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Molecular mechanisms that dictate chromatin organizationin vivoare under active investigation, and the extent to which intrinsic interactions contribute to this process remains debatable. A central quantity for evaluating their contribution is the strength of nucleosome-nucleosome binding, which previous experiments have estimated to range from 2 to 14kBT. We introduce an explicit ion model to dramatically enhance the accuracy of residue-level coarse-grained modeling approaches across a wide range of ionic concentrations. This model allows forde novopredictions of chromatin organization and remains computationally efficient, enabling large-scale conformational sampling for free energy calculations. It reproduces the energetics of protein-DNA binding and unwinding of single nucleosomal DNA, and resolves the differential impact of mono and divalent ions on chromatin conformations. Moreover, we showed that the model can reconcile various experiments on quantifying nucleosomal interactions, providing an explanation for the large discrepancy between existing estimations. We predict the interaction strength at physiological conditions to be 9kBT, a value that is nonetheless sensitive to DNA linker length and the presence of linker histones. Our study strongly supports the contribution of physicochemical interactions to the phase behavior of chromatin aggregates and chromatin organization inside the nucleus.

The structure, dynamics, and binding of individual biomolecules have been extensively investigated using single-molecule Förster resonance energy transfer (smFRET) as a ‘spectroscopic ruler.’ The FRET efficiency between a fluorophore pair is used to measure distances in the several nanometer range. Existing approaches to detect closer distances come at the expense of sensitivity to longer distances. Here, we introduce single-molecule acceptor rise-time (smART) FRET that spans closer and longer distances. The acceptor rise time encodes the FRET rate, which scales polynomially with distance and thus has a steep dependence that expands the working range by 50%. High precision and accuracy is achieved through the spectroscopic separation between the rise time and the photophysical fluctuations that obfuscate other FRET readouts. Using the nanoscale sensitivity, we resolved the architectures of DNA bound to the single-stranded binding protein fromE. coli, demonstrating the ability of smART FRET to elucidate the complex behaviors of biomolecules.

Polycomb repressive complex 2 (PRC2) is an essential protein complex that silences gene expression via post-translational modifications of chromatin. This paper combined homology modeling, atomistic and coarse-grained molecular dynamics simulations, and single-molecule force spectroscopy experiments to characterize both its full-length structure and PRC2-DNA interactions. Using free energy calculations with a newly parameterized protein-DNA force field, we studied a total of three potential PRC2 conformations and their impact on DNA binding and bending. Consistent with cryo-EM studies, we found that EZH2, a core subunit of PRC2, provides the primary interface for DNA binding, and its curved surface can induce DNA bending. Our simulations also predicted the C2 domain of the SUZ12 subunit to contact DNA. Multiple PRC2 complexes bind with DNA cooperatively via allosteric communication through the DNA, leading to a hairpin-like looped configuration. Single-molecule experiments support PRC2-mediated DNA looping and the role of AEBP2 in regulating such loop formation. The impact of AEBP2 can be partly understood from its association with the C2 domain, blocking C2 from DNA binding. Our study suggests that accessory proteins may regulate the genomic location of PRC2 by interfering with its DNA interactions., Graphical Abstract Graphical AbstractMultiple PRC2 complexes bind with DNA cooperatively via allosteric communication through the DNA, leading to the formation of hairpin-like looped configurations.

The arrangement of nucleosomes inside chromatin is of extensive interest. While in vitro experiments have revealed the formation of 30 nm fibers, most in vivo studies have failed to confirm their presence in cell nuclei. To reconcile the diverging experimental findings, we characterized chromatin organization using a residue-level coarse-grained model. The computed force–extension curve matches well with measurements from single-molecule experiments. Notably, we found that a dodeca-nucleosome in the two-helix zigzag conformation breaks into structures with nucleosome clutches and a mix of trimers and tetramers under tension. Such unfolded configurations can also be stabilized through trans interactions with other chromatin chains. Our study suggests that unfolding from chromatin fibers could contribute to the irregularity of in vivo chromatin configurations. We further revealed that chromatin segments with fibril or clutch structures engaged in distinct binding modes and discussed the implications of these inter-chain interactions for a potential sol–gel phase transition.

The T cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T cell receptor (TCR) specific to non-self antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate., 10 pages, 4 figures, to be submitted to Physical Review E

The three-dimensional organization of chromatin is expected to play critical roles in regulating genome functions. High-resolution characterization of its structure and dynamics could improve our understanding of gene regulation mechanisms but has remained challenging. Using a near-atomistic model that preserves the chemical specificity of protein-DNA interactions at residue and base-pair resolution, we studied the stability and folding pathways of a tetra-nucleosome. Dynamical simulations performed with an advanced sampling technique uncovered multiple pathways that connect open chromatin configurations with the zigzag crystal structure. Intermediate states along the simulated folding pathways resemble chromatin configurations reported from in situ experiments. We further determined a six-dimensional free energy surface as a function of the inter-nucleosome distances via a deep learning approach. The zigzag structure can indeed be seen as the global minimum of the surface. However, it is not favored by a significant amount relative to the partially unfolded, in situ configurations. Chemical perturbations such as histone H4 tail acetylation and thermal fluctuations can further tilt the energetic balance to stabilize intermediate states. Our study provides insight into the connection between various reported chromatin configurations and has implications on the in situ relevance of the 30 nm fiber., The three-dimensional organization of chromatin plays critical roles in regulating genome function. Here the authors apply a near atomistic model to study the structure and dynamics of the chromatin folding unit - the tetra-nucleosome - to provide insight into how chromatin folds.

The phase problem in X-ray crystallography arises from the fact that only the intensities, and not the phases, of the diffracting electromagnetic waves are measured directly. Molecular replacement can often estimate the relative phases of reflections starting with those derived from a template structure, which is usually a previously solved structure of a similar protein. The key factor in the success of molecular replacement is finding a good template structure. When no good solved template exists, predicted structures based partially on templates can sometimes be used to generate models for molecular replacement, thereby extending the lower bound of structural and sequence similarity required for successful structure determination. Here, the effectiveness is examined of structures predicted by a state-of-the-art prediction algorithm, the Associative memory, Water-mediated, Structure and Energy Model Suite (AWSEM-Suite), which has been shown to perform well in predicting protein structures in CASP13 when there is no significant sequence similarity to a solved protein or only very low sequence similarity to known templates. The performance of AWSEM-Suite structures in molecular replacement is discussed and the results show that AWSEM-Suite performs well in providing useful phase information, often performing better than I-TASSER-MR and the previous algorithm AWSEM-Template.

Significance Polycomb repressive complex 2 (PRC2) is a major epigenetic machinery that maintains transcriptionally silent heterochromatin in the nucleus and plays critical roles in embryonic development and oncogenesis. It is generally thought that PRC2 propagates repressive histone marks by modifying neighboring nucleosomes in strictly linear progression. However, the behavior of PRC2 on native-like chromatin substrates remains incompletely characterized, making the precise mechanism of PRC2-mediated heterochromatin maintenance elusive. Here we use single-molecule force spectroscopy and computational modeling to dissect the interactions between PRC2 and polynucleosome arrays. Our results provide direct evidence that PRC2 can simultaneously engage nonadjacent nucleosome pairs. The demonstration of PRC2's ability to bridge noncontiguous chromosomal segments furthers our understanding of how Polycomb complexes spread epigenetic modifications and compact chromatin., Polycomb repressive complex 2 (PRC2) installs and spreads repressive histone methylation marks on eukaryotic chromosomes. Because of the key roles that PRC2 plays in development and disease, how this epigenetic machinery interacts with DNA and nucleosomes is of major interest. Nonetheless, the mechanism by which PRC2 engages with native-like chromatin remains incompletely understood. In this work, we employ single-molecule force spectroscopy and molecular dynamics simulations to dissect the behavior of PRC2 on polynucleosome arrays. Our results reveal an unexpectedly diverse repertoire of PRC2 binding configurations on chromatin. Besides reproducing known binding modes in which PRC2 interacts with bare DNA, mononucleosomes, and adjacent nucleosome pairs, our data also provide direct evidence that PRC2 can bridge pairs of distal nucleosomes. In particular, the “1–3” bridging mode, in which PRC2 engages two nucleosomes separated by one spacer nucleosome, is a preferred low-energy configuration. Moreover, we show that the distribution and stability of different PRC2–chromatin interaction modes are modulated by accessory subunits, oncogenic histone mutations, and the methylation state of chromatin. Overall, these findings have implications for the mechanism by which PRC2 spreads histone modifications and compacts chromatin. The experimental and computational platforms developed here provide a framework for understanding the molecular basis of epigenetic maintenance mediated by Polycomb-group proteins.

Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire. Predicting binding specificity of T-cell receptors (TCRs) and putative antigens can help improve cancer immunotherapy. Lin et al. propose RACER, which efficiently makes use of supervised machine learning to learn important molecular interactions contributing to TCR–peptide binding.

As a common malignant tumor, colorectal cancer (CRC) has a high incidence. Recent investigations have suggested that although great improvement has been achieved in the survival rate of early-stage CRC patients, the overall survival rate remains low. Mounting reports have proved that lncRNAs take part in the development of various cancers and possess the regulatory functions in cancers. For example, ASB16 antisense RNA 1 (ASB16-AS1) is a poorly researched novel lncRNA whose specific functions in CRC are still unknown. In our research, we discovered that ASB16-AS1 was with high expression in CRC cells. In addition, ASB16-AS1 silencing restrained the proliferation, migration, invasion, and stemness while accelerating cell apoptosis of CRC cells. Mechanism experiments were applied to explore the regulatory mechanism of ASB16-AS1. It turned out that miR-185-5p could interact with ASB16-AS1 and inhibited the progression of CRC cells. TEAD1 (TEA domain transcription factor1) - a major effector of the Hippo signaling was proved to serve as the target of miR-185-5p and promote CRC development. In short, ASB16-AS1 drove the progression of CRC through the regulation of miR-185-5p/TEAD1 axis.

Refining predicted protein structures with all-atom molecular dynamics simulations is one route to producing, entirely by computational means, structural models of proteins that rival in quality those that are determined by X-ray diffraction experiments. Slow rearrangements within the compact folded state, however, make routine refinement of predicted structures by unrestrained simulations infeasible. In this work, we draw inspiration from the fields of metallurgy and blacksmithing, where practitioners have worked out practical means of controlling equilibration by mechanically deforming their samples. We describe a two-step refinement procedure that involves identifying collective variables for mechanical deformations using a coarse-grained model and then sampling along these deformation modes in all-atom simulations. Identifying those low-frequency collective modes that change the contact map the most proves to be an effective strategy for choosing which deformations to use for sampling. The method is tested on 20 refinement targets from the CASP12 competition and is found to induce large structural rearrangements that drive the structures closer to the experimentally determined structures during relatively short all-atom simulations of 50 ns. By examining the accuracy of side-chain rotamer states in subensembles of structures that have varying degrees of similarity to the experimental structure, we identified the reorientation of aromatic side chains as a step that remains slow even when encouraging global mechanical deformations in the all-atom simulations. Reducing the side-chain rotamer isomerization barriers in the all-atom force field is found to further speed up refinement.

Three-dimensional (3D) organization of the human genome plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, and DNA replication. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. However, it faces significant challenges as the human genome consists of over 6 × 10(9) base pairs, a system size that exceeds the capacity of traditional modeling approaches. In this perspective, we review the progress that has been made in modeling the human genome. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. Applications of these models at a near-atomistic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ. We conclude with an outlook on the opportunities and challenges in studying chromosome dynamics.

Quinclorac (QNC) is a highly selective, hormonal, and low-toxic herbicide with a long duration. And the growth and development of subsequent crops are easily affected by QNC accumulated in the soil. In this paper, a QNC-degrading strain D was isolated and screened from the rice paddy soil. Through morphology, physiological and biochemical tests and 16Sr DNA gene analysis, strain D was identified asCellulosimicrobium cellulanssp. And the QNC degradation characteristics of strain D were studied. Under the optimal culture conditions, the QNC-degrading rate was 45.9% after culturing for 21 days. The QNC-degrading efficiency of strain D in the field was evaluated by a simulated pot experiment. The results show that strain D can promote the growth of rice and QNC-degrading effectively. This research could provide a new bacterial species for microbial degradation of QNC and lay a theoretical foundation for further research on QNC remediation.ImportanceAt present, some QNC-degrading bacteria have been isolated from different environments, but there are no reports ofCellulosimicrobium cellulanssp. bacterial that could degrade QNC. In this study, a new QNC-degradation strain was selected from the paddy soil. The degradation characteristics of strain D were studied in detail. The results shown that strain D had a satisfactory quinclorac-degrading efficiency. Two degradation products of QNC by strain D were identified by HPLC-Q-TOF/MS: 3-pyridylacetic acid (138.0548 m/z) and 3-ethylpyridine (108.0805 m/z), which have not been reported before. The strain D had a potential ability of quinclorac-degrading effectively in the quinclorac-polluted paddy field environment.

Made available in DSpace on 2021-06-25T10:59:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Intrinsically disordered proteins (IDPs) lack a rigid three-dimensional structure and populate a polymorphic ensemble of conformations. Because of the lack of a reference conformation, their energy landscape representation in terms of reaction coordinates presents a daunting challenge. Here, our newly developed energy landscape visualization method (ELViM), a reaction coordinate-free approach, shows its prime application to explore frustrated energy landscapes of an intrinsically disordered protein, prostate-associated gene 4 (PAGE4). PAGE4 is a transcriptional coactivator that potentiates the oncogene c-Jun. Two kinases, namely, HIPK1 and CLK2, phosphorylate PAGE4, generating variants phosphorylated at different serine/threonine residues (HIPK1-PAGE4 and CLK2-PAGE4, respectively) with opposing functions. While HIPK1-PAGE4 predominantly phosphorylates Thr51 and potentiates c-Jun, CLK2-PAGE4 hyperphosphorylates PAGE4 and attenuates transactivation. To understand the underlying mechanisms of conformational diversity among different phosphoforms, we have analyzed their atomistic trajectories simulated using AWSEM forcefield, and the energy landscapes were elucidated using ELViM. This method allows us to identify and compare the population distributions of different conformational ensembles of PAGE4 phosphoforms using the same effective phase space. The results reveal a predominant conformational ensemble with an extended C-terminal segment of WT PAGE4, which exposes a functional residue Thr51, implying its potential of undertaking a fly-casting mechanism while binding to its cognate partner. In contrast, for HIPK1-PAGE4, a compact conformational ensemble enhances its population sequestering phosphorylated-Thr51. This clearly explains the experimentally observed weaker affinity of HIPK1-PAGE4 for c-Jun. ELViM appears as a powerful tool, especially to analyze the highly frustrated energy landscape representation of IDPs where appropriate reaction coordinates are hard to apprehend. Center for Theoretical Biological Physics Rice University Department of Chemistry Massachusetts Institute of Technology Department of Medical Oncology and Therapeutics Research City of Hope National Medical Center Department of Chemical Sciences Indian Institute of Science Education and Research Kolkata Departamento de Física Instituto de Biociências Letras e Ciências Exatas Universidade Estadual Paulista (UNESP) Departamento de Física Instituto de Biociências Letras e Ciências Exatas Universidade Estadual Paulista (UNESP)

1AbstractRecent advances in machine learning, bioinformatics and the understanding of the folding problem have enabled efficient predictions of protein structures with moderate accuracy, even for targets when there is little information from templates. All-atom molecular dynamics simulations provide a route to refine such predicted structures, but unguided atomistic simulations, even when lengthy in time, often fail to eliminate incorrect structural features that would allow the structure to become more energetically favorable owing to the necessity of making large scale motions and overcoming energy barriers for side chain repacking. In this study, we show that localizing packing frustration at atomic resolution by examining the statistics of the energetic changes that occur when the local environment of a site is changed allows one to identify the most likely locations of incorrect contacts. The global statistics of atomic resolution frustration in structures that have been predicted using various algorithms provide strong indicators of structural quality when tested over a database of 20 targets from previous CASP experiments. Residues that are more correctly located turn out to be more minimally frustrated than more poorly positioned sites. These observations provide a diagnosis of both global and local quality of predicted structures, and thus can be used as guidance in all-atom refinement simulations of the 20 targets. Refinement simulations guided by atomic packing frustration turn out to be quite efficient and significantly improve the quality of the structures.

Long noncoding RNAs (lncRNAs) have been reported to play a significant role in the occurrence and progression of tumors. In different tumors, they can either act as an oncogene or tumor suppressor via modulating various target mRNAs. OIP5-AS1 belongs to lncRNA family. It has been reported to be involved in the tumorigenesis of some cancers, such as bladder cancer, gastric cancer, and multiple myeloma. However, the role it plays in hepatocellular carcinoma (HCC) remains unclear. This study aims to explore the inherent mechanism of lncRNA OIP5-AS1 in HCC. In the first place, qRT-PCR found that OIP5-AS1 and VEGFA expressions were significantly increased while miR-3163 was obviously reduced in HCC cells and tissues. Next, a series of functional experiments found that knockdown of OIP5-AS1 suppressed HCC cell proliferation, migration and angiogenesis abilities while promoting cell apoptosis simultaneously. Last but not least, miR-3163 inhibition or VEGFA overexpression can reverse the anti-tumor effect of OIP5-AS1. In summary, OIP5-AS1 affects HCC proliferation, metastasis, and angiogenesis in HCC by regulating VEGFA expression through sponging miR-3163.

Current efforts in the proteolysis targeting chimera (PROTAC) field mostly focus on choosing an appropriate E3 ligase for the target protein, improving the binding affinities towards the target protein and the E3 ligase, and optimizing the PROTAC linker. However, due to the large molecular weights of PROTACs, their cellular uptake remains an issue. Through comparing how different warhead chemistry, reversible noncovalent (RNC), reversible covalent (RC), and irreversible covalent (IRC) binders, affects the degradation of Bruton’s Tyrosine Kinase (BTK), we serendipitously discover that cyano-acrylamide-based reversible covalent chemistry can significantly enhance the intracellular accumulation and target engagement of PROTACs and develop RC-1 as a reversible covalent BTK PROTAC with a high target occupancy as its corresponding kinase inhibitor and effectiveness as a dual functional inhibitor and degrader, a different mechanism-of-action for PROTACs. Importantly, this reversible covalent strategy is generalizable to improve other PROTACs, opening a path to enhance PROTAC efficacy., PROTACs have emerged as promising therapeutic agents but their cellular uptake is often inefficient. Here, the authors show that reversible covalent warhead chemistry improves PROTAC intracellular accumulation and target engagement, and develop a dual inhibitor/degrader of Bruton’s tyrosine kinase

Polycomb repressive complex 2 (PRC2) maintains transcriptionally silent heterochromatin by installing and spreading repressive histone methylation marks along nucleosome arrays. Despite extensive research, the mechanism by which PRC2 engages with chromatin remains incompletely understood. Here we employ single-molecule force spectroscopy and molecular dynamics simulations to dissect the interactions of PRC2 with polynucleosome substrates. Our results reveal an unexpectedly diverse repertoire of PRC2 binding configurations on chromatin. Besides interacting with bare DNA, mononucleosomes, and neighboring nucleosome pairs, PRC2 is also found to bridge non-adjacent nucleosomes, an activity associated with chromatin compaction. Furthermore, the distribution and stability of these PRC2–chromatin interaction modes are differentially modulated by accessory PRC2 cofactors, oncogenic histone mutations, and the methylation state of chromatin. Overall, this work provides a paradigm for understanding the physical basis of epigenetic maintenance mediated by Polycomb group proteins.

The atomistic associative memory, water mediated, structure and energy model (AAWSEM) is an efficient coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using structural information derived from all-atom simulations of long segments of the protein. For α helical proteins, the accuracy of structure prediction using AAWSEM has been established previously. In this article, we examine the capability of AAWSEM to predict the structure of α/β proteins. We also elaborate on an iterative approach that uses the structures from a first round of AAWSEM simulation as fragment memories. This iterative scheme improves the quality of the structure prediction and makes the free energy profile more funneled toward native configurations. We explore the use of clustering analyses as a way of evaluating the confidence in various structure prediction models. Clustering using a local relative order parameter (mutual Q) of the predicted structural ensemble turns out to be optimal. The tightest cluster according to mutual Q generally has the most correctly folded structure. Since there is no bioinformatic input, AAWSEM amounts to an ab initio protein structure prediction method that combines the efficiency of coarse-grained simulations with the local structural accuracy that can be achieved from all-atom simulations.

Hemagglutinin (HA), the membrane-bound fusion protein of the Influenza virus, enables the entry of virus into host cells via a structural rearrangement. There is strong evidence that the primary trigger for this rearrangement is the low pH environment of a late endosome. To understand the structural basis and the dynamic consequences of the pH trigger, explicit-solvent molecular dynamics simulations were employed to investigate the initial stages of the HA transition. Our results indicate that lowered pH destabilizes HA and speeds up the dissociation of the fusion peptides (FPs). A buried salt-bridge between the N-terminus and ASP112 of HA stem domain locks the FPs and may act as one of the pH sensors. In line with recent observations from simplified protein models, we find that, after the dissociation of FPs, a structural order-disorder transition in a loop connecting the central coiled-coil to the C-terminal domains produces a highly mobile HA. This motion suggests the existence of a long-lived asymmetric, or “symmetry-broken” intermediate during the HA conformational change. This intermediate conformation is consistent with models of hemifusion, and its early formation during the conformational change has implications for the aggregation seen in HA activity.

Folded proteins show a high degree of structural order and undergo (fairly constrained) collective motions related to their functions. On the other hand, intrinsically disordered proteins (IDPs), while lacking a well-defined three-dimensional structure, do exhibit some structural and dynamical ordering, but are less constrained in their motions than folded proteins. The larger structural plasticity of IDPs emphasizes the importance of entropically driven motions. Many IDPs undergo function-related disorder-to-order transitions driven by their interaction with specific binding partners. As experimental techniques become more sensitive and become better integrated with computational simulations, we are beginning to see how the modest structural ordering and large amplitude collective motions of IDPs endow them with an ability to mediate multiple interactions with different partners in the cell. To illustrate these points, here, we use Prostate-associated gene 4 (PAGE4), an IDP implicated in prostate cancer (PCa) as an example. We first review our previous efforts using molecular dynamics simulations based on atomistic AWSEM to study the conformational dynamics of PAGE4 and how its motions change in its different physiologically relevant phosphorylated forms. Our simulations quantitatively reproduced experimental observations and revealed how structural and dynamical ordering are encoded in the sequence of PAGE4 and can be modulated by different extents of phosphorylation by the kinases HIPK1 and CLK2. This ordering is reflected in changing populations of certain secondary structural elements as well as in the regularity of its collective motions. These ordered features are directly correlated with the functional interactions of WT-PAGE4, HIPK1-PAGE4 and CLK2-PAGE4 with the AP-1 signaling axis. These interactions give rise to repeated transitions between (high HIPK1-PAGE4, low CLK2-PAGE4) and (low HIPK1-PAGE4, high CLK2-PAGE4) cell phenotypes, which possess differing sensitivities to the standard PCa therapies, such as androgen deprivation therapy (ADT). We argue that, although the structural plasticity of an IDP is important in promoting promiscuous interactions, the modulation of the structural ordering is important for sculpting its interactions so as to rewire with agility biomolecular interaction networks with significant functional consequences.

When good structural templates can be identified, template-based modeling is the most reliable way to predict the tertiary structure of proteins. In this study, we combine template-based modeling with a realistic coarse-grained force field, AWSEM, that has been optimized using the principles of energy landscape theory. The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained force field having both transferable tertiary interactions and knowledge-based local-in-sequence interaction terms. We incorporate template information into AWSEM by introducing soft collective biases to the template structures, resulting in a model that we call AWSEM-Template. Structure prediction tests on eight targets, four of which are in the low sequence identity "twilight zone" of homology modeling, show that AWSEM-Template can achieve high-resolution structure prediction. Our results also confirm that using a combination of AWSEM and a template-guided potential leads to more accurate prediction of protein structures than simply using a template-guided potential alone. Free energy profile analyses demonstrate that the soft collective biases to the template effectively increase funneling toward native-like structures while still allowing significant flexibility so as to allow for correction of discrepancies between the target structure and the template. A further stage of refinement using all-atom molecular dynamics augmented with soft collective biases to the structures predicted by AWSEM-Template leads to a further improvement of both backbone and side-chain accuracy by maintaining sufficient flexibility but at the same time discouraging unproductive unfolding events often seen in unrestrained all-atom refinement simulations. The all-atom refinement simulations also reduce patches of frustration of the initial predictions. Some of the backbones found among the structures produced during the initial coarse-grained prediction step already have CE-RMSD values of less than 3 A with 90% or more of the residues aligned to the experimentally solved structure for all targets. All-atom structures generated during the following all-atom refinement simulations, which started from coarse-grained structures that were chosen without reference to any knowledge about the native structure, have CE-RMSD values of less than 2.5 A with 90% or more of the residues aligned for 6 out of 8 targets. Clustering low energy structures generated during the initial coarse-grained annealing picks out reliably structures that are within 1 A of the best sampled structures in 5 out of 8 cases. After the all-atom refinement, structures that are within 1 A of the best sampled structures can be selected using a simple algorithm based on energetic features alone in 7 out of 8 cases.

Significance Influenza hemagglutinin (HA) is the viral membrane protein that guides entry of flu viruses into host cells. A global refolding of the stem domain of HA, H A 2 , is crucial to successful viral membrane fusion, but the molecular mechanism relating this conformational change of H A 2 to its fusogenic properties remains unclear. We use molecular dynamics simulations to quantify the thermodynamic importance of the B loop domain for driving the overall H A 2 rearrangement. We find that the B loop’s loop-to-coiled-coil refolding is not strongly downhill and thus is a possible target for therapeutics. A buried hydrophilic amino acid is implicated in destabilizing the coiled coil, and the sequence divergence at this position may indicate functional differences between group 1 and 2 HAs.

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era., Comment: 13 pages, including 9 figures and 5 appendixes

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequence that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six alpha-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground-up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.

Influenza hemagglutinin (HA), a homotrimeric glycoprotein crucial for membrane fusion, undergoes a large-scale structural rearrangement during viral invasion. X-ray crystallography has shown that the pre- and postfusion configurations of HA2, the membrane-fusion subunit of HA, have disparate secondary, tertiary, and quaternary structures, where some regions are displaced by more than 100 A. To explore structural dynamics during the conformational transition, we studied simulations of a minimally frustrated model based on energy landscape theory. The model combines structural information from both the pre- and postfusion crystallographic configurations of HA2. Rather than a downhill drive toward formation of the central coiled-coil, we discovered an order-disorder transition early in the conformational change as the mechanism for the release of the fusion peptides from their burial sites in the prefusion crystal structure. This disorder quickly leads to a metastable intermediate with a broken threefold symmetry. Finally, kinetic competition between the formation of the extended coiled-coil and C-terminal melting results in two routes from this intermediate to the postfusion structure. Our study reiterates the roles that cracking and disorder can play in functional molecular motions, in contrast to the downhill mechanical interpretations of the “spring-loaded” model proposed for the HA2 conformational transition.