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. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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. Since the disordered tubulin CTTs are missing from the available experimental structures, we used homology modeling to build two complete models of tubulin heterotrimers with different isotypes for the ß-tubulin subunit (ßI/αI/ßI and ßIII/αI/ßIII). We then performed long timescale classical Molecular Dynamics simulations for the tau-R2/tubulin assembly (in systems with and without CTTs) and analyzed the resulting trajectories to obtain a detailed view of the protein interface in the complex and the impact of the CTTs on the stability of this assembly. Additional analyses of the CTT mobility in the presence, or in the absence, of tau also highlight how tau might modulate the CTT activity as hooks that are involved in the recruitment of several MAPs. In particular, we observe a wrapping phenomenon, where the ß-tubulin CTTs form a loop over tau-R2, thus stabilizing its interaction with the tubulin surface and simultaneously reducing the CTT availability for interactions with other MAPs.

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. Here, we focus on the impact of these models in a specific application by structural biologists using X-ray crystallography. We propose guidelines to prepare models to be used for molecular replacement trials to solve the phase problem. We also encourage colleagues to share as much detail as possible about how they use these models in their research, where the models did not yield correct molecular replacement solutions, and how these predictions fit with their experimental 3D structure. We feel this is important to improve the pipelines using these models and also to get feedback on their overall quality.

Between Two Walls: Modeling the Adsorption Behavior of ß-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 ß-glucosidase A (ßGA) 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. While the adsorption process has little impact on the protein conformation, it can nonetheless perturb its mechanical properties and catalytic activity. Our results show that compared to the SAM-functionalized surface, the adsorption of ßGA on bare gold is more stable, but less specific, and more likely to disrupt the enzyme's function. This observation emphasizes the fact that the structural organization of proteins at the solid interface is a key point when designing devices based on enzyme immobilization, as one must find an acceptable stability-activity trade-off.

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. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

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. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.

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 α1-ß1ß2ß3-α2 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. Here, we addressed these issues using the dsRBD of human dihydrouridine synthase-2 (hDus2) (hDus2-dsRBD) as a model. This dsRBD harbors N- and C-terminal extensions, the former being directly involved in the recognition of tRNA substrate of hDus2. These extensions engage residues that form a long-range hydrophobic network (LHN) outside the RNA-binding interface. We show by coarse-grain Brownian dynamics that the Nt-extension and its residues F359 and Y364 rigidify the major folding nucleus of the canonical structure via an indirect effect. hDus2-dsRBD unfolds following a two-state cooperative model, whereas both F359A and Y364A mutants, designed to destabilize this LHN, unfold irreversibly. Structural and computational analyses show that these mutants are unstable due to an increase in the dynamics of the two extensions favoring solvent exposure of α2-helix and weakening the main folding nucleus rigidity. This LHN appears essential for maintaining a thermodynamic stability of the overall system and eventually a functional conformation for tRNA recognition. Altogether, our findings suggest that functional adaptability of extended dsRBDs is promoted by a cooperative hydrophobic coupling between the extensions acting as effectors and the folding nucleus of the canonical structure.

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. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master's students in bioinformatics and modeling, with protein-protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.

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. We show how calculating directional force constants within residue pairs, and investigating their variation upon protein closure, can lead to the detection of a limited set of residues that form a structural lock in the protein's closed conformation. This lock, which is composed of residues whose side-chains are tightly interacting, highlights a new class of residues that are important for protein function by stabilizing the closed structure, and that cannot be detected using earlier tools like local rigidity profiles or distance variations maps, or alternative bioinformatics approaches, such as coevolution scores.

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. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.

Mobility and Core-Protein Binding Patterns of Disordered C-Terminal Tails in ß-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/ßI- and αI/ßIII-tubulin isotypes that include their C-terminal tails. We then investigated the conformational variability of the two ß-tails using long time-scale classical molecular dynamics simulations that revealed similar features, notably the unexpected presence of common anchoring regions on the surface of the tuulin dimer, but also distinctive mobility or interaction patterns, some of which could be related to the tail lengths and charge distributions. We also observed in our simulations that the C-terminal tail from the ßI isotype, but not the ßIII isotype, formed contacts in the putative binding site of a recently discovered peptide that disrupts microtubule formation in glioma cells. Hindering the binding site in the ßI isotype would be consistent with this peptide's preferential disruption of microtubule formation in glioma, whose cells overexpress ßIII, compared to normal glial cells. While these observations need to be confirmed with more intensive sampling, our study opens new perspectives for the development of isotype-specific chemotherapy drugs.

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. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Investigating the Structural Variability and Binding Modes of the Glioma Targeting NFL-TBS.40-63 Peptide on Tubulin.

NFL-TBS.40-63 is a 24 amino acid peptide corresponding to the tubulin-binding site located on the light neurofilament subunit, which selectively enters glioblastoma cells, where it disrupts their microtubule network and inhibits their proliferation. We investigated its structural variability and binding modes on a tubulin heterodimer using a combination of NMR experiments, docking, and molecular dynamics (MD) simulations. Our results show that, while lacking a stable structure, the peptide preferentially binds on a specific single site located near the ß-tubulin C-terminal end, thus giving us precious hints regarding the mechanism of action of the NFL-TBS.40-63 peptide's antimitotic activity at the molecular level.

Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information.

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/

The weak, fluctuating, dipole moment of membrane-bound hydrogenase from Aquifex aeolicus accounts for its adaptability to charged electrodes.

[NiFe] hydrogenases from Aquifex aeolicus (AaHase) and Desulfovibrio fructosovorans (DfHase) have been mainly studied to characterize physiological electron transfer processes, or to develop biotechnological devices such as biofuel cells. In this context, it remains difficult to control the orientation of AaHases on electrodes to achieve a fast interfacial electron transfer. Here, we study the electrostatic properties of these two proteins based on microsecond-long molecular dynamics simulations that we compare to voltammetry experiments. Our calculations show weak values and large fluctuations of the dipole direction in AaHase compared to DfHase, enabling the AaHase to absorb on both negatively and positively charged electrodes, with an orientation distribution that induces a spread in electron transfer rates. Moreover, we discuss the role of the transmembrane helix of AaHase and show that it does not substantially impact the general features of the dipole moment.

Heme orientation modulates histidine dissociation and ligand binding kinetics in the hexacoordinated human neuroglobin.

Neuroglobin (Ngb) is a globin present in the brain and retina of mammals. This hexacoordinated hemoprotein binds small diatomic molecules, albeit with lower affinity compared with other globins. Another distinctive feature of most mammalian Ngb is their ability to form an internal disulfide bridge that increases ligand affinity. As often seen for prosthetic heme b containing proteins, human Ngb exhibits heme heterogeneity with two alternative heme orientations within the heme pocket. To date, no details are available on the impact of heme orientation on the binding properties of human Ngb and its interplay with the cysteine oxidation state. In this work, we used (1)H NMR spectroscopy to probe the cyanide binding properties of different Ngb species in solution, including wild-type Ngb and the single (C120S) and triple (C46G/C55S/C120S) mutants. We demonstrate that in the disulfide-containing wild-type protein cyanide ligation is fivefold faster for one of the two heme orientations (the A isomer) compared with the other isomer, which is attributed to the lower stability of the distal His64-iron bond and reduced steric hindrance at the bottom of the cavity for heme sliding in the A conformer. We also attribute the slower cyanide reactivity in the absence of a disulfide bridge to the tighter histidine-iron bond. More generally, enhanced internal mobility in the CD loop bearing the disulfide bridge hinders access of the ligand to heme iron by stabilizing the histidine-iron bond. The functional impact of heme disorder and cysteine oxidation state on the properties of the Ngb ligand is discussed.

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. In the second, we document a transition process from a research environment to general-purpose designs. Several testimonials from practitioners are given. By describing the design process of abstracted shapes, exploded views of molecular structures, motion-averaged slices, 360-degree panoramic projections, and experiments with lit sphere shading, we document how designers help make scientific data accessible without betraying its meaning, and how a creative mind adds value over purely data-driven visualizations. A similar conclusion was drawn for public outreach, as we found that comic-book-style drawings are better suited for communicating science to a broad audience.

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

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 Cupriavidus necator (CnMBH), targeting its conformational and mechanical stability using molecular modelling and long all-atom Gaussian accelerated molecular dynamics (GaMD). Our simulations predict that isolated HoxG is stable in aqueous solution and preserves a large portion of its mechanical properties, but loses rigidity in regions around the active site, in contrast to the MBH heterodimer. Inspired by biochemical data showing dimerization of the HoxG protein and IR measurements revealing an increased stability of the [NiFe] cofactor in protein preparations with higher dimer content, corresponding simulations of homodimeric forms were also undertaken. While the monomeric subunit contains several flexible regions, our data predicts a regained rigidity in homodimer models. Furthermore, we computed the electrostatic properties of models obtained by enhanced sampling with GaMD, which displays a significant amount of positive charge at the protein surface, especially in solvent-exposed former dimer interfaces. These data offer novel insights on the way the [NiFe] core is protected from de-assembly and provide hints for enzyme anchoring to surfaces, which is essential information for further investigations on these minimal enzymes.

Enzyme closure and nucleotide binding structurally lock guanylate kinase.

We investigate the conformational dynamics and mechanical properties of guanylate kinase (GK) using a multiscale approach combining high-resolution atomistic molecular dynamics and low-resolution Brownian dynamics simulations. The GK enzyme is subject to large conformational changes, leading from an open to a closed form, which are further influenced by the presence of nucleotides. As suggested by recent work on simple coarse-grained models of apo-GK, we primarily focus on GK's closure mechanism with the aim to establish a detailed picture of the hierarchy and chronology of structural events essential for the enzymatic reaction. We have investigated open-versus-closed, apo-versus-holo, and substrate-versus-product-loaded forms of the GK enzyme. Bound ligands significantly modulate the mechanical and dynamical properties of GK and rigidity profiles of open and closed states hint at functionally important differences. Our data emphasizes the role of magnesium, highlights a water channel permitting active site hydration, and reveals a structural lock that stabilizes the closed form of the enzyme.

Frontier residues lining globin internal cavities present specific mechanical properties.

The internal cavity matrix of globins plays a key role in their biological function. Previous studies have already highlighted the plasticity of this inner network, which can fluctuate with the proteins breathing motion, and the importance of a few key residues for the regulation of ligand diffusion within the protein. In this Article, we combine all-atom molecular dynamics and coarse-grain Brownian dynamics to establish a complete mechanical landscape for six different globins chain (myoglobin, neuroglobin, cytoglobin, truncated hemoglobin, and chains α and ß of hemoglobin). We show that the rigidity profiles of these proteins can fluctuate along time, and how a limited set of residues present specific mechanical properties that are related to their position at the frontier between internal cavities. Eventually, we postulate the existence of conserved positions within the globin fold, which form a mechanical nucleus located at the center of the cavity network, and whose constituent residues are essential for controlling ligand migration in globins.