Molecular Dynamics of Multivalent Soluble Antigen Arrays Support a Two-Signal Co-delivery Mechanism in the Treatment of Experimental Autoimmune Encephalomyelitis.

Many current therapies for autoimmune diseases such as multiple sclerosis (MS) result in global immunosuppression, rendering insufficient efficacy with increased risk of adverse side effects. Multivalent soluble antigen arrays, nanomaterials presenting both autoantigen and secondary inhibitory signals on a flexible polymer backbone, are hypothesized to shift the immune response toward selective autoantigenic tolerance to repress autoimmune disease. Two-signal co-delivery of both autoantigen and secondary signal were deemed necessary for therapeutic efficacy against experimental autoimmune encephalomyelitis, a murine model of MS. Dynamic light scattering and in silico molecular dynamics simulations complemented these studies to illuminate the role of two-signal co-delivery in determining therapeutic potential. Physicochemical characteristics such as particle size and molecular affinity for intermolecular interactions and chain entanglement likely facilitated cotransport of two signals to produce efficacy. These findings elucidate potential mechanisms whereby soluble antigen arrays enact their therapeutic effect and help to guide the development of future multivalent antigen-specific immunotherapies.

Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach.

Biosimilarity assessments are performed to decide whether 2 preparations of complex biomolecules can be considered "highly similar." In this work, a machine learning approach is demonstrated as a mathematical tool for such assessments using a variety of analytical data sets. As proof-of-principle, physical stability data sets from 8 samples, 4 well-defined immunoglobulin G1-Fragment crystallizable glycoforms in 2 different formulations, were examined (see More et al., companion article in this issue). The data sets included triplicate measurements from 3 analytical methods across different pH and temperature conditions (2066 data features). Established machine learning techniques were used to determine whether the data sets contain sufficient discriminative power in this application. The support vector machine classifier identified the 8 distinct samples with high accuracy. For these data sets, there exists a minimum threshold in terms of information quality and volume to grant enough discriminative power. Generally, data from multiple analytical techniques, multiple pH conditions, and at least 200 representative features were required to achieve the highest discriminative accuracy. In addition to classification accuracy tests, various methods such as sample space visualization, similarity analysis based on Euclidean distance, and feature ranking by mutual information scores are demonstrated to display their effectiveness as modeling tools for biosimilarity assessments.

Production, purification, and characterization of recombinant hFSH glycoforms for functional studies.

Previously, our laboratory demonstrated the existence of a ß-subunit glycosylation-deficient human FSH glycoform, hFSH(21). A third variant, hFSH(18), has recently been detected in FSH glycoforms isolated from purified pituitary hLH preparations. Human FSH(21) abundance in individual female pituitaries progressively decreased with increasing age. Hypo-glycosylated glycoform preparations are significantly more active than fully-glycosylated hFSH preparations. The purpose of this study was to produce, purify and chemically characterize both glycoform variants expressed by a mammalian cell line. Recombinant hFSH was expressed in a stable GH3 cell line and isolated from serum-free cell culture medium by sequential, hydrophobic and immunoaffinity chromatography. FSH glycoform fractions were separated by Superdex 75 gel-filtration. Western blot analysis revealed the presence of both hFSH(18) and hFSH(21) glycoforms in the low molecular weight fraction, however, their electrophoretic mobilities differed from those associated with the corresponding pituitary hFSH variants. Edman degradation of FSH(21/18)-derived ß-subunit before and after peptide-N-glycanase F digestion confirmed that it possessed a mixture of both mono-glycosylated FSHß subunits, as both Asn(7) and Asn(24) were partially glycosylated. FSH receptor-binding assays confirmed our previous observations that hFSH(21/18) exhibits greater receptor-binding affinity and occupies more FSH binding sites when compared to fully-glycosylated hFSH(24). Thus, the age-related reduction in hypo-glycosylated hFSH significantly reduces circulating levels of FSH biological activity that may further compromise reproductive function. Taken together, the ability to express and isolate recombinant hFSH glycoforms opens the way to study functional differences between them both in vivo and in vitro.

An overview of computational life science databases & exchange formats of relevance to chemical biology research.

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities.