Development of donor-specific human leukocyte antigen (HLA) antibodies (DSA) remains a major risk factor for graft loss following organ transplantation, where DSA are directed towards patches on the three-dimensional structure of the respective organ donor\'s HLA proteins. Matching donors and recipients based on HLA epitopes appears beneficial for the avoidance of DSA. Defining surface epitopes however remains challenging and the concepts underlying their characterization are not fully understood. Based on our recently implemented computational deep learning pipeline to define HLA Class I protein-specific surface residues, we hypothesized a correlation between the number of HLA protein-specific solvent-accessible interlocus amino acid mismatches (arbitrarily called Snowflake) and the incidence of DSA. To validate our hypothesis, we considered two cohorts simultaneously. The kidney transplant cohort (KTC) considers 305 kidney-transplanted patients without DSA prior to transplantation. During the follow-up, HLA antibody screening was performed regularly to identify DSA. The pregnancy cohort (PC) considers 231 women without major sensitization events prior to pregnancy who gave live birth. Post-delivery serum was screened for HLA antibodies directed against the child\'s inherited paternal haplotype (CSA). Based on the involved individuals\' HLA typings, the numbers of interlocus-mismatched antibody-verified eplets (AbvEPS), the T cell epitope PIRCHE-II model and Snowflake were calculated locus-specific (HLA-A, -B and -C), normalized and pooled. In both cohorts, Snowflake numbers were significantly elevated in recipients/mothers that developed DSA/CSA. Univariable regression revealed significant positive correlation between DSA/CSA and AbvEPS, PIRCHE-II and Snowflake. Snowflake numbers showed stronger correlation with numbers of AbvEPS compared to Snowflake numbers with PIRCHE-II. Our data shows correlation between Snowflake scores and the incidence of DSA after allo-immunization. Given both AbvEPS and Snowflake are B cell epitope models, their stronger correlation compared to PIRCHE-II and Snowflake appears plausible. Our data confirms that exploring solvent accessibility is a valuable approach for refining B cell epitope definitions.

Molecular Dynamics (MD) is a method used to calculate the movement of atoms and molecules broadly applied to several aspects of science. It involves computational simulation, which makes it, at first glance, not easily accessible. The rise of several automated tools to perform molecular simulations has allowed researchers to navigate through the various steps of MD. This enables to elucidate structural properties of proteins that could not be analyzed otherwise, such as the impact of glycosylation. Glycosylation dictates the physicochemical and biological properties of a protein modulating its solubility, stability, resistance to proteolysis, interaction partners, enzymatic activity, binding and recognition. Given the high conformational and compositional diversity of the glycan chains, assessing their influence on the protein structure is challenging using conventional analytical techniques. In this manuscript, we present a step-by-step workflow to build and perform MD analysis of glycoproteins focusing on the SPIKE glycoprotein of SARS-CoV-2 to appraise the impact of glycans in structure stabilization and antibody occlusion.

Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure-function relations of RNAs.

MTHFR deficiency still deserves an investigation to associate the phenotype to protein structure variations. To this aim, considering the MTHFR wild type protein structure, with a catalytic and a regulatory domain and taking advantage of state-of-the-art computational tools, we explore the properties of 72 missense variations known to be disease associated. By computing the thermodynamic ΔΔG change according to a consensus method that we recently introduced, we find that 61% of the disease-related variations destabilize the protein, are present both in the catalytic and regulatory domain and correspond to known biochemical deficiencies. The propensity of solvent accessible residues to be involved in protein-protein interaction sites indicates that most of the interacting residues are located in the regulatory domain, and that only three of them, located at the interface of the functional protein homodimer, are both disease-related and destabilizing. Finally, we compute the protein architecture with Hidden Markov Models, one from Pfam for the catalytic domain and the second computed in house for the regulatory domain. We show that patterns of disease-associated, physicochemical variation types, both in the catalytic and regulatory domains, are unique for the MTHFR deficiency when mapped into the protein architecture.

Spike (S) glycoprotein of SARS-CoV2 exists chiefly in two conformations, open and closed. Most previous structural studies on S protein have been conducted at pH 8.0, but knowledge of the conformational propensities under both physiological and endosomal pH conditions is important to inform vaccine development. Our current study employed single-particle cryoelectron microscopy to visualize multiple states of open and closed conformations of S protein at physiological pH 7.4 and near-physiological pH 6.5 and pH 8.0. Propensities of open and closed conformations were found to differ with pH changes, whereby around 68% of S protein exists in open conformation at pH 7.4. Furthermore, we noticed a continuous movement in the N-terminal domain, receptor-binding domain (RBD), S2 domain, and stalk domain of S protein conformations at various pH values. Several key residues involving RBD-neutralizing epitopes are differentially exposed in each conformation. This study will assist in developing novel therapeutic measures against SARS-CoV2.

OBJECTIVE: Histidine (His) undergoes light-induced reactions such as oxidation, crosslinking and addition. These reactions are initiated by singlet oxygen (1O2) to generate His photo-oxidation products, which are subject to nucleophilic attack by a non-oxidized His residue from another protein or by nucleophilic buffer components such as Tris and His. This report aims to identify light-induced His-adducts to a monoclonal antibody (mAb-1) due to the reaction of His molecules in the buffer with the photooxidized His residues under ICH light conditions. Since polysorbate-20 (PS-20) is a commonly used excipient in biotherapeutics formulation, it is also important to study the impact of PS-20 concentration on protein photostability.
RESULTS: We identified and characterized light-induced His-adducts of mAb-1 by LC-MS/MS. We showed that the levels of light-induced His-adducts generally correlate with the solvent accessibility of His residues in the protein. In addition, the presence of PS-20 at concentrations commonly used in protein drug formulations can significantly increase the levels of light-induced His-adducts.
CONCLUSIONS: Since His residues are present in a conserved region in the Fc domain, and may be present in the complementarity-determining region (CDR), the impact on the biological functions of the His-adducts observed here should be further studied to evaluate the risk of their presence.

Superfine pulverisation (SFP) pretreatment of Lycium barbarum L. leaves was performed to obtain highly crystalline cellulose. Compared with other common pulverisation methods, SFP enhanced cellulosic crystallinity by 18.3 % and 8.4 %, with and without post-acid treatments, respectively. XRD and solid-state NMR analyses showed that SFP facilitated the exposure of amorphous substances (i.e., hemicellulose and lignin) to NaOH and H2O2. Large amounts of silicon (5.5 %) and aluminium (2.1 %) were found to incorporate into the crystalline regions of SFP-produced cellulose. Further FTIR and thermogravimetric analyses revealed that SFP-produced cellulose contained large amounts of hydroxyl groups, affecting the cellulosic crystallinity and thermal stability. These findings demonstrate the potential for SFP to serve as a green technology for production of highly crystalline and mineral-rich cellulose.

Carboxyl-modified substrates are the most common chemical moieties that are frequently used as protein defibrillators. We studied the stability of protein-benzoic acid complexes with bovine serum albumin (BSA), zein and lysozyme proteins using various computational methods. Structural model for zein was built using homology modelling technique and molecular docking was used to prepare complex structures of all three proteins with benzoic acid. Molecular dynamics calculations performed on these complex structures provided a strong support for the stability of protein-benzoic acid complexes. The results from various analyses including root-mean-square deviation (RMSD) and radius of gyration showed the stability and compactness of all proteins-benzoic acid complexes. Moreover, exploration of structural fluctuations in proteins revealed the stability of active site residues. Two potential binding modes of benzoic acid with all three proteins were identified via cluster analysis. The binding mode which was retrieved from top cluster containing 86-91% of total conformations displayed very strong binding interactions for zein, BSA and lysozyme proteins. In addition, the results of binding mode showed that various interactions, including hydrogen binding, hydrophobic and electrostatic interactions were important for the optimal binding of benzoic acid with the active sites of proteins. Exploration of solvent accessible surface area showed that lysozyme-binding cavity was more exposed to the surface as compared to the other two proteins. Free energy analysis of all protein systems showed the stability of protein-benzoic acid complexes with lysozyme and BSA relatively more stable than zein system. The results of our study provided important insights to the dynamic and structural information about protein-benzoic acid interactions with BSA, zein and lysozyme proteins. This work is important in enhancing the stability of therapeutic protein drugs loaded on carboxyl substrates.

Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.