Two physics-based models for pH-dependent calculations of protein solubility.

When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.

Practical Applications of Biofluorescent Particle Counting in Environmental Monitoring Investigations.

Investigations into environmental monitoring (EM) excursions can be prolonged and do not always result in clear root causes or corrective and preventative actions. This article outlines how biofluorescent particle counting (BFPC) can be used in investigations to eliminate the inherent delays of culture-based methods. The application for investigations supplements routine EM, acting as a risk-reduction tool enabling real-time detection of viable microorganisms in air samples and supporting root cause analysis and remedial actions. The article includes guidance on how to use the technology, a real case study involving a mold excursion, and examples of business benefits achieved by various companies.

Continuous Microbiological Environmental Monitoring for Process Understanding and Reduced Interventions in Aseptic Manufacturing.

This paper provides recommendations for quality oversight, manufacturing operations, and industry perspective of regulatory expectations to enable aseptic facilities to move toward real-time and continuous microbiological environmental monitoring, thereby reducing interventions and future replacement of Grade A settle plates and nonremote active air sampling. The replacement of traditional monitoring with biofluorescent particle-counting systems provides an improvement in process understanding and product safety and reduces operator manipulations, assuring product quality and real-time process verification. The future state pharmaceutical technology roadmaps include gloveless isolators with real-time and continuous monitoring for aseptic manufacturing.LAY ABSTRACT: This paper advocates the use of an alternative and relatively new method of monitoring the air for contamination in biopharmaceutical manufacturing facilities. The alternative method is based on a type of instrument the authors refer to as a biofluorescent particle counter (BFPC). The BFPC method has the advantage of being able to detect airborne microorganisms continuously and to record the actual time of detection. The replacement of traditional monitoring with BFPC systems can provide better data, which can be used to improve the understanding of contamination risks in complex manufacturing processes, ultimately providing more confidence in product safety. The authors present data showing the suitability of BFPC. This immediate result is very useful for picking up early any possible contamination and should, therefore, provide a better way to monitor and control the risk of contamination. As traditional monitoring methods require manual manipulation, an additional advantage of BFPC systems is that they can reduce manual manipulations. Elimination of all interventions is a goal in the industry, because although they are tightly controlled, interventions are an unwanted potential source of contamination.

Fully automated antibody structure prediction using BIOVIA tools: Validation study.

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.

A pH-dependent computational approach to the effect of mutations on protein stability.

This article describes a novel software implementation for high-throughput scanning mutagenesis with a focus on protein stability. The approach combines molecular mechanics calculations with calculations of protein ionization and a Gaussian-chain model of electrostatic interactions in unfolded state. Comprehensive testing demonstrates a state-of-the-art accuracy for predicted free energy differences on single, double, and triple mutations with a correlation coefficient R above 0.7, which takes about 1.5 min per mutation on a single CPU. Unlike most of existing in silico methods for fast mutagenesis, the stability changes are reported as a continuous function of solution pH for wide pH intervals. We also propose a novel in silico strategy for searching stabilized protein variants that is based on combinatorial scanning mutagenesis using representative amino acid types. Our in silico predictions are in excellent agreement with the hyper-stabilized variants of mesophilic cold shock protein found using the Proside method of direct evolution. © 2016 Wiley Periodicals, Inc.

T cell ignorance is bliss: T cells are not tolerized by Langerhans cells presenting human papillomavirus antigens in the absence of costimulation.

Human papillomavirus type 16 (HPV16) infections are intra-epithelial, and thus, HPV16 is known to interact with Langerhans cells (LCs), the resident epithelial antigen-presenting cells (APCs). The current paradigm for APC-mediated induction of T cell anergy is through delivery of T cell receptor signals via peptides on MHC molecules (signal 1), but without costimulation (signal 2). We previously demonstrated that LCs exposed to HPV16 in vitro present HPV antigens to T cells without costimulation, but it remained uncertain if such T cells would remain ignorant, become anergic, or in the case of CD4+ T cells, differentiate into Tregs. Here we demonstrate that Tregs were not induced by LCs presenting only signal 1, and through a series of in vitro immunizations show that CD8+ T cells receiving signal 1 + 2 from LCs weeks after consistently receiving signal 1 are capable of robust effector functions. Importantly, this indicates that T cells are not tolerized but instead remain ignorant to HPV, and are activated given the proper signals.

Functional analysis of HPV-like particle-activated Langerhans cells in vitro.

Langerhans cells (LCs) are antigen-presenting cells responsible for initiating an immune response against human papillomaviruses (HPVs) entering the epithelial layer in vivo as they are the first immune cell that HPV comes into contact with. LCs become activated in response to foreign antigens, which causes internal signaling resulting in the increased expression of co-stimulatory molecules and the secretion of inflammatory cytokines. Functionally activated LCs are then capable of migrating to the lymph nodes where they interact with antigen-specific T cells and initiate an adaptive T-cell response in vivo. However, HPV has evolved in a manner that suppresses LC function, and thus the induction of antigen-specific T cells is hindered. While many methods exist to monitor the activity of LCs in vitro, the migration and induction of cytotoxic T cells is ultimately indicative of a functional immune response. Here, methods in analyzing functional migration and induction of antigen-specific T cells after stimulation of LCs with HPV virus-like particles in vitro are described.

Forced LIGHT expression in prostate tumors overcomes Treg mediated immunosuppression and synergizes with a prostate tumor therapeutic vaccine by recruiting effector T lymphocytes.

BACKGROUND: LIGHT, a ligand for lymphotoxin-ß receptor (LTßR) and herpes virus entry mediator, is predominantly expressed on activated immune cells and LTßR signaling leads to the recruitment of lymphocytes. The interaction between LIGHT and LTßR has been previously shown to activate immune cells and result in tumor regression in a virally-induced tumor model, but the role of LIGHT in tumor immunosuppression or in a prostate cancer setting, where self antigens exist, has not been explored. We hypothesized that forced expression of LIGHT in prostate tumors would shift the pattern of immune cell infiltration toward an anti-tumoral milieu, would inhibit T regulatory cells (Tregs) and would induce prostate cancer tumor associated antigen (TAA) specific T cells that would eradicate tumors. METHODS: Real Time PCR was used to evaluate expression of forced LIGHT and other immunoregulatory genes in prostate tumors samples. For in vivo studies, adenovirus encoding murine LIGHT was injected intratumorally into TRAMP-C2 prostate cancer cell tumor bearing mice. Chemokine and cytokine concentrations were determined by multiplex ELISA. Flow cytometry was used to phenotype tumor infiltrating lymphocytes and expression of LIGHT on the tumor cell surface. Tumor-specific lymphocytes were quantified via ELISpot assay. Treg induction and Treg suppression assays determined Treg functionality after LIGHT treatment. RESULTS: LIGHT in combination with a therapeutic vaccine, PSCA TriVax, reduced tumor burden. LIGHT expression peaked within 48 hr of infection, recruited effector T cells that recognized mouse prostate stem cell antigen (PSCA) into the tumor microenvironment, and inhibited infiltration of Tregs. Tregs isolated from tumor draining lymph nodes had impaired suppressive capability after LIGHT treatment. CONCLUSION: Forced LIGHT treatment combined with PSCA TriVax therapeutic vaccination delays prostate cancer progression in mice by recruiting effector T lymphocytes to the tumor and inhibiting Treg mediated immunosuppression. Prostate 75:280-291, 2015. © 2014 Wiley Periodicals, Inc.

Automated antibody structure prediction using Accelrys tools: results and best practices.

We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template-based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X-ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X-ray structures.

Inhibition of Langerhans cell maturation by human papillomavirus type 16: a novel role for the annexin A2 heterotetramer in immune suppression.

High-risk human papillomaviruses (HPVs) are sexually transmitted viruses causally associated with several cancers. During its natural life cycle, HPV16, the most common high-risk genotype, infects the epithelial basal cells in a process facilitated through a recently identified receptor, the annexin A2 heterotetramer (A2t). During infection, HPV16 also interacts with Langerhans cells (LC), the APC of the epithelium, inducing immune suppression, which is mediated by the HPV16 L2 minor capsid protein. Despite the importance of these virus-immune cell interactions, the specific mechanisms of HPV16 entry into LC and HPV16-induced immune suppression remain undefined. An N-terminal peptide of HPV16 L2 (aa 108-126) has been shown to specifically interact with A2t. In this study, we show that incubation of human LC with this peptide blocks binding of HPV16. Inhibiting this interaction with an A2t ligand or by small interfering RNA downregulation of A2t significantly decreases HPV16 internalization into LC in an L2-dependent manner. A2t is associated with suppression of LC maturation as demonstrated through attenuated secretion of Th1-associated cytokines and decreased surface expression of MHC class II on LC exposed to A2t. Conversely, small molecule inhibition of A2t prevents HPV16-induced suppression of LC immune function as indicated by significantly increased secretion of inflammatory cytokines and surface expression of CD86 in HPV16 treated LC pre-exposed to A2t inhibitors. These results demonstrate that HPV16 suppresses LC maturation through an interaction with A2t, revealing a novel role for this protein.

The evolving field of human papillomavirus receptor research: a review of binding and entry.

Human papillomaviruses (HPVs) infect epithelia and can lead to the development of lesions, some of which have malignant potential. HPV type 16 (HPV16) is the most oncogenic genotype and causes various types of cancer, including cervical, anal, and head and neck cancers. However, despite significant research, our understanding of the mechanism by which HPV16 binds to and enters host cells remains fragmented. Over several decades, many HPV receptors and entry pathways have been described. This review puts those studies into context and offers a model of HPV16 binding and entry as a framework for future research. Our model suggests that HPV16 binds to heparin sulfate proteoglycans (HSPGs) on either the epithelial cell surface or basement membrane through interactions with the L1 major capsid protein. Growth factor receptors may also become activated through HSPG/growth factor/HPV16 complexes that initiate signaling cascades during early virion-host cell interactions. After binding to HSPGs, the virion undergoes conformational changes, leading to isomerization by cyclophilin B and proprotein convertase-mediated L2 minor capsid protein cleavage that increases L2 N terminus exposure. Along with binding to HSPGs, HPV16 binds to α6 integrins, which initiate further intracellular signaling events. Following these primary binding events, HPV16 binds to a newly identified L2-specific receptor, the annexin A2 heterotetramer. Subsequently, clathrin-, caveolin-, lipid raft-, flotillin-, cholesterol-, and dynamin-independent endocytosis of HPV16 occurs.

pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life.

Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein-protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism.

Fully automated molecular mechanics based induced fit protein-ligand docking method.

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

A fast and accurate computational approach to protein ionization.

We report a very fast and accurate physics-based method to calculate pH-dependent electrostatic effects in protein molecules and to predict the pK values of individual sites of titration. In addition, a CHARMm-based algorithm is included to construct and refine the spatial coordinates of all hydrogen atoms at a given pH. The present method combines electrostatic energy calculations based on the Generalized Born approximation with an iterative mobile clustering approach to calculate the equilibria of proton binding to multiple titration sites in protein molecules. The use of the GBIM (Generalized Born with Implicit Membrane) CHARMm module makes it possible to model not only water-soluble proteins but membrane proteins as well. The method includes a novel algorithm for preliminary refinement of hydrogen coordinates. Another difference from existing approaches is that, instead of monopeptides, a set of relaxed pentapeptide structures are used as model compounds. Tests on a set of 24 proteins demonstrate the high accuracy of the method. On average, the RMSD between predicted and experimental pK values is close to 0.5 pK units on this data set, and the accuracy is achieved at very low computational cost. The pH-dependent assignment of hydrogen atoms also shows very good agreement with protonation states and hydrogen-bond network observed in neutron-diffraction structures. The method is implemented as a computational protocol in Accelrys Discovery Studio and provides a fast and easy way to study the effect of pH on many important mechanisms such as enzyme catalysis, ligand binding, protein-protein interactions, and protein stability.

LOOPER: a molecular mechanics-based algorithm for protein loop prediction.

We describe a new ab initio method and corresponding program, LOOPER, for the prediction of protein loop conformations. The method is based on a multi-step algorithm (developed as a set of CHARMm scripts) and uses standard CHARMm force field parameters for energy minimization and scoring. One of the main obstacles to ab initio computational loop modeling is the exponential growth of the backbone conformational states with the number of residues in the loop fragment. In contrast to many ab initio algorithms that use Monte-Carlo schemes or exhaustive sampling, LOOPER adopts a systematic search strategy with minimal sampling of the backbone torsion angles. During the initial conformational sampling, two representative states are sampled for each alanine-like residue based on pairs of initial varphi and psi dihedral angles, except glycine, which is sampled by four representative conformations. The initial (varphi, psi) values are determined from the analysis of a novel iso-energy contour map which is proposed as an alternative structure validation method to the widely used Ramachandra plot. The efficient sampling strategy is combined with energy minimization at each step. The initial energy minimization and scoring of the loop include the interactions of the protein core with loop backbone atoms only. Construction and optimization of the side-chain conformations is followed by a final ranking stage based on the CHARMm energy with a generalized Born solvation term as a scoring function. The systematic and efficient sampling strategy in LOOPER consistently finds near native loop conformations in our validation study. At the same time, the computational overhead of our method is significantly lower than many alternative approaches that use exhaustive search strategies.

The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactions.

The basic differences between the 20 natural amino acid residues are due to differences in their side-chain structures. This characteristic design of protein building blocks implies that side-chain-side-chain interactions play an important, even dominant role in 3D-structural realization of amino acid codes. Here we present the results of a comparative analysis of the contributions of side-chain-side-chain (s-s) and side-chain-backbone (s-b) interactions to the stabilization of folded protein structures within the framework of the CHARMm molecular data model. Contrary to intuition, our results suggest that side-chain-backbone interactions play the major role in side-chain packing, in stabilizing the folded structures, and in differentiating the folded structures from the unfolded or misfolded structures, while the interactions between side chains have a secondary effect. An additional analysis of electrostatic energies suggests that combinatorial dominance of the interactions between opposite charges makes the electrostatic interactions act as an unspecific folding force that stabilizes not only native structure, but also compact random conformations. This observation is in agreement with experimental findings that, in the denatured state, the charge-charge interactions stabilize more compact conformations. Taking advantage of the dominant role of side-chain-backbone interactions in side-chain packing to reduce the combinatorial problem, we developed a new algorithm, ChiRotor, for rapid prediction of side-chain conformations. We present the results of a validation study of the method based on a set of high resolution X-ray structures.

Molecular basis for the Cu2+ binding-induced destabilization of beta2-microglobulin revealed by molecular dynamics simulation.

According to experimental data, binding of the Cu(2+) ions destabilizes the native state of beta2-microglobulin (beta2m). The partial unfolding of the protein was generally considered an early step toward fibril formation in dialysis-related amyloidosis. Recent NMR studies have suggested that the destabilization of the protein might be achieved through increased flexibility upon Cu(2+) binding. However, the molecular mechanism of destabilization due to Cu(2+), its role in amyloid formation, and the relative contributions of different potential copper-binding sites remain unclear. To elucidate the effect of ion ligation at atomic detail, a series of molecular dynamics simulations were carried out on apo- and Cu(2+)-beta2m systems in explicit aqueous solutions, with varying numbers of bound ions. Simulations at elevated temperatures (360 K) provide detailed pictures for the process of Cu(2+)-binding-induced destabilization of the native structure at the nanosecond timescale, which are in agreement with experiments. Conformational transitions toward partially unfolded states were observed in protein solutions containing bound copper ions at His-31 and His-51, which is marked by an increase in the protein vibrational entropy, with TDeltaS(vibr) ranging from 30 to 69 kcal/mol. The binding of Cu(2+) perturbs the secondary structure and the hydrogen bonding pattern disrupts the native hydrophobic contacts in the neighboring segments, which include the beta-strand D2 and part of the beta-strand E, B, and C and results in greater exposure of the D-E loop and the B-C loop to the water environment. Analysis of the MD trajectories suggests that the changes in the hydrophobic environment near the copper-binding sites lower the barrier of conformational transition and stabilize the more disordered conformation. The results also indicate that the binding of Cu(2+) at His-13 has little effect on the conformational stability, whereas the copper-binding site His-31, and to a lesser extent His-51, are primarily responsible for the observed changes in the protein conformation and dynamics.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction.

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.