Structural basis of the human NAIP/NLRC4 inflammasome assembly and pathogen sensing.

The NLR family caspase activation and recruitment domain-containing 4 (NLRC4) inflammasome is a critical cytosolic innate immune machine formed upon the direct sensing of bacterial infection and in response to cell stress during sterile chronic inflammation. Despite its major role in instigating the subsequent host immune response, a more complete understanding of the molecular events in the formation of the NLRC4 inflammasome in humans is lacking. Here we identify Bacillus thailandensis type III secretion system needle protein (Needle) as a potent trigger of the human NLR family apoptosis inhibitory protein (NAIP)/NLRC4 inflammasome complex formation and determine its structural features by cryogenic electron microscopy. We also provide a detailed understanding of how type III secretion system pathogen components are sensed by human NAIP to form a cascade of NLRC4 protomer through a critical lasso-like motif, a 'lock-key' activation model and large structural rearrangement, ultimately forming the full human NLRC4 inflammasome. These results shed light on key regulatory mechanisms specific to the NLRC4 inflammasome assembly, and the innate immune modalities of pathogen sensing in humans.

Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations.

Cyclic peptides are an emerging therapeutic modality that can target protein-protein interaction sites with high affinity and selectivity. A common medicinal chemistry strategy for the optimization of peptide hits is conformational stabilization through macrocyclization. We present a method based on explicit solvent enhanced sampling molecular dynamics simulations for estimating the impact of varying linker lengths and chemistry on the conformational stability of a peptide. The method is demonstrated on three cyclic peptide series that bind to proteins PCSK9, trypsin, and MDM2 adopting loop, ß-sheet, and helical secondary structures. In general, the simulations show greater solution stability of the receptor-bound conformation for the higher-affinity peptides, consistent with the idea that preorganizing a ligand for binding can enhance binding affinity. The impact of the force field and sampling is discussed for one series that does not follow this trend. We have successfully applied this method to internal discovery programs to design peptides with increased potency and chemical stability.

MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands.

Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.

Residue-Specific Impact of EDTA and Methionine on Protein Oxidation in Biotherapeutics Formulations Using an Integrated Biotherapeutics Drug Product Development Workflow.

The rational design and selection of formulation composition to meet molecule-specific and product-specific needs are critical for biotherapeutics development to ensure physical and chemical stability. This work, based on three antibody-based (mAb) proteins (mAbA, mAbB, and mAbC), evaluates residue-specific impact of EDTA and methionine on protein oxidation, using an integrated biotherapeutics drug product development workflow. This workflow includes statistical experimental design, high-throughput experimental automation and execution, structure-based in silico modeling, inferential statistical analysis, and enhanced interactive data visualization of large datasets. This oxidation study evaluates the impact of formulation parameters including pH, protein concentration, and the presence of polysorbate 80 on the oxidation of specific conserved and variable residues of mAbs A, B, and C in the presence of stressors (iron, peroxide) and/or protectants (EDTA, L-methionine). Residue-specific analysis by automated high-throughput peptide mapping demonstrates differential residue-specific effects of EDTA and methionine in protecting against oxidation, highlighting the need for molecule-specific and product-specific selection of these excipients during formulation development. Computational modeling based on a homology model and the two-shell water coordination method (WCN) was employed to gain mechanistic understanding of residue-specific oxidation susceptibility of methionine residues. The computational determinants of local solvent exposure of methionine residues showed good correlation of WCN with experimentally determined oxidation for corresponding residues. The rapid generation of high-resolution data, statistical data analysis and interactive visualization of the high-throughput residue-level data containing ∼200 unique formulations facilitate residue-specific, molecule-specific and product-specific oxidation (global and local) assessment for oxidation protectants during early development for mAbs and related mAb-based modalities.

Surface salt bridges contribute to the extreme thermal stability of an FN3-like domain from a thermophilic bacterium.

This study uses differential scanning calorimetry, X-ray crystallography, and molecular dynamics simulations to investigate the structural basis for the high thermal stability (melting temperature 97.5°C) of a FN3-like protein domain from thermophilic bacteria Thermoanaerobacter tengcongensis (FN3tt). FN3tt adopts a typical FN3 fold with a three-stranded beta sheet packing against a four-stranded beta sheet. We identified three solvent exposed arginine residues (R23, R25, and R72), which stabilize the protein through salt bridge interactions with glutamic acid residues on adjacent strands. Alanine mutation of the three arginine residues reduced melting temperature by up to 22°C. Crystal structures of the wild type (WT) and a thermally destabilized (∆Tm -19.7°C) triple mutant (R23L/R25T/R72I) were found to be nearly identical, suggesting that the destabilization is due to interactions of the arginine residues. Molecular dynamics simulations showed that the salt bridge interactions in the WT were stable and provided a dynamical explanation for the cooperativity observed between R23 and R25 based on calorimetry measurements. In addition, folding free energy changes computed using free energy perturbation molecular dynamics simulations showed high correlation with melting temperature changes. This work is another example of surface salt bridges contributing to the enhanced thermal stability of thermophilic proteins. The molecular dynamics simulation methods employed in this study may be broadly useful for in silico surface charge engineering of proteins.

Product-Specific Impact of Viscosity Modulating Formulation Excipients During Ultra-High Concentration Biotherapeutics Drug Product Development.

Developing ultra-high concentration biotherapeutics drug products can be challenging due to increased viscosity, processing, and stability issues. Excipients used to alleviate these concerns are traditionally evaluated at lower protein concentrations. This study investigates whether classically known modulators of stability and viscosity at low (<50 mg/mL) to high (>50 - 150 mg/mL) protein concentrations are beneficial in ultra-high (>150 mg/mL) concentration protein formulations and drug products. This study evaluates the effect of arginine monohydrochloride, proline, and lysine monohydrochloride on viscosity and concentratability at different high and ultra-high protein concentrations using a monoclonal antibody, mAbN, formulation as a candidate protein system. The effect of excipients on the viscosity and concentratability (rate and extent) was different at high versus ultra-high protein concentrations. These results highlight that classical excipients in literature known to modulate protein interactions at low protein concentrations and reduce viscosity at high protein concentrations may need to be evaluated at target protein concentrations in a product-specific manner while developing ultra-high concentration biologics drug products.

Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS).

Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach.

Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B22 and kD and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

Homology-based hydrogen bond information improves crystallographic structures in the PDB.

The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H-bond) distances as a source of information. However, H-bond restraints can improve structures at low resolution where diffraction data are limited. To improve low-resolution structure refinement, we present methods for deriving H-bond information either globally from well-refined high-resolution structures from the PDB-REDO databank, or specifically from on-the-fly constructed sets of homologous high-resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low-resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB-REDO databank (https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset.

Efficient Generation of Bispecific Murine Antibodies for Pre-Clinical Investigations in Syngeneic Rodent Models.

Therapeutic concepts exploiting tumor-specific antibodies are often established in pre-clinical xenograft models using immuno-deficient mice. More complex therapeutic paradigms, however, warrant the use of immuno-competent mice, that more accurately capture the relevant biology that is being exploited. These models require the use of (surrogate) mouse or rat antibodies to enable optimal interactions with murine effector molecules. Immunogenicity is furthermore decreased, allowing longer-term treatment. We recently described controlled Fab-arm exchange (cFAE) as an easy-to-use method for the generation of therapeutic human IgG1 bispecific antibodies (bsAb). To facilitate the investigation of dual-targeting concepts in immuno-competent mice, we now applied and optimized our method for the generation of murine bsAbs. We show that the optimized combinations of matched point-mutations enabled efficient generation of murine bsAbs for all subclasses studied (mouse IgG1, IgG2a and IgG2b; rat IgG1, IgG2a, IgG2b, and IgG2c). The mutations did not adversely affect the inherent effector functions or pharmacokinetic properties of the corresponding subclasses. Thus, cFAE can be used to efficiently generate (surrogate) mouse or rat bsAbs for pre-clinical evaluation in immuno-competent rodents.

Functional, Biophysical, and Structural Characterization of Human IgG1 and IgG4 Fc Variants with Ablated Immune Functionality.

Engineering of fragment crystallizable (Fc) domains of therapeutic immunoglobulin (IgG) antibodies to eliminate their immune effector functions while retaining other Fc characteristics has numerous applications, including blocking antigens on Fc gamma (Fcγ) receptor-expressing immune cells. We previously reported on a human IgG2 variant termed IgG2σ with barely detectable activity in antibody-dependent cellular cytotoxicity, phagocytosis, complement activity, and Fcγ receptor binding assays. Here, we extend that work to IgG1 and IgG4 antibodies, alternative subtypes which may offer advantages over IgG2 antibodies. In several in vitro and in vivo assays, the IgG1σ and IgG4σ variants showed equal or even lower Fc-related activities than the corresponding IgG2σ variant. In particular, IgG1σ and IgG4σ variants demonstrate complete lack of effector function as measured by antibody-dependent cellular cytotoxicity, complement-dependent cytotoxicity, antibody-dependent cellular phagocytosis, and in vivo T-cell activation. The IgG1σ and IgG4σ variants showed acceptable solubility and stability, and typical human IgG1 pharmacokinetic profiles in human FcRn-transgenic mice and cynomolgus monkeys. In silico T-cell epitope analyses predict a lack of immunogenicity in humans. Finally, crystal structures and simulations of the IgG1σ and IgG4σ Fc domains can explain the lack of Fc-mediated immune functions. These variants show promise for use in those therapeutic antibodies and Fc fusions for which the Fc domain should be immunologically "silent".

Equilibrium molecular thermodynamics from Kirkwood sampling.

We present two methods for barrierless equilibrium sampling of molecular systems based on the recently proposed Kirkwood method (J. Chem. Phys. 2009, 130, 134102). Kirkwood sampling employs low-order correlations among internal coordinates of a molecule for random (or non-Markovian) sampling of the high dimensional conformational space. This is a geometrical sampling method independent of the potential energy surface. The first method is a variant of biased Monte Carlo, where Kirkwood sampling is used for generating trial Monte Carlo moves. Using this method, equilibrium distributions corresponding to different temperatures and potential energy functions can be generated from a given set of low-order correlations. Since Kirkwood samples are generated independently, this method is ideally suited for massively parallel distributed computing. The second approach is a variant of reservoir replica exchange, where Kirkwood sampling is used to construct a reservoir of conformations, which exchanges conformations with the replicas performing equilibrium sampling corresponding to different thermodynamic states. Coupling with the Kirkwood reservoir enhances sampling by facilitating global jumps in the conformational space. The efficiency of both methods depends on the overlap of the Kirkwood distribution with the target equilibrium distribution. We present proof-of-concept results for a model nine-atom linear molecule and alanine dipeptide.

A conformational factorisation approach for estimating the binding free energies of macromolecules.

We present a conformational factorization approach. The theory is based on a superposition partition function, decomposed as a sum over contributions from local minima. The factorisation greatly reduces the number of minima that need to be considered, by employing the same local configurations for groups that are sufficiently distant from the binding site. The theory formalises the conditions required to analyse how our definition of the binding site region affects the free energy difference between the apo and holo states. We employ basin-hopping parallel tempering to sample minima that contribute significantly to the partition function, and calculate the binding free energies within the harmonic normal mode approximation. A further significant gain in efficiency is achieved using a recently developed local rigid body framework in both the sampling and the normal mode analysis, which reduces the number of degrees of freedom. We benchmark this approach for human aldose reductase (PDB code 2INE). When varying the size of the rigid region, the free energy difference converges for factorisation of groups at a distance of 14 Å from the binding site, which corresponds to 80% of the protein being locally rigidified.

Energy landscapes and global thermodynamics for alanine peptides.

We compare different approaches for computing the thermodynamics of biomolecular systems. Techniques based on parallel replicas evolving via molecular dynamics or Monte Carlo simulations produce overlapping histograms for the densities of states. In contrast, energy landscape methods employ a superposition partition function constructed from local minima of the potential energy surface. The latter approach is particularly powerful for systems exhibiting broken ergodicity, and it is usually implemented using a harmonic normal mode approximation, which has not been extensively tested for biomolecules. The present contribution compares these alternative approaches for small alanine peptides modelled using the CHARMM and AMBER force fields. Densities of states produced from canonical sampling using multiple temperature replicas provide accurate reference data to evaluate the effect of the harmonic normal mode approximation in the superposition calculations. This benchmarking lays foundations for the application of energy landscape methods to larger biomolecules. It will also provide well characterised model systems for developing enhanced sampling methods, and for the treatment of anharmonicity corresponding to individual local minima.

Cloning, soluble expression and purification of high yield recombinant hGMCSF in Escherichia coli.

Expression of human granulocyte macrophage colony stimulating factor (hGMCSF), a cytokine of therapeutic importance, as a thioredoxin (TRX) fusion has been investigated in Escherichia coli BL21 (DE3) codon plus cells. The expression of this protein was low when cloned under the T7 promoter without any fusion tags. High yield of GMCSF was achieved (∼88 mg/L of fermentation broth) in the shake flask when the gene was fused to the E. coli TRX gene. The protein was purified using a single step Ni(2+)-NTA affinity chromatography and the column bound fusion tag was removed by on-column cleavage with enterokinase. The recombinant hGMCSF was expressed as a soluble and biologically active protein in E. coli, and upon purification, the final yield was ∼44 mg/L in shake flask with a specific activity of 2.3 × 10(8) U/mg. The results of Western blot and RP-HPLC analyses, along with biological activity using the TF-1 cell line, established the identity of the purified hGMCSF. In this paper, we report the highest yield of hGMCSF expressed in E. coli. The bioreactor study shows that the yield of hGMCSF could be easily scalable with a yield of ∼400 mg/L, opening up new opportunities for large scale production hGMCSF in E. coli.

Accelerated convergence of molecular free energy via superposition approximation-based reference states.

The free energy of a molecular system can, at least in principle, be computed by thermodynamic perturbation from a reference system whose free energy is known. The convergence of such a calculation depends critically on the conformational overlap between the reference and the physical systems. One approach to defining a suitable reference system is to construct it from the one-dimensional marginal probability distribution functions (PDFs) of internal coordinates observed in a molecular simulation. However, the conformational overlap of this reference system tends to decline steeply with increasing dimensionality, due to the neglect of correlations among the coordinates. Here, we test a reference system that can account for pairwise correlations among the internal coordinates, as captured by their two-dimensional marginal PDFs derived from a molecular simulation. Incorporating pairwise correlations in the reference system is found to dramatically improve the convergence of the free energy estimates relative to the first-order reference system, due to increased conformational overlap with the physical distribution.

Over expression and analysis of O-glycosylated recombinant human granulocyte colony stimulating factor in Pichia pastoris using Agilent 2100 Bioanalyzer.

Recombinant human granulocyte colony stimulating factor (rhGCSF) was expressed in methylotrophic yeast Pichia pastoris under the control of AOX1 promoter after integration of the GCSF gene into P. pastoris genome. Methanol induction of the Pichia integrants yielded only 2mgl(-1) of rhGCSF whereas inclusion of surfactants during induction enhanced the yields to the level of 200-250mgl(-1) in shake flask studies after 72h of induction. Preliminary studies in a bioreactor showed rhGCSF expression levels of 6mg rhGCSF g(-1) methanol day(-1) which is significantly higher to the reported value of 0.4mg rhGCSF g(-1) methanol day(-1) reported till date for Pichia derived rhGCSF. A single step purification protocol of shake flask derived rhGCSF yielded homogenous rhGCSF protein of >99% purity. Even though, purified rhGCSF showed a single band on reducing SDS-PAGE, examination of the same protein on Agilent 2100 Bioanalyzer, revealed two closely unresolved peaks. Such a pattern was also observed for crude rhGCSF preparations. Mutagenesis of the O-glycosylation site of rhGCSF (Thr(133) to Leu(133)) showed a single peak on bioanalyzer, which overlapped with the peak obtained for a non-glycosylated rhGCSF. Our data discloses for the first time the novel use of Agilent Bioanalyzer to detect glycoforms of proteins in crude and purified preparations and such a tool could be easily applied for glycoprotein profiling of monoclonal antibodies and other fusion proteins expressed in mammalian cells. This is the first report of a simple, rapid, sensitive and a cost-efficient tool for detection of glycoproteins.

Configurational entropy in protein-peptide binding: computational study of Tsg101 ubiquitin E2 variant domain with an HIV-derived PTAP nonapeptide.

Configurational entropy is thought to influence biomolecular processes, but there are still many open questions about this quantity, including its magnitude, its relationship to molecular structure, and the importance of correlation. The mutual information expansion (MIE) provides a novel and systematic approach to extracting configurational entropy changes due to correlated motions from molecular simulations. We present the first application of the MIE method to protein-ligand binding using multiple molecular dynamics simulations to study the association of the ubiquitin E2 variant domain of the protein Tsg101 and an HIV-derived nonapeptide. This investigation utilizes the second-order MIE approximation, which accounts for correlations between all pairs of degrees of freedom. The computed change in configurational entropy is large and has a major contribution from changes in pairwise correlation. The results also reveal intricate structure-entropy relationships. Thus, the present analysis suggests that in order for a model of binding to be accurate, it must include a careful accounting of configurational entropy changes.

Sampling conformations in high dimensions using low-dimensional distribution functions.

We present an approximation to a molecule's N-dimensional conformational probability density function (pdf) in terms of marginal pdfs of highest order l, where l is much less than N. The approximation is constructed as a product of conditional pdfs derived by recursive application of the generalized Kirkwood superposition approximation. Furthermore, an algorithm is presented to sample conformations from the approximate full-dimensional pdf based upon all input marginal pdfs. The sampling algorithm is tested for three small molecule systems by using the algorithm to sample conformations at levels l=1, 2, or 3 and comparing the distributions of sampled conformations with those from the molecular dynamics (MD) simulations. The distributions of conformations sampled at third (l=3) order resemble the MD distributions rather well and significantly better than those sampled at second (l=2) or first (l=1) order. In addition to highlighting the importance of correlations among internal degrees of freedom, these results suggest that low-order correlations suffice to describe most of the conformational fluctuations of molecules in a thermal environment.

Hydration of a hydrophobic cavity and its functional role: a simulation study of human interleukin-1beta.

Molecular dynamics simulations reveal that the hydrophobic cavity in human cytokine Interleukin-1beta is hydrated and can dynamically accommodate between one and four water molecules. These waters have residence times >> 500 ps and can give rise to detectable NOEs, in agreement with NMR observations of Ernst et al. (Science 1995; 267:1813-1817). The waters also display high positional disorder within the cavity, which explains why they have not been resolved crystallographically. The average distribution of water molecules over time within the cavity matches well the low resolution electron density extracted by Yu et al. (Proc Natl Acad Sci 1999; 96:103-108). The water molecules hydrate the hydrophobic cavity preferentially as complex clusters. These clusters result from a combination of hydrogen bonds between the waters and stabilizing interactions between the waters and aromatic rings forming the cavity. Free energy estimates suggest that it takes 4-waters to hydrate the cavity in a thermodynamically stable manner leading to a gain in free energy of transfer from bulk of approximately approximately 3.6 kcal/mol. This arises from the existence of the water clusters in multiple hydrogen bonded states. In addition, the waters are found to migrate either individually or as clusters out of the cavity through several pathways. The upper limit for one-dimensional diffusion of the waters within the protein matrix is 4 A/ps (relative to 6 A/ps for bulk). Simulations reveal pathways in addition to those identified crystallographically, with motions controlled by the rotations of sidechains. We find that only when the hydrophobic cavity is hydrated, do correlated motions couple distant sites with the sites that make contact with the receptor and this data partly offers an explanation of experimental mutagenesis data. Simulations, together with recent observations based on mutagenesis by Heidary et al. (J Mol Biol 2005; 353:1187-1198) that hydrogen bond networks couple motions across long distances in interleukin-1beta, lead us to hypothesize that the hydration of the cavity (conserved across mammals) can thermodynamically enhance hydrogen bond networks to enable coupling across long distances by acting as a plug and this in turn enables a kinetic control of the rate of transmission of signals.