ATLAS: protein flexibility description from atomistic molecular dynamics simulations.

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Poincaré maps for visualization of large protein families.

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA.

ICARUS: flexible protein structural alignment based on Protein Units.

MOTIVATION: Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. RESULTS: We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. AVAILABILITY AND IMPLEMENTATION: Code is freely available online at https://github.com/DSIMB/ICARUS.

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.

SWORD2: hierarchical analysis of protein 3D structures.

Understanding the functions and origins of proteins requires splitting these macromolecules into fragments that could be independent in terms of folding, activity, or evolution. For that purpose, structural domains are the typical level of analysis, but shorter segments, such as subdomains and supersecondary structures, are insightful as well. Here, we propose SWORD2, a web server for exploring how an input protein structure may be decomposed into 'Protein Units' that can be hierarchically assembled to delimit structural domains. For each partitioning solution, the relevance of the identified substructures is estimated through different measures. This multilevel analysis is achieved by integrating our previous work on domain delineation, 'protein peeling' and model quality assessment. We hope that SWORD2 will be useful to biologists searching for key regions in their proteins of interest and to bioinformaticians building datasets of protein structures. The web server is freely available online: https://www.dsimb.inserm.fr/SWORD2.

PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction.

Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.

ABCG2 Is Overexpressed on Red Blood Cells in Ph-Negative Myeloproliferative Neoplasms and Potentiates Ruxolitinib-Induced Apoptosis.

Myeloproliferative neoplasms (MPNs) are a group of disorders characterized by clonal expansion of abnormal hematopoietic stem cells leading to hyperproliferation of one or more myeloid lineages. The main complications in MPNs are high risk of thrombosis and progression to myelofibrosis and leukemia. MPN patients with high risk scores are treated by hydroxyurea (HU), interferon-α, or ruxolitinib, a tyrosine kinase inhibitor. Polycythemia vera (PV) is an MPN characterized by overproduction of red blood cells (RBCs). ABCG2 is a member of the ATP-binding cassette superfamily transporters known to play a crucial role in multidrug resistance development. Proteome analysis showed higher ABCG2 levels in PV RBCs compared to RBCs from healthy controls and an additional increase of these levels in PV patients treated with HU, suggesting that ABCG2 might play a role in multidrug resistance in MPNs. In this work, we explored the role of ABCG2 in the transport of ruxolitinib and HU using human cell lines, RBCs, and in vitro differentiated erythroid progenitors. Using stopped-flow analysis, we showed that HU is not a substrate for ABCG2. Using transfected K562 cells expressing three different levels of recombinant ABCG2, MPN RBCs, and cultured erythroblasts, we showed that ABCG2 potentiates ruxolitinib-induced cytotoxicity that was blocked by the ABCG2-specific inhibitor KO143 suggesting ruxolitinib intracellular import by ABCG2. In silico modeling analysis identified possible ruxolitinib-binding site locations within the cavities of ABCG2. Our study opens new perspectives in ruxolitinib efficacy research targeting cell types depending on ABCG2 expression and polymorphisms among patients.

Explicit measurement of the endotoxin adsorption efficiency detects non-Langmuir behavior at low concentrations.

Lipopolysaccharides (LPS) are the Gram-negative bacteria cell wall components capable to induce the system inflammatory response even at picomolar concentrations. LPS detection at these concentrations is necessary to develop new sorbents for the efficient purification of the biological fluids. LAL-test widely used for LPS concentration estimation is based on the LPS biological activity measurement and thus may depend on the LPS concentration in a non-linear way. Here we propose a new explicit method for the LPS concentration measurement based on fluorescently labeled LPS and direct photon counting and develop the new protocol for LPS adsorption efficiency measurement. Following the suggested protocol in the experiments on novel sorbents, we demonstrate that LPS adsorption at small biologically relevant concentrations is non-Langmuir.

Coupled rows of PBS cores and PSII dimers in cyanobacteria: symmetry and structure.

Phycobilisome (PBS) is a giant water-soluble photosynthetic antenna transferring the energy of absorbed light mainly to the photosystem II (PSII) in cyanobacteria. Under the low light conditions, PBSs and PSII dimers form coupled rows where each PBS is attached to the cytoplasmic surface of PSII dimer, and PBSs come into contact with their face surfaces (state 1). The model structure of the PBS core that we have developed earlier by comparison and combination of different fine allophycocyanin crystals, as reported in Zlenko et al. (Photosynth Res 130(1):347-356, 2016b), provides a natural way of the PBS core face-to-face stacking. According to our model, the structure of the protein-protein contact between the neighboring PBS cores in the rows is the same as the contact between the APC hexamers inside the PBS core. As a result, the rates of energy transfer between the cores can occur, and the row of PBS cores acts as an integral PBS "supercore" providing energy transfer between the individual PBS cores. The PBS cores row pitch in our elaborated model (12.4 nm) is very close to the PSII dimers row pitch obtained by the electron microscopy (12.2 nm) that allowed to unite a model of the PBS cores row with a model of the PSII dimers row. Analyzing the resulting model, we have determined the most probable locations of ApcD and ApcE terminal emitter subunits inside the bottom PBS core cylinders and also revealed the chlorophyll molecules of PSII gathering energy from the PBS.

Conformational Dynamics of the Single Lipopolysaccharide O-Antigen in Solution.

The O-antigen is the most variable and highly immunogenic part of the lipopolysaccharide molecule that covers the surface of Gram-negative bacteria and makes up the first line of cellular defense. To provide insight into the details of the O-antigen arrangement on the membrane surface, we simulated its behavior in solution by molecular dynamics. We developed the energetically favorable O-antigen conformation by analyzing free-energy distributions for its disaccharide fragments. Starting from this conformation, we simulated the behavior of the O-antigen chain on long timescales. Depending on the force field and temperature, the single molecule can undergo reversible or irreversible coil-to-globule transitions. The mechanism of these transitions is related either to the rotation of the carbohydrate residues around O-glycosidic bonds or to flips of the pyranose rings. We found that the presence of rhamnose in the O-antigen chain crucially increases its conformational mobility.

Influence of Antithrombin on the Regimes of Blood Coagulation: Insights from the Mathematical Model.

Blood coagulation is regulated through a complex network of biochemical reactions of blood factors. The main acting enzyme is thrombin whose propagation in blood plasma leads to fibrin clot formation. Spontaneous clot formation is normally controlled through the action of different plasma inhibitors, in particular, through the thrombin binding by antithrombin. In the current study we develop a mathematical model of clot formation both in quiescent plasma and in blood flow and determine the analytical conditions on the antithrombin concentration corresponding to different regimes of blood coagulation.

Molecular dynamics of the human RhD and RhAG blood group proteins.

Introduction: Blood group antigens of the RH system (formerly known as "Rhesus") play an important role in transfusion medicine because of the severe haemolytic consequences of antibodies to these antigens. No crystal structure is available for RhD proteins with its partner RhAG, and the precise stoichiometry of the trimer complex remains unknown. Methods: To analyse their structural properties, the trimers formed by RhD and/or RhAG subunits were generated by protein modelling and molecular dynamics simulations were performed. Results: No major differences in structural behaviour were found between trimers of different compositions. The conformation of the subunits is relatively constant during molecular dynamics simulations, except for three large disordered loops. Discussion: This work makes it possible to propose a reasonable stoichiometry and demonstrates the potential of studying the structural behaviour of these proteins to investigate the hundreds of genetic variants relevant to transfusion medicine.

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy.

Operando wide-field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, the power of unsupervised machine learning (ML) algorithms is harnessed to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH- fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM). The ML procedures also reveal statistically significant patterns of reactivity under dynamic conditions, such as pH acidification. The results align well with a numerical model of chemical communication, underscoring the synergy between data-driven ML and physics-driven FEM approaches.

MEDUSA: Prediction of Protein Flexibility from Sequence.

Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.

New insights into GluT1 mechanics during glucose transfer.

Glucose plays a crucial role in the mammalian cell metabolism. In the erythrocytes and endothelial cells of the blood-brain barrier, glucose uptake is mediated by the glucose transporter type 1 (GluT1). GluT1 deficiency or mutations cause severe physiological disorders. GluT1 is also an important target in cancer therapy as it is overexpressed in tumor cells. Previous studies have suggested that GluT1 mediates solute transfer through a cycle of conformational changes. However, the corresponding 3D structures adopted by the transporter during the transfer process remain elusive. In the present work, we first elucidate the whole conformational landscape of GluT1 in the absence of glucose, using long molecular dynamics simulations and show that the transitions can be accomplished through thermal fluctuations. Importantly, we highlight a strong coupling between intracellular and extracellular domains of the protein that contributes to the transmembrane helices reorientation during the transition. The conformations adopted during the simulations differ from the known 3D bacterial homologs structures resolved in similar states. In holo state simulations, we find that glucose transits along the pathway through significant rotational motions, while maintaining hydrogen bonds with the protein. These persistent motions affect side chains orientation, which impacts protein mechanics and allows glucose progression.

Reaction-diffusion waves of blood coagulation.

One of the main characteristics of blood coagulation is the speed of clot growth. In the current work we consider a mathematical model of the coagulation cascade and study existence, stability and speed of propagation of the reaction-diffusion waves of blood coagulation. We also develop a simplified one-equation model that reflects the main features of the thrombin wave propagation. For this equation we estimate the wave speed analytically. The resulting formulas provide a good approximation for the speed of wave propagation in a more complex model as well as for the experimental data.

Optimal radiation fractionation for low-grade gliomas: Insights from a mathematical model.

We study optimal radiotherapy fractionations for low-grade glioma using mathematical models. Both space-independent and space-dependent models are studied. Two different optimization criteria have been developed, the first one accounting for the global effect of the tumor mass on the disease symptoms and the second one related to the delay of the malignant transformation of the tumor. The models are studied theoretically and numerically using the method of feasible directions. We have searched for optimal distributions of the daily doses dj in the standard protocol of 30 fractions using both models and the two different optimization criteria. The optimal results found in all cases are minor deviations from the standard protocol and provide only marginal potential gains. Thus, our results support the optimality of current radiation fractionations over the standard 6 week treatment period. This is also in agreement with the observation that minor variations of the fractionation have failed to provide measurable gains in survival or progression free survival, pointing out to a certain optimality of the current approach.