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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 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.

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.

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.

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.

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.

Influenza hemaggutinin (HA), a trimeric membrane fusion glycoprotein, guides the invasion of flu viruses into its host cells through endocytosis after binding to their receptors. During this process, a reduced endosomal pH triggers a large-scale structural rearrangement of HA2, the evolutionarily conserved stem domain of HA. This structural change is responsible for the fusion of the viral and the host membranes, facilitating the subsequent delivery of viral genome. Although the end states structures of the HA2 transition have been crystallized, the molecular details of this transition is unknown. To map out the overall picture, here we employ a combination of both coarse-grained and explicit solvent molecular dynamics simulations with the aim of understanding the underlying mechanism of membrane fusion. Our simulations suggest that the overall transition is separated into a fast phase and a slow phase. At the fast phase, HA2 experiences a fast order-disorder transition at a junction domain (Loop3-4) and reaches an asymmetric configuration. In the subsequent slow phase HA2 starts two transition pathways. Also, the results reveal that a reduction in pH destabilizes HA2 by protonating some key residues near its fusion peptides. Finally, a timescale competition among different domains of HA2 is shown to be necessary for HA function.