The Characterization of Ancient Methanococcales Malate Dehydrogenases Reveals That Strong Thermal Stability Prevents Unfolding Under Intense γ-Irradiation.

Malate dehydrogenases (MalDHs) (EC.1.1.

P-Collabs: Investigating Counterion-Mediated Bridges in the Multiply Phosphorylated Tau-R2 Repeat.

Tau is an intrinsically disordered (IDP) microtubule-associated protein (MAP) that plays a key part in microtubule assembly and organization. The function of tau can be regulated by multiple phosphorylation sites. These post-translational modifications are known to decrease the binding affinity of tau for microtubules, and abnormal tau phosphorylation patterns are involved in Alzheimer's disease.

A Perspective on the Prospective Use of AI in Protein Structure Prediction.

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction.

It Takes Tau to Tango: Investigating the Fuzzy Interaction between the R2-Repeat Domain and Tubulin C-Terminal Tails.

The microtubule-associated protein (MAP) tau plays a key role in the regulation of microtubule assembly and spatial organization. Tau hyperphosphorylation affects its binding on the tubulin surface and has been shown to be involved in several pathologies such as Alzheimer's disease. As the tau binding site on the microtubule lays close to the disordered and highly flexible tubulin C-terminal tails (CTTs), these are likely to impact the tau-tubulin interaction.

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology.

The recent breakthrough made in the field of three-dimensional (3D) structure prediction by artificial intelligence softwares, such as initially AlphaFold2 (AF2) and RosettaFold (RF) and more recently large Language Models (LLM), has revolutionized the field of structural biology in particular and also biology as a whole. These models have clearly generated great enthusiasm within the scientific community, and different applications of these 3D predictions are regularly described in scientific articles, demonstrating the impact of these high-quality models. Despite the acknowledged high accuracy of these models in general, it seems important to make users of these models aware of the wealth of information they offer and to encourage them to make the best use of them.

Conformational and mechanical stability of the isolated large subunit of membrane-bound [NiFe]-hydrogenase from .

Comprising at least a bipartite architecture, the large subunit of [NiFe]-hydrogenase harbors the catalytic nickel-iron site while the small subunit houses an array of electron-transferring Fe-S clusters. Recently, some [NiFe]-hydrogenase large subunits have been isolated showing an intact and redox active catalytic cofactor. In this computational study we have investigated one of these metalloproteins, namely the large subunit HoxG of the membrane-bound hydrogenase from (MBH), targeting its conformational and mechanical stability using molecular modelling and long all-atom Gaussian accelerated molecular dynamics (GaMD).

Design - a new way to look at old molecules.

We discuss how design enriches molecular science, particularly structural biology and bioinformatics. We present two use cases, one in academic practice and the other to design for outreach. The first case targets the representation of ion channels and their dynamic properties.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces.

Between Two Walls: Modeling the Adsorption Behavior of β-Glucosidase A on Bare and SAM-Functionalized Gold Surfaces.

The efficient immobilization of enzymes on surfaces remains a complex but central issue in the biomaterials field, which requires us to understand this process at the atomic level. Using a multiscale approach combining all-atom molecular dynamics and coarse-grain Brownian dynamics simulations, we investigated the adsorption behavior of β-glucosidase A (βGA) on bare and self-assembled monolayer (SAM)-functionalized gold surfaces. We monitored the enzyme position and orientation during the molecular dynamics (MD) trajectories and measured the contacts it forms with both surfaces.

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes.

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information.

Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces.

The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface.

Implicit Modeling of the Impact of Adsorption on Solid Surfaces for Protein Mechanics and Activity with a Coarse-Grained Representation.

Surface immobilized enzymes play a key role in numerous biotechnological applications such as biosensors, biofuel cells, or biocatalytic synthesis. As a consequence, the impact of adsorption on the enzyme structure, dynamics, and function needs to be understood on the molecular level as it is critical for the improvement of these technologies. With this perspective in mind, we used a theoretical approach for investigating local protein flexibility on the residue scale that couples a simplified protein representation with an elastic network and Brownian dynamics simulations.

Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners.

In the crowded cell, a strong selective pressure operates on the proteome to limit the competition between functional and non-functional protein-protein interactions. We developed an original theoretical framework in order to interrogate how this competition constrains the behavior of proteins with respect to their partners or random encounters. Our theoretical framework relies on a two-dimensional (2D) representation of interaction energy landscapes, with 2D energy maps, which reflect in a synthetic way the spatial distribution of the interaction propensity of a protein surface for another protein.

Coarse-grain simulations on NMR conformational ensembles highlight functional residues in proteins.

Dynamics are a key feature of protein function, and this is especially true of gating residues, which occupy cavity or tunnel lining positions in the protein structure, and will reversibly switch between open and closed conformations in order to control the diffusion of small molecules within a protein's internal matrix. Earlier work on globins and hydrogenases have shown that these gating residues can be detected using a multiscale scheme combining all-atom classic molecular dynamics simulations and coarse-grain calculations of the resulting conformational ensemble mechanical properties. Here, we show that the structural variations observed in the conformational ensembles produced by NMR spectroscopy experiments are sufficient to induce noticeable mechanical changes in a protein, which in turn can be used to identify residues important for function and forming a mechanical nucleus in the protein core.

Conformational Stability Adaptation of a Double-Stranded RNA-Binding Domain to Transfer RNA Ligand.

The double-stranded RNA-binding domain (dsRBD) is a broadly distributed domain among RNA-maturing enzymes. Although this domain recognizes dsRNA's structures via a conserved canonical structure adopting an α-βββ-α topology, several dsRBDs can accommodate discrete structural extensions expanding further their functional repertoire. How these structural elements engage cooperative communications with the canonical structure and how they contribute to the dsRBD's overall folding are poorly understood.

Mechanical variations in proteins with large-scale motions highlight the formation of structural locks.

Protein function depends just as much on flexibility as on structure, and in numerous cases, a protein's biological activity involves transitions that will impact both its conformation and its mechanical properties. Here, we use a coarse-grain approach to investigate the impact of structural changes on protein flexibility. More particularly, we focus our study on proteins presenting large-scale motions.

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions.

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites.

Meet-U: Educating through research immersion.

We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 people and have to realize a project from A to Z that answers a challenging question in biology.

Determinants of neuroglobin plasticity highlighted by joint coarse-grained simulations and high pressure crystallography.

Investigating the effect of pressure sheds light on the dynamics and plasticity of proteins, intrinsically correlated to functional efficiency. Here we detail the structural response to pressure of neuroglobin (Ngb), a hexacoordinate globin likely to be involved in neuroprotection. In murine Ngb, reversible coordination is achieved by repositioning the heme more deeply into a large internal cavity, the "heme sliding mechanism".

Mobility and Core-Protein Binding Patterns of Disordered C-Terminal Tails in β-Tubulin Isotypes.

Although they play a significant part in the regulation of microtubule structure, dynamics, and function, the disordered C-terminal tails of tubulin remain invisible to experimental structural methods and do not appear in the crystallographic structures that are currently available in the Protein Data Bank. Interestingly, these tails concentrate most of the sequence variability between tubulin isotypes and are the sites of the principal post-translational modifications undergone by this protein. Using homology modeling, we developed two complete models for the human αI/βI- and αI/βIII-tubulin isotypes that include their C-terminal tails.

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface.

Fold and flexibility: what can proteins' mechanical properties tell us about their folding nucleus?

The determination of a protein's folding nucleus, i.e. a set of native contacts playing an important role during its folding process, remains an elusive yet essential problem in biochemistry.

Docking Peptides on Proteins: How to Open a Lock, in the Dark, with a Flexible Key.

In this issue of Structure, Schindler et al. (2015b) present us with pepATTRACT, a protocol embedded in the ATTRACT docking engine for fully blind flexible peptide docking on proteins that yields high quality models of complexes.