Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA

Joint Efforts of Replicative Helicase and SSB Ensure Inherent Replicative Tolerance of G-Quadruplex

G-quadruplex (G4) is a four-stranded noncanonical DNA structure that has long been recognized as a potential hindrance to DNA replication. However, how replisomes effectively deal with G4s to avoid replication failure is still obscure. Here, using single-molecule and ensemble approaches, the consequence of the collision between bacteriophage T7 replisome and an intramolecular G4 located on either the leading or lagging strand is examined. It is found that the adjacent fork junctions induced by G4 formation incur the binding of T7 DNA polymerase (DNAP). In addition to G4, these inactive DNAPs present insuperable obstacles, impeding the progression of DNA synthesis. Nevertheless, T7 helicase can dismantle them and resolve lagging-strand G4s, paving the way for the advancement of the replication fork. Moreover, with the assistance of the single-stranded DNA binding protein (SSB) gp2.5, T7 helicase is also capable of maintaining a leading-strand G4 structure in an unfolded state, allowing for a fraction of T7 DNAPs to synthesize through without collapse. These findings broaden the functional repertoire of a replicative helicase and underscore the inherent G4 tolerance of a replisome.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Coarse-grained models allow computational investigation of biomolecular processes occurring on long time and length scales, intractable with atomistic simulation. Traditionally, many coarse-grained models rely mostly on pairwise interaction potentials. However, the decimation of degrees of freedom should, in principle, lead to a complex many-body effective interaction potential. In this work, we use experimental data on mutant stability to parametrize coarse-grained models for two proteins with and without many-body terms. We demonstrate that many-body terms are necessary to reproduce quantitatively the effects of point mutations on protein stability, particularly to implicitly take into account the effect of the solvent.

Machines on Genes through the Computational Microscope

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

The marionette mechanism of domain-domain communication in the antagonist, agonist, and coactivator responses of the estrogen receptor

The human estrogen receptor α (hERα) is involved in the regulation of growth, development, and tissue homeostasis. Agonists that bind to the receptor's ligand-binding domain (LBD) lead to recruitment of coactivators and the enhancement of gene expression. In contrast, antagonists bind to the LBD and block the binding of coactivators thus decreasing gene expressions. In this work, we carry out simulations using the AWSEM (Associative memory, Water mediated, Structure and Energy Model)-Suite force field along with the 3SPN.2C force field for DNA to predict the structure of hERα and study its dynamics when binding to DNA and coactivators. Using simulations of antagonist-bound hERα and agonist-bound hERα by themselves and also along with bound DNA and coactivators, principal component analyses and free energy landscape analyses capture the pathway of domain-domain communication for agonist-bound hERα. This communication is mediated through the hinge domains that are ordinarily intrinsically disordered. These disordered segments manipulate the hinge domains much like the strings of a marionette as they twist in different ways when antagonists or agonists are bound to the ligand-binding domain.

Unravelling How Single-Stranded DNA Binding Protein Coordinates DNA Metabolism Using Single-Molecule Approaches

Single-stranded DNA-binding proteins (SSBs) play vital roles in DNA metabolism. Proteins of the SSB family exclusively and transiently bind to ssDNA, preventing the DNA double helix from re-annealing and maintaining genome integrity. In the meantime, they interact and coordinate with various proteins vital for DNA replication, recombination, and repair. Although SSB is essential for DNA metabolism, proteins of the SSB family have been long described as accessory players, primarily due to their unclear dynamics and mechanistic interaction with DNA and its partners. Recently-developed single-molecule tools, together with biochemical ensemble techniques and structural methods, have enhanced our understanding of the different coordination roles that SSB plays during DNA metabolism. In this review, we discuss how single-molecule assays, such as optical tweezers, magnetic tweezers, Förster resonance energy transfer, and their combinations, have advanced our understanding of the binding dynamics of SSBs to ssDNA and their interaction with other proteins partners. We highlight the central coordination role that the SSB protein plays by directly modulating other proteins' activities, rather than as an accessory player. Many possible modes of SSB interaction with protein partners are discussed, which together provide a bigger picture of the interaction network shaped by SSB.

Structural and dynamic basis of DNA capture and translocation by mitochondrial Twinkle helicase

Twinkle is a mitochondrial replicative helicase which can self-load onto and unwind mitochondrial DNA. Nearly 60 mutations on Twinkle have been linked to human mitochondrial diseases. Using cryo-electron microscopy (cryo-EM) and high-speed atomic force microscopy (HS-AFM), we obtained the atomic-resolution structure of a vertebrate Twinkle homolog with DNA and captured in real-time how Twinkle is self-loaded onto DNA. Our data highlight the important role of the non-catalytic N-terminal domain of Twinkle. The N-terminal domain directly contacts the C-terminal helicase domain, and the contact interface is a hotspot for disease-related mutations. Mutations at the interface destabilize Twinkle hexamer and reduce helicase activity. With HS-AFM, we observed that a highly dynamic Twinkle domain, which is likely to be the N-terminal domain, can protrude ∼5 nm to transiently capture nearby DNA and initialize Twinkle loading onto DNA. Moreover, structural analysis and subunit doping experiments suggest that Twinkle hydrolyzes ATP stochastically, which is distinct from related helicases from bacteriophages.