Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations.

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer's disease (AD), the extracellular aggregates originate from amyloid-ß proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-ß peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to ß-sheet like secondary structures of sequences from amyloid-ß protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and ß-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

Mechanism of pore opening in the calcium-activated chloride channel TMEM16A.

The anion channel TMEM16A is activated by intracellular Ca2+ in a highly cooperative process. By combining electrophysiology and autocorrelation analysis, we investigated the mechanism of channel activation and the concurrent rearrangement of the gate in the narrow part of the pore. Features in the fluctuation characteristics of steady-state current indicate the sampling of intermediate conformations that are successively occupied during gating. The initial step is related to conformational changes induced by Ca2+ binding, which is ensued by rearrangements that open the pore. Mutations in the gate shift the equilibrium of transitions in a manner consistent with a progressive destabilization of this region during pore opening. We come up with a mechanism of channel activation where the binding of Ca2+ induces conformational changes in the protein that, in a sequential manner, propagate from the binding site and couple to the gate in the narrow pore to allow ion permeation.

CYK4 relaxes the bias in the off-axis motion by MKLP1 kinesin-6.

Centralspindlin, a complex of the MKLP1 kinesin-6 and CYK4 GAP subunits, plays key roles in metazoan cytokinesis. CYK4-binding to the long neck region of MKLP1 restricts the configuration of the two MKLP1 motor domains in the centralspindlin. However, it is unclear how the CYK4-binding modulates the interaction of MKLP1 with a microtubule. Here, we performed three-dimensional nanometry of a microbead coated with multiple MKLP1 molecules on a freely suspended microtubule. We found that beads driven by dimeric MKLP1 exhibited persistently left-handed helical trajectories around the microtubule axis, indicating torque generation. By contrast, centralspindlin, like monomeric MKLP1, showed similarly left-handed but less persistent helical movement with occasional rightward movements. Analysis of the fluctuating helical movement indicated that the MKLP1 stochastically makes off-axis motions biased towards the protofilament on the left. CYK4-binding to the neck domains in MKLP1 enables more flexible off-axis motion of centralspindlin, which would help to avoid obstacles along crowded spindle microtubules.

Pathological conformations of disease mutant Ryanodine Receptors revealed by cryo-EM.

Ryanodine Receptors (RyRs) are massive channels that release Ca2+ from the endoplasmic and sarcoplasmic reticulum. Hundreds of mutations are linked to malignant hyperthermia (MH), myopathies, and arrhythmias. Here, we explore the first MH mutation identified in humans by providing cryo-EM snapshots of the pig homolog, R615C, showing that it affects an interface between three solenoid regions. We also show the impact of apo-calmodulin (apoCaM) and how it can induce opening by bending of the bridging solenoid, mediated by its N-terminal lobe. For R615C RyR1, apoCaM binding abolishes a pathological 'intermediate' conformation, distributing the population to a mixture of open and closed channels, both different from the structure without apoCaM. Comparisons show that the mutation primarily affects the closed state, inducing partial movements linked to channel activation. This shows that disease mutations can cause distinct pathological conformations of the RyR and facilitate channel opening by disrupting interactions between different solenoid regions.

Discovery of a hidden transient state in all bromodomain families.

Bromodomains (BDs) are small protein modules that interact with acetylated marks in histones. These posttranslational modifications are pivotal to regulate gene expression, making BDs promising targets to treat several diseases. While the general structure of BDs is well known, their dynamical features and their interplay with other macromolecules are poorly understood, hampering the rational design of potent and selective inhibitors. Here, we combine extensive molecular dynamics simulations, Markov state modeling, and available structural data to reveal a transiently formed state that is conserved across all BD families. It involves the breaking of two backbone hydrogen bonds that anchor the ZA-loop with the αA helix, opening a cryptic pocket that partially occludes the one associated to histone binding. By analyzing more than 1,900 experimental structures, we unveil just two adopting the hidden state, explaining why it has been previously unnoticed and providing direct structural evidence for its existence. Our results suggest that this state is an allosteric regulatory switch for BDs, potentially related to a recently unveiled BD-DNA-binding mode.

A theoretical study on the signal transduction process of bacterial photoreceptor PpSB1 based on the Markov state model.

Light-oxygen-voltage (LOV) domains are blue light sensors and play an important role in signal transduction in many organisms. Generally, LOV domains use chromophores to absorb photons, and then photochemical reactions will occur to convert light energy into chemical energy and transduce it to the main chain of proteins. These reactions can cause conformational rearrangement of proteins, and thus leading to signal transduction. Therefore, it is important to study the signal transduction process of LOV domains for understanding the control mechanism of cellular functions. However, how small photochemical changes in the active sites of the LOV domains lead to large conformational rearrangements of proteins, which in turn lead to signal transduction, has been puzzling us for a long time. Currently, the LOV domains are mainly studied in plants. The signal transduction mechanism of LOV domains in bacteria is still unclear. In this work, the Markov state model (MSM) combined with molecular dynamics (MD) simulations was applied to investigate the signal transduction process of the LOV protein from pseudomonas putida (PpSB1-LOV). The present work will play an important role in understanding the signal transduction mechanism of PpSB1-LOV domains, which may provide theoretical basis for the design and improvement of LOV-based optogenetic tools.

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches.

BACKGROUND: Pneumonia ranks as one of the main infectious sources of mortality among kids under 5 years of age, killing 2500 a day; late research has additionally demonstrated that mortality is higher in the elderly. A few biomarkers, which up to this point have been distinguished for its determination lack specificity, as these biomarkers fail to build up a differentiation between pneumonia and other related diseases, for example, pulmonary tuberculosis and Human Immunodeficiency Infection (HIV). There is an inclusive global consensus of an improved comprehension of the utilization of new biomarkers, which are delivered in light of pneumonia infection for precision identification to defeat these previously mentioned constraints. Antimicrobial peptides (AMPs) have been demonstrated to be promising remedial specialists against numerous illnesses. This research work sought to identify AMPs as biomarkers for three bacterial pneumonia pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Acinetobacter baumannii using in silico technology. Hidden Markov Models (HMMER) was used to identify putative anti-bacterial pneumonia AMPs against the identified receptor proteins of Streptococcus pneumoniae, Klebsiella pneumoniae, and Acinetobacter baumannii. The physicochemical parameters of these putative AMPs were computed and their 3-D structures were predicted using I-TASSER. These AMPs were subsequently subjected to docking interaction analysis against the identified bacterial pneumonia pathogen proteins using PATCHDOCK. RESULTS: The in silico results showed 18 antibacterial AMPs which were ranked based on their E values with significant physicochemical parameters in conformity with known experimentally validated AMPs. The AMPs also bound the pneumonia receptors of their respective pathogens sensitively at the extracellular regions. CONCLUSIONS: The propensity of these AMPs to bind pneumonia pathogens proteins justifies that they would be potential applicant biomarkers for the recognizable detection of these bacterial pathogens in a point-of-care POC pneumonia diagnostics. The high sensitivity, accuracy, and specificity of the AMPs likewise justify the utilization of HMMER in the design and discovery of AMPs for disease diagnostics and therapeutics.

The misfolding mechanism of the key fragment R3 of tau protein: a combined molecular dynamics simulation and Markov state model study.

The formation of neurofibrillary tangles (NFT) by abnormal aggregation of misfolded microtubule-associated protein tau is a hallmark of tauopathies, including Alzheimer's disease. However, it remains unclear how tau monomers undergo conformational changes and further lead to the abnormal aggregation. In this work, molecular dynamics simulation combined with the Markov state model (MSM) analysis was used to uncover the misfolding progress and structural characteristics of the key R3 fragment of tau protein at the atomic level. The simulation results show that R3 exists in disordered structures mainly, which is consistent with the experimental results. The MSM analysis identified multiple ß-sheet conformations of R3. The residues involved in the ß-sheet structure formation are mainly located in three regions: PHF6 at the N-terminal, S324 to N327 at the middle of R3, and K331 to G334 at the C-terminal. In addition, the path analysis of the formation of the ß-sheet structure by transition path theory (TPT) revealed that there are multiple paths to form ß-sheet structures from the disordered state, and the timescales are at the millisecond level, indicating that a large number of structural rearrangements occur during the formation of ß-sheet structures. It is interesting to note that S19 is a critical intermediate state for the formation of two target ß-sheet structures, S23 and S4. In S19, three regions of V306 to K311, C322 to G326, and K331 to G334 form a turn structure, the regions that form the ß-sheet structure in target states S23 and S4, indicating that the formation of a turn structure is necessary to form a ß-sheet structure and then the turn structure will eventually transform into the ß-sheet structure through key hydrogen bonding interactions. These findings can provide insights into the kinetics of tau protein misfolding.

Ligand-centered assessment of SARS-CoV-2 drug target models in the Protein Data Bank.

A bright spot in the SARS-CoV-2 (CoV-2) coronavirus pandemic has been the immediate mobilization of the biomedical community, working to develop treatments and vaccines for COVID-19. Rational drug design against emerging threats depends on well-established methodology, mainly utilizing X-ray crystallography, to provide accurate structure models of the macromolecular drug targets and of their complexes with candidates for drug development. In the current crisis, the structural biological community has responded by presenting structure models of CoV-2 proteins and depositing them in the Protein Data Bank (PDB), usually without time embargo and before publication. Since the structures from the first-line research are produced in an accelerated mode, there is an elevated chance of mistakes and errors, with the ultimate risk of hindering, rather than speeding up, drug development. In the present work, we have used model-validation metrics and examined the electron density maps for the deposited models of CoV-2 proteins and a sample of related proteins available in the PDB as of April 1, 2020. We present these results with the aim of helping the biomedical community establish a better-validated pool of data. The proteins are divided into groups according to their structure and function. In most cases, no major corrections were necessary. However, in several cases significant revisions in the functionally sensitive area of protein-inhibitor complexes or for bound ions justified correction, re-refinement, and eventually reversioning in the PDB. The re-refined coordinate files and a tool for facilitating model comparisons are available at https://covid-19.bioreproducibility.org. DATABASE: Validated models of CoV-2 proteins are available in a dedicated, publicly accessible web service https://covid-19.bioreproducibility.org.

Quantitative assessment of the determinant structural differences between redox-active and inactive glutaredoxins.

Class I glutaredoxins are enzymatically active, glutathione-dependent oxidoreductases, whilst class II glutaredoxins are typically enzymatically inactive, Fe-S cluster-binding proteins. Enzymatically active glutaredoxins harbor both a glutathione-scaffold site for reacting with glutathionylated disulfide substrates and a glutathione-activator site for reacting with reduced glutathione. Here, using yeast ScGrx7 as a model protein, we comprehensively identified and characterized key residues from four distinct protein regions, as well as the covalently bound glutathione moiety, and quantified their contribution to both interaction sites. Additionally, we developed a redox-sensitive GFP2-based assay, which allowed the real-time assessment of glutaredoxin structure-function relationships inside living cells. Finally, we employed this assay to rapidly screen multiple glutaredoxin mutants, ultimately enabling us to convert enzymatically active and inactive glutaredoxins into each other. In summary, we have gained a comprehensive understanding of the mechanistic underpinnings of glutaredoxin catalysis and have elucidated the determinant structural differences between the two main classes of glutaredoxins.

Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45.

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data.

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.

Trajectory-Based Simulation of EPR Spectra: Models of Rotational Motion for Spin Labels on Proteins.

Direct time-domain simulation of continuous-wave (CW) electron paramagnetic resonance (EPR) spectra from molecular dynamics (MD) trajectories has become increasingly popular, especially for proteins labeled with nitroxide spin labels. Due to the time-consuming nature of simulating adequately long MD trajectories, two approximate methods have been developed to reduce the MD-trajectory length required for modeling EPR spectra: hindered Brownian diffusion (HBD) and hidden Markov models (HMMs). Here, we assess the accuracy of these two approximate methods relative to direct simulations from MD trajectories for three spin-labeled protein systems (a simple helical peptide, a soluble protein, and a membrane protein) and two nitroxide spin labels with differing mobilities (R1 and 2,2,6,6-tetramethylpiperidine-1-oxyl-4-amino-4-carboxylic acid (TOAC)). We find that the HMMs generally outperform HBD. Although R1 dynamics partially resembles hindered Brownian diffusion, HMMs accommodate the multiple dynamic time scales for the transitions between rotameric states of R1 that cannot be captured accurately by a HBD model. The MD trajectories of the TOAC-labeled proteins show that its dynamics closely resembles slow multisite exchange between twist-boat and chair ring puckering states. This motion is modeled well by HMM but not by HBD. All MD-trajectory data processing, stochastic trajectory simulations, and CW EPR spectral simulations are implemented in EasySpin, a free software package for MATLAB.

The four-helix bundle in cholinesterase dimers: Structural and energetic determinants of stability.

The crystal structures of truncated forms of cholinesterases provide good models for assessing the role of non-covalent interactions in dimer assembly in the absence of cross-linking disulfide bonds. These structures identify the four-helix bundle that serves as the interface for formation of acetylcholinesterase and butyrylcholinesterase dimers. Here we performed a theoretical comparison of the structural and energetic factors governing dimerization. This included identification of inter-subunit and intra-subunit hydrogen bonds and hydrophobic interactions, evaluation of solvent-accessible surfaces, and estimation of electrostatic contributions to dimerization. To reveal the contribution to dimerization of individual amino acids within the contact area, free energy perturbation alanine screening was performed. Markov state modelling shows that the loop between the α13 and α14 helices in BChE is unstable, and occupies 4 macro-states. The order of magnitude of mean first passage times between these macrostates is ~10-8 s. Replica exchange molecular dynamics umbrella sampling calculations revealed that the free energy of human BChE dimerization is -15.5 kcal/mol, while that for human AChE is -26.4 kcal/mol. Thus, the C-terminally truncated human butyrylcholinesterase dimer is substantially less stable than that of human acetylcholinesterase. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:CHEMBIOINT:1.

Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein-protein interactions.

Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein-protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.

Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α.

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Go model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.

Assessment of structurally and functionally high-risk nsSNPs impacts on human bone morphogenetic protein receptor type IA (BMPR1A) by computational approach.

BMPR1A (BMP type 1 receptor) is a transmembrane cell-surface receptor also known as ALK3 (activin-like kinases-3) encodes for a type I serine/threonine kinase receptor and a member of the transforming growth-factor ß-receptor (TGF-ß) super family. The BMPR1A has a significant interaction with BMP-2 for protein activity and also has a low affinity with growth and differentiation factor 5 (GDF5); positively regulates chondrocyte differentiation. The genetic variations can alter the structure and function of the BMPR1A gene that causes several diseases such as juvenile polyposis syndrome or hereditary cancer-predisposing syndrome. The current study was carried out to identify potential deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in BMPR1A by implementing different computational algorithms such as SIFT, PolyPhen2, SNAP2, PROVEAN, PhD-SNP, SNPs&GO, nsSNPAnalyzer, and P-Mut. From 205 nsSNPs in BMPR1A, 7 nsSNPs (C76Y, C124R, C124Y, C376Y, R443C, R480W, and W487R) were predicted as deleterious in 8 prediction algorithms. The Consurf analysis showed that selected 7 nsSNPs were present in the highly conserved regions. Molecular dynamics simulation analysis also performed to explore conformational changes in the variant structure with respect to its native structure. According to the MDS result, all variants flexibility and rigidity were unbalanced, which may alter the structural and functional behavior of the native protein. Although, three nsSNPs i.e., C124R, C376Y, and R443C have already been reported in patients associated with JPS, but their structural and functional molecular studies remain uncharacterized. Therefore, the findings of this study can provide a better understanding of uncharacterized nsSNPS and to find their association with disease susceptibility and also facilitate to the researchers for designing or developing the target dependent drugs.

A Dynamic Hydrophobic Core and Surface Salt Bridges Thermostabilize a Designed Three-Helix Bundle.

Thermostable proteins are advantageous in industrial applications, as pharmaceuticals or biosensors, and as templates for directed evolution. As protein-design methodologies improve, bioengineers are able to design proteins to perform a desired function. Although many rationally designed proteins end up being thermostable, how to intentionally design de novo, thermostable proteins is less clear. UVF is a de novo-designed protein based on the backbone structure of the Engrailed homeodomain (EnHD) and is highly thermostable (Tm > 99°C vs. 52°C for EnHD). Although most proteins generally have polar amino acids on their surfaces and hydrophobic amino acids buried in their cores, protein engineers followed this rule exactly when designing UVF. To investigate the contributions of the fully hydrophobic core versus the fully polar surface to UVF's thermostability, we built two hybrid, chimeric proteins combining the sets of buried and surface residues from UVF and EnHD. Here, we determined a structural, dynamic, and thermodynamic explanation for UVF's thermostability by performing 4 µs of all-atom, explicit-solvent molecular dynamics simulations at 25 and 100°C, Tanford-Kirkwood solvent accessibility Monte Carlo electrostatic calculations, and a thermodynamic analysis of 40 temperature runs by the weighted-histogram analysis method of heavy-atom, structure-based models of UVF, EnHD, and both chimeric proteins. Our models showed that UVF was highly dynamic because of its fully hydrophobic core, leading to a smaller loss of entropy upon folding. The charged residues on its surface made favorable electrostatic interactions that contributed enthalpically to its thermostability. In the chimeric proteins, both the hydrophobic core and charged surface independently imparted thermostability.

Use of MALDI-MS with solid-state hydrogen deuterium exchange for semi-automated assessment of peptide and protein physical stability in lyophilized solids.

Biological therapeutics are established as major contributors to the pharmaceutical pipeline. Many of these biological drugs are lyophilized to preserve their conformation and reduce decomposition during storage and shipping. Therefore, understanding and controlling the effects of lyophilization on protein higher order structure is critical for commercialization of biologics. Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS) is a well-established technique for studying protein higher order structure. Previous publications have demonstrated a solid state HDX (ssHDX) method for labeling formulated, lyophilized proteins to assess their physical stability during, but this process still suffered from low throughput and undesired back exchange. Recently, our group described a method combining HDX-MS with MALDI to greatly reduce the time of analysis and nearly eliminate H/D back-exchange, but that method was not suited for interrogating solid samples. This work integrates the two techniques to assess and predict the stability of peptides and proteins following mixing and lyophilization with various excipient formulations. Sample mixing and handling were performed through the use of a bench-top robotics and programmed data MALDI-MS acquisition allowed for monitoring deuterium incorporation for dried peptides and protein samples following continuous labeling with D2O vapor. Effects of excipients upon peptide stability were also tracked and compared to a control for a three day labeling time course. This workflow is automated and free from back-exchange. As demonstrated by deuterium retention of bradykinin, these features serve to reduce experimental error normally associated with conventional deuterium exchange experiments. The proposed union of MALDI-MS and ssHDX can be applied to study higher order structure of proteins and peptides and the effects of added excipients in an environment that closely resembles the storage and shipping conditions of biopharmaceuticals and may be beneficial in giving insights studying protein structural dynamics in solids.