformational gating in ammonia lyases

BACKGROUND

Ammonia lyases are enzymes of industrial and biomedical interest. Knowledge of structure-dynamics-function relationship in ammonia lyases is instrumental for exploiting the potential of these enzymes in industrial or biomedical applications.

METHODS

We investigated the conformational changes in the proximity of the catalytic pocket of a 3-methylaspartate ammonia lyase (MAL) as a model system. At this scope, we used microsecond all-atom molecular dynamics simulations, analyzed with dimensionality reduction techniques, as well as in terms of contact networks and correlated motions.

RESULTS

We identify two regulatory elements in the MAL structure, i.e., the β5-α2 loop and the helix-hairpin-loop subdomain. These regulatory elements undergo conformational changes switching from 'occluded' to 'open' states. The rearrangements are coupled to changes in the accessibility of the active site. The β5-α2 loop and the helix-hairpin-loop subdomain modulate the formation of tunnels from the protein surface to the catalytic site, making the active site more accessible to the substrate when they are in an open state.

CONCLUSIONS

Our work pinpoints a sequential mechanism, in which the helix-hairpin-loop subdomain of MAL needs to break a subset of intramolecular interactions first to favor the displacement of the β5-α2 loop. The coupled conformational changes of these two elements contribute to modulate the accessibility of the catalytic site.

GENERAL SIGNIFICANCE

Similar molecular mechanisms can have broad relevance in other ammonia lyases with similar regulatory loops. Our results also imply that it is important to account for protein dynamics in the design of variants of ammonia lyases for industrial and biomedical applications.

Predictive compression of molecular dynamics trajectories

Molecular dynamics simulations help to understand the complex behavior of molecules. The output of such a simulation describes the trajectories of individual atoms as snapshots of atom positions in time. Many compression schemes were developed to reduce the amount of data needed for storing long trajectories. This is achieved by limiting the precision of coordinates, encoding differences instead of absolute values, dimensionality reduction by principal component analysis, or by using polynomials approximating vertex trajectories. However, compression schemes using actual bonds between atoms have not been utilized to their full potential. Therefore, we developed a lossy compression method that captures the local, mostly rotational movement of atoms with respect to their bonded neighbors and predicts their positions in each frame. This allows full control over the data distortion. In our experiments, the method achieves data rates which are substantially better than the rates achieved by competing methods at the same error level.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Analyzing Biomolecular Ensembles

Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.

Effects of Distal Mutations on the Structure, Dynamics and Catalysis of Human Monoacylglycerol Lipase

An understanding of how conformational dynamics modulates function and catalysis of human monoacylglycerol lipase (hMGL), an important pharmaceutical target, can facilitate the development of novel ligands with potential therapeutic value. Here, we report the discovery and characterization of an allosteric, regulatory hMGL site comprised of residues Trp-289 and Leu-232 that reside over 18 Å away from the catalytic triad. These residues were identified as critical mediators of long-range communication and as important contributors to the integrity of the hMGL structure. Nonconservative replacements of Trp-289 or Leu-232 triggered concerted motions of structurally distinct regions with a significant conformational shift toward inactive states and dramatic loss in catalytic efficiency of the enzyme. Using a multimethod approach, we show that the dynamically relevant Trp-289 and Leu-232 residues serve as communication hubs within an allosteric protein network that controls signal propagation to the active site, and thus, regulates active-inactive interconversion of hMGL. Our findings provide new insights into the mechanism of allosteric regulation of lipase activity, in general, and may provide alternative drug design possibilities.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

Hybrid Voronoi diagrams, their computation and reduction for applications in computational biochemistry

Geometric models of molecular structures are often described as a set of balls, where balls represent individual atoms. The ability to describe and explore the empty space among these balls is important, e.g., in the analysis of the interaction of enzymes with substrates, ligands and solvent molecules. Voronoi diagrams from the field of computational geometry are often used here, because they provide a mathematical description of how the whole space can be divided into regions assigned to individual atoms. This paper introduces a combination of two different types of Voronoi diagrams into a new hybrid Voronoi diagram - one part of this diagram belongs to the additively weighted (aw-Voronoi) diagram and the other to the power diagram. The boundary between them is controlled by a user-defined constant (the probe radius). Both parts are computed by different algorithms, which are already known. The reduced aw-Voronoi diagram is then obtained by removing the power diagram part from the hybrid diagram. Reduced aw-Voronoi diagrams are perfectly tailored for the analysis of dynamic molecular structures, their computation is faster and storage requirements are lower than in the case of complete aw-Voronoi diagrams. Here, we showed their application to key proteins in cancer research such as p53 and ARID proteins as case study. We identified a biologically relevant cavity in p53 structural ensembles generated by molecular dynamics simulations and analyzed its accessibility, attesting the potential of our approach. This method is relevant for cancer research since it permits to depict a dynamical view of cavities and pockets in proteins that could be affected by mutations in the disease. Our approach opens novel prospects for the study of cancer-related proteins by molecular simulations and the identification of novel targets for drug design.

The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer

SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.

DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions

Binding-induced conformational changes of a protein at regions distant from the binding site may play crucial roles in protein function and regulation. The p53 tumour suppressor is an example of such an allosterically regulated protein. Little is known, however, about how DNA binding can affect distal sites for transcription factors. Furthermore, the molecular details of how a local perturbation is transmitted through a protein structure are generally elusive and occur on timescales hard to explore by simulations. Thus, we employed state-of-the-art enhanced sampling atomistic simulations to unveil DNA-induced effects on p53 structure and dynamics that modulate the recruitment of cofactors and the impact of phosphorylation at Ser215. We show that DNA interaction promotes a conformational change in a region 3 nm away from the DNA binding site. Specifically, binding to DNA increases the population of an occluded minor state at this distal site by more than 4-fold, whereas phosphorylation traps the protein in its major state. In the minor conformation, the interface of p53 that binds biological partners related to p53 transcription-independent functions is not accessible. Significantly, our study reveals a mechanism of DNA-mediated protection of p53 from interactions with partners involved in the p53 transcription-independent signalling. This also suggests that conformational dynamics is tightly related to p53 signalling.

The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B

Ras mediates cell proliferation, survival and differentiation. Mutations in K-Ras4B are predominant at residues G12, G13 and Q61. Even though all impair GAP-assisted GTP → GDP hydrolysis, the mutation frequencies of K-Ras4B in human cancers vary. Here we aim to figure out their mechanisms and differential oncogenicity. In total, we performed 6.4 μs molecular dynamics simulations on the wild-type K-Ras4B (K-Ras4B(WT)-GTP/GDP) catalytic domain, the K-Ras4B(WT)-GTP-GAP complex, and the mutants (K-Ras4B(G12C/G12D/G12V)-GTP/GDP, K-Ras4B(G13D)-GTP/GDP, K-Ras4B(Q61H)-GTP/GDP) and their complexes with GAP. In addition, we simulated 'exchanged' nucleotide states. These comprehensive simulations reveal that in solution K-Ras4B(WT)-GTP exists in two, active and inactive, conformations. Oncogenic mutations differentially elicit an inactive-to-active conformational transition in K-Ras4B-GTP; in K-Ras4B(G12C/G12D)-GDP they expose the bound nucleotide which facilitates the GDP-to-GTP exchange. These mechanisms may help elucidate the differential mutational statistics in K-Ras4B-driven cancers. Exchanged nucleotide simulations reveal that the conformational transition is more accessible in the GTP-to-GDP than in the GDP-to-GTP exchange. Importantly, GAP not only donates its R789 arginine finger, but stabilizes the catalytically-competent conformation and pre-organizes catalytic residue Q61; mutations disturb the R789/Q61 organization, impairing GAP-mediated GTP hydrolysis. Together, our simulations help provide a mechanistic explanation of key mutational events in one of the most oncogenic proteins in cancer.

Dissecting protein architecture with communication blocks and communicating segment pairs

BACKGROUND

Proteins adapt to environmental conditions by changing their shape and motions. Characterising protein conformational dynamics is increasingly recognised as necessary to understand how proteins function. Given a conformational ensemble, computational tools are needed to extract in a systematic way pertinent and comprehensive biological information.

RESULTS

Here, we present a method, Communication Mapping (COMMA), to decipher the dynamical architecture of a protein. The method first extracts residue-based dynamic properties from all-atom molecular dynamics simulations. Then, it integrates them in a graph theoretic framework, where it identifies groups of residues or protein regions that mediate short- and long-range communication. COMMA introduces original concepts to contrast the different roles played by these regions, namely communication blocks and communicating segment pairs, and evaluates the connections and communication strengths between them. We show the utility and capabilities of COMMA by applying it to three archetypal proteins, namely protein A, the tyrosine kinase KIT and the tumour suppressor p53.

CONCLUSION

Our method permits to compare in a direct way the dynamical behaviour either of proteins with different characteristics or of the same protein in different conditions. It is useful to identify residues playing a key role in protein allosteric regulation and to explain the effects of deleterious mutations in a mechanistic way. COMMA is a fully automated tool with broad applicability. It is freely available to the community at www.lcqb.upmc.fr/COMMA .

E2 superfamily of ubiquitin-conjugating enzymes: constitutively active or activated through phosphorylation in the catalytic cleft

Protein phosphorylation is a modification that offers a dynamic and reversible mechanism to regulate the majority of cellular processes. Numerous diseases are associated with aberrant regulation of phosphorylation-induced switches. Phosphorylation is emerging as a mechanism to modulate ubiquitination by regulating key enzymes in this pathway. The molecular mechanisms underpinning how phosphorylation regulates ubiquitinating enzymes, however, are elusive. Here, we show the high conservation of a functional site in E2 ubiquitin-conjugating enzymes. In catalytically active E2s, this site contains aspartate or a phosphorylatable serine and we refer to it as the conserved E2 serine/aspartate (CES/D) site. Molecular simulations of substrate-bound and -unbound forms of wild type, mutant and phosphorylated E2s, provide atomistic insight into the role of the CES/D residue for optimal E2 activity. Both the size and charge of the side group at the site play a central role in aligning the substrate lysine toward E2 catalytic cysteine to control ubiquitination efficiency. The CES/D site contributes to the fingerprint of the E2 superfamily. We propose that E2 enzymes can be divided into constitutively active or regulated families. E2s characterized by an aspartate at the CES/D site signify constitutively active E2s, whereas those containing a serine can be regulated by phosphorylation.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to improvements in force-field accuracy, along with in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes. Several metrics have been developed to describe correlated motions in the MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer time scales than the ones that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations, especially for comparative purposes, requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalized Frobenius norm of the differences between protein correlation maps, to compare correlated motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used, and we compared our index to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya Coefficient.

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.