Detecting Hachimoji DNA: An Eight-Building-Block Genetic System with MoS2 and Janus MoSSe Monolayers.

In the pursuit of personalized medicine, the development of efficient, cost-effective, and reliable DNA sequencing technology is crucial. Nanotechnology, particularly the exploration of two-dimensional materials, has opened different avenues for DNA nucleobase detection, owing to their impressive surface-to-volume ratio. This study employs density functional theory with van der Waals corrections to methodically scrutinize the adsorption behavior and electronic band structure properties of a DNA system composed of eight hachimoji nucleotide letters adsorbed on both MoS2 and MoSSe monolayers. Through a comprehensive conformational search, we pinpoint the most favorable adsorption sites, quantifying their adsorption energies and charge transfer properties. The analysis of electronic band structure unveils the emergence of flat bands in close proximity to the Fermi level post-adsorption, a departure from the pristine MoS2 and MoSSe monolayers. Furthermore, leveraging the nonequilibrium Green's function approach, we compute the current-voltage characteristics, providing valuable insights into the electronic transport properties of the system. All hachimoji bases exhibit physisorption with a horizontal orientation on both monolayers. Notably, base G demonstrates high sensitivity on both substrates. The obtained current-voltage (I-V) characteristics, both without and with base adsorption on MoS2 and the Se side of MoSSe, affirm excellent sensing performance. This research significantly advances our understanding of potential DNA sensing platforms and their electronic characteristics, thereby propelling the endeavor for personalized medicine through enhanced DNA sequencing technologies.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Toward α-1,3/4 fucosyltransferases targeted drug discovery: In silico uncovering of promising natural inhibitors of fucosyltransferase 6.

Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.

Secreted Aspartyl Proteinases Targeted Multi-Epitope Vaccine Design for Candida dubliniensis Using Immunoinformatics.

Candida dubliniensis is an opportunistic pathogen associated with oral and invasive fungal infections in immune-compromised individuals. Furthermore, the emergence of C. dubliniensis antifungal drug resistance could exacerbate its treatment. Hence, in this study a multi-epitope vaccine candidate has been designed using an immunoinformatics approach by targeting C. dubliniensis secreted aspartyl proteinases (SAP) proteins. In silico tools have been utilized to predict epitopes and determine their allergic potential, antigenic potential, toxicity, and potential to elicit interleukin-2 (IL2), interleukin-4 (IL4), and IFN-γ. Using the computational tools, eight epitopes have been predicted that were then linked with adjuvants for final vaccine candidate development. Computational immune simulation has depicted that the immunogen designed emerges as a strong immunogenic candidate for a vaccine. Further, molecular docking and molecular dynamics simulation analyses revealed stable interactions between the vaccine candidate and the human toll-like receptor 5 (TLR5). Finally, immune simulations corroborated the promising candidature of the designed vaccine, thus calling for further in vivo investigation.

Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.

Motivation: Protein-protein interactions drive many relevant biological events, such as infection, replication and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers. Results: Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 Matthews' correlation coefficient on the test set, surpassing the state-of-the-art scoring functions. Availability and implementation: Docking models from Benchmark 5 are available at https://doi.org/10.5281/zenodo.4012018. Processed tabular data are available at https://repository.kaust.edu.sa/handle/10754/666961. Google colab is available at https://colab.research.google.com/drive/1vbVrJcQSf6\_C3jOAmZzgQbTpuJ5zC1RP?usp=sharing. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Occurrence and stability of anion-π interactions between phosphate and nucleobases in functional RNA molecules.

We present a systematic structural and energetic characterization of phosphate(OP)-nucleobase anion…π stacking interactions in RNAs. We observed OP-nucleobase stacking contacts in a variety of structural motifs other than regular helices and spanning broadly diverse sequence distances. Apart from the stacking between a phosphate and a guanine or a uracil two-residue upstream in specific U-turns, such interactions in RNA have been scarcely characterized to date. Our QM calculations showed an energy minimum at a distance between the OP atom and the nucleobase plane centroid slightly below 3 Å for all the nucleobases. By sliding the OP atom over the nucleobase plane we localized the optimal mutual positioning of the stacked moieties, corresponding to an energy minimum below -6 kcal•mol-1, for all the nucleobases, consistently with the projections of the OP atoms over the different π-rings we observed in experimental occurrences. We also found that the strength of the interaction clearly correlates with its electrostatic component, pointing to it as the most relevant contribution. Finally, as OP-uracil and OP-guanine interactions represent together 86% of the instances we detected, we also proved their stability under dynamic conditions in model systems simulated by state-of-the art DFT-MD calculations.

Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus.

Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.

A random forest classifier for protein-protein docking models.

Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated 3 × 10 4 docking models for each of the 230 complexes in the protein-protein benchmark, version 5, using three different docking programs (HADDOCK, FTDock and ZDOCK), for a cumulative set of ≈ 7 × 10 6 docking models. Three different machine learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named COnservation Driven Expert System (CoDES). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions. Supplementary information: Supplementary data are available at Bioinformatics Advances online. Software and data availability statement: The docking models are available at https://doi.org/10.5281/zenodo.4012018. The programs underlying this article will be shared on request to the corresponding authors.

D936Y and Other Mutations in the Fusion Core of the SARS-CoV-2 Spike Protein Heptad Repeat 1: Frequency, Geographical Distribution, and Structural Effect.

The crown of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is constituted by its spike (S) glycoprotein. S protein mediates the SARS-CoV-2 entry into the host cells. The "fusion core" of the heptad repeat 1 (HR1) on S plays a crucial role in the virus infectivity, as it is part of a key membrane fusion architecture. While SARS-CoV-2 was becoming a global threat, scientists have been accumulating data on the virus at an impressive pace, both in terms of genomic sequences and of three-dimensional structures. On 15 February 2021, from the SARS-CoV-2 genomic sequences in the GISAID resource, we collected 415,673 complete S protein sequences and identified all the mutations occurring in the HR1 fusion core. This is a 21-residue segment, which, in the post-fusion conformation of the protein, gives many strong interactions with the heptad repeat 2, bringing viral and cellular membranes in proximity for fusion. We investigated the frequency and structural effect of novel mutations accumulated over time in such a crucial region for the virus infectivity. Three mutations were quite frequent, occurring in over 0.1% of the total sequences. These were S929T, D936Y, and S949F, all in the N-terminal half of the HR1 fusion core segment and particularly spread in Europe and USA. The most frequent of them, D936Y, was present in 17% of sequences from Finland and 12% of sequences from Sweden. In the post-fusion conformation of the unmutated S protein, D936 is involved in an inter-monomer salt bridge with R1185. We investigated the effect of the D936Y mutation on the pre-fusion and post-fusion state of the protein by using molecular dynamics, showing how it especially affects the latter one.

Replacing thymine with a strongly pairing fifth Base: A combined quantum mechanics and molecular dynamics study.

The non-natural ethynylmethylpyridone C-nucleoside (W), a thymidine (T) analogue that can be incorporated in oligonucleotides by automated synthesis, has recently been reported to form a high fidelity base pair with adenosine (A) and to be well accommodated in B-DNA duplexes. The enhanced binding affinity for A of W, as compared to T, makes it an ideal modification for biotechnological applications, such as efficient probe hybridization for the parallel detection of multiple DNA strands. In order to complement the experimental study and rationalize the impact of the non-natural W nucleoside on the structure, stability and dynamics of DNA structures, we performed quantum mechanics (QM) calculations along with molecular dynamics (MD) simulations. Consistently with the experimental study, our QM calculations show that the A:W base pair has an increased stability as compared to the natural A:T pair, due to an additional CH-π interaction. Furthermore, we show that mispairing between W and guanine (G) causes a distortion in the planarity of the base pair, thus explaining the destabilization of DNA duplexes featuring a G:W pair. MD simulations show that incorporation of single or multiple consecutive A:W pairs in DNA duplexes causes minor changes to the intra- and inter-base geometrical parameters, while a moderate widening/shrinking of the major/minor groove of the duplexes is observed. QM calculations applied to selected stacks from the MD simulations also show an increased stacking energy for W, over T, with the neighboring bases.

Understanding High-Salt and Cold Adaptation of a Polyextremophilic Enzyme.

The haloarchaeon Halorubrum lacusprofundi is among the few polyextremophilic organisms capable of surviving in one of the most extreme aquatic environments on Earth, the Deep Lake of Antarctica (-18 °C to +11.5 °C and 21-28%, w/v salt content). Hence, H. lacusprofundi has been proposed as a model for biotechnology and astrobiology to investigate potential life beyond Earth. To understand the mechanisms that allow proteins to adapt to both salinity and cold, we structurally (including X-ray crystallography and molecular dynamics simulations) and functionally characterized the ß-galactosidase from H. lacusprofundi (hla_bga). Recombinant hla_bga (produced in Haloferax volcanii) revealed exceptional stability, tolerating up to 4 M NaCl and up to 20% (v/v) of organic solvents. Despite being cold-adapted, hla_bga was also stable up to 60 °C. Structural analysis showed that hla_bga combined increased surface acidity (associated with halophily) with increased structural flexibility, fine-tuned on a residue level, for sustaining activity at low temperatures. The resulting blend enhanced structural flexibility at low temperatures but also limited protein movements at higher temperatures relative to mesophilic homologs. Collectively, these observations help in understanding the molecular basis of a dual psychrophilic and halophilic adaptation and suggest that such enzymes may be intrinsically stable and functional over an exceptionally large temperature range.

Enzymatic Formation of an Artificial Base Pair Using a Modified Purine Nucleoside Triphosphate.

The expansion of the genetic alphabet with additional, unnatural base pairs (UBPs) is an important and long-standing goal in synthetic biology. Nucleotides acting as ligands for the coordination of metal cations have advanced as promising candidates for such an expansion of the genetic alphabet. However, the inclusion of artificial metal base pairs in nucleic acids mainly relies on solid-phase synthesis approaches, and very little is known about polymerase-mediated synthesis. Herein, we report the selective and high yielding enzymatic construction of a silver-mediated base pair (dImC-AgI-dPurP) as well as a two-step protocol for the synthesis of DNA duplexes containing such an artificial metal base pair. Guided by DFT calculations, we also shed light into the mechanism of formation of this artificial base pair as well as into the structural and energetic preferences. The enzymatic synthesis of the dImC-AgI-dPurP artificial metal base pair provides valuable insights for the design of future, more potent systems aiming at expanding the genetic alphabet.

The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses.

BACKGROUND: Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process. In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis. RESULTS: As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as case studies, we illustrate here in detail the approach we adopted. CONCLUSIONS: Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.

Occurrence and stability of lone pair-π and OH-π interactions between water and nucleobases in functional RNAs.

We identified over 1000 instances of water-nucleobase stacking contacts in a variety of RNA molecules from a non-redundant set of crystal structures with resolution ≤3.0 Å. Such contacts may be of either the lone pair-π (lp-π) or the OH-π type, in nature. The distribution of the distances of the water oxygen from the nucleobase plane peaks at 3.5 Å for A, G and C, and approximately at 3.1-3.2 Å for U. Quantum mechanics (QM) calculations confirm, as expected, that the optimal energy is reached at a shorter distance for the lp-π interaction as compared to the OH-π one (3.0 versus 3.5 Å). The preference of each nucleobase for either type of interaction closely correlates with its electrostatic potential map. Furthermore, QM calculations show that for all the nucleobases a favorable interaction, of either the lp-π or the OH-π type, can be established at virtually any position of the water molecule above the nucleobase skeleton, which is consistent with the uniform projection of the OW atoms over the nucleobases ring we observed in the experimental occurrences. Finally, molecular dynamics simulations of a model system for the characterization of water-nucleobase stacking contacts confirm the stability of these interactions also under dynamic conditions.

A homozygous MRPL24 mutation causes a complex movement disorder and affects the mitoribosome assembly.

Mitochondrial ribosomal protein large 24 (MRPL24) is 1 of the 82 protein components of mitochondrial ribosomes, playing an essential role in the mitochondrial translation process. We report here on a baby girl with cerebellar atrophy, choreoathetosis of limbs and face, intellectual disability and a combined defect of complexes I and IV in muscle biopsy, caused by a homozygous missense mutation identified in MRPL24. The variant predicts a Leu91Pro substitution at an evolutionarily conserved site. Using human mutant cells and the zebrafish model, we demonstrated the pathological role of the identified variant. In fact, in fibroblasts we observed a significant reduction of MRPL24 protein and of mitochondrial respiratory chain complex I and IV subunits, as well a markedly reduced synthesis of the mtDNA-encoded peptides. In zebrafish we demonstrated that the orthologue gene is expressed in metabolically active tissues, and that gene knockdown induced locomotion impairment, structural defects and low ATP production. The motor phenotype was complemented by human WT but not mutant cRNA. Moreover, sucrose density gradient fractionation showed perturbed assembly of large subunit mitoribosomal proteins, suggesting that the mutation leads to a conformational change in MRPL24, which is expected to cause an aberrant interaction of the protein with other components of the 39S mitoribosomal subunit.

Towards the online computer-aided design of catalytic pockets.

The engineering of catalysts with desirable properties can be accelerated by computer-aided design. To achieve this aim, features of molecular catalysts can be condensed into numerical descriptors that can then be used to correlate reactivity and structure. Based on such descriptors, we have introduced topographic steric maps that provide a three-dimensional image of the catalytic pocket-the region of the catalyst where the substrate binds and reacts-enabling it to be visualized and also reshaped by changing various parameters. These topographic steric maps, especially when used in conjunction with density functional theory calculations, enable catalyst structural modifications to be explored quickly, making the online design of new catalysts accessible to the wide chemical community. In this Perspective, we discuss the application of topographic steric maps either to rationalize the behaviour of known catalysts-from synthetic molecular species to metalloenzymes-or to design improved catalysts.

Targeting CD34+ cells of the inflamed synovial endothelium by guided nanoparticles for the treatment of rheumatoid arthritis.

Despite the advances in the treatment of rheumatoid arthritis (RA) achieved in the last few years, several patients are diagnosed late, do not respond to or have to stop therapy because of inefficacy and/or toxicity, leaving still a huge unmet need. Tissue-specific strategies have the potential to address some of these issues. The aim of the study is the development of a safe nanotechnology approach for tissue-specific delivery of drugs and diagnostic probes. CD34 + endothelial precursors were addressed in inflamed synovium using targeted biodegradable nanoparticles (tBNPs). These nanostructures were made of poly-lactic acid, poly-caprolactone, and PEG and then coated with a synovial homing peptide. Immunofluorescence analysis clearly demonstrated their capacity to selectively address CD34 + endothelial cells in synovial tissue obtained from human, mouse, and rat. Biodistribution studies in two different animal models of rheumatoid arthritis (antigen-induced arthritis/AIA and collagen-induced arthritis/CIA) confirmed the selective accumulation in inflamed joints but also evidenced the capacity of tBNP to detect early phases of the disease and the preferential liver elimination. The therapeutic effect of methotrexate (MTX)-loaded tBNPs were studied in comparison with conventional MTX doses. MTX-loaded tBNPs prevented and treated CIA and AIA at a lower dose and reduced administration frequency than MTX. Moreover, MTX-loaded tBNP showed a novel mechanism of action, in which the particles target and kill CD34 + endothelial progenitors, preventing neo-angiogenesis and, consequently, synovial inflammation. tBNPs represent a stable and safe platform to develop highly-sensitive imaging and therapeutic approaches in RA targeting specifically synovial neo-angiogenesis to reduce local inflammation.

Structural and Energetic Impact of Non-natural 7-Deaza-8-azaguanine, 7-Deaza-8-azaisoguanine, and Their 7-Substituted Derivatives on Hydrogen-Bond Pairing with Cytosine and Isocytosine.

The impact of 7-deaza-8-azaguanine (DAG) and 7-deaza-8-azaisoguanine (DAiG) modifications on the geometry and stability of the G:C Watson-Crick (cWW) base pair and the G:iC and iG:C reverse Watson-Crick (tWW) base pairs has been characterized theoretically. In addition, the effect on the same base pairs of seven C7-substituted DAG and DAiG derivatives, some of which have been previously experimentally characterized, has been investigated. Calculations indicate that all of these modifications have a negligible impact on the geometry of the above base pairs, and that modification of the heterocycle skeleton has a small impact on the base-pair interaction energies. Instead, base-pair interaction energies are dependent on the nature of the C7 substituent. For the 7-substituted DAG-C cWW systems, a linear correlation between the base-pair interaction energy and the Hammett constant of the 7-substituent is found, with higher interaction energies corresponding to more electron-withdrawing substituents. Therefore, the explored modifications are expected to be accommodated in both parallel and antiparallel nucleic acid duplexes without perturbing their geometry, while the strength of a base pair (and duplex) featuring a DAG modification can, in principle, be tuned by incorporating different substituents at the C7 position.

Mapping the minimum domain of the fibronectin binding site on transglutaminase 2 (TG2) and its importance in mediating signaling, adhesion, and migration in TG2-expressing cells.

The interaction between the enzyme transglutaminase 2 (TG2) and fibronectin (FN) is involved in the cell-matrix interactions that regulate cell signaling, adhesion, and migration and play central roles in pathologic conditions, particularly fibrosis and cancer. A precise definition of the exact interaction domains on both proteins could provide a tool to design novel molecules with potential therapeutic applications. Although specific residues involved in the interaction within TG2 have been analyzed, little is known regarding the TG2 binding site on FN. This site has been mapped to a large internal 45-kDa protein fragment coincident with the gelatin binding domain (GBD). With the goal of defining the minimal FN interacting domain for TG2, we produced several expression constructs encoding different portions or modules of the GBD and tested their binding and functional properties. The results demonstrate that the I8 module is necessary and sufficient for TG2-binding in vitro, but does not have functional effects on TG2-expressing cells. Modules I7 and I9 increase the strength of the binding and are required for cell adhesion. A 15-kDa fragment encompassing modules I7-9 behaves as the whole 45-kDa GBD and mediates signaling, adhesion, spreading, and migration of TG2+ cells. This study provides new insights into the mechanism for TG2 binding to FN.-Soluri, M. F., Boccafoschi, F., Cotella, D., Moro, L., Forestieri, G., Autiero, I., Cavallo, L., Oliva, R., Griffin, M., Wang, Z., Santoro, C., Sblattero, D. Mapping the minimum domain of the fibronectin binding site on transglutaminase 2 (TG2) and its importance in mediating signaling, adhesion, and migration in TG2-expressing cells.

Apixaban Interacts with Haemoglobin: Effects on Its Plasma Levels.

The direct oral anticoagulant apixaban (APX), a strong factor Xa inhibitor, binds also to plasma proteins, especially albumin, and minimally to α1-acid glycoprotein. Although APX can cross the red cell membrane, due to its chemical structure, and could bind to haemoglobin (Hb), no investigation was performed on this possible phenomenon that could affect the APX plasma concentration and thus its pharmacokinetics and pharmacodynamics. We addressed this issue by (1) measuring the levels of APX and haematological/biochemical parameters in 90 patients on APX therapy; (2) assessing the effect of APX on oxygen saturation curves of Hb; (3) testing the direct APX binding to Hb by fluorescence spectroscopy and a zinc-induced precipitation of Hb coupled to a reversed-phase high-performance liquid chromatography (HPLC)-based method; and (4) simulating in silico by molecular docking the APX interaction with human Hb. In a multivariable analysis, Hb was the only independent variable significantly and inversely associated in 90 patients with APX peak plasma level, at variance with patients treated with rivaroxaban (n = 86) and dabigatran (n = 34) therapy. APX causes a progressive left-shift of the oxygen dissociation curve of purified Hb solution, with a Kd ≅300 µM. Fluorescence- and HPLC-based assays concordantly showed that APX binds to Hb with a Kd ≅350 µM. Finally, docking simulations showed that APX can fit into in the central cavity of Hb. These findings support the hypothesis that APX does bind to Hb, which, due to its millimolar concentration in blood, can act as 'buffer' for the drug and consequently affect its free plasma level.