Xiaoling Wang

University of Delaware, Delaware, United States

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Publications (8)27.26 Total impact

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    ABSTRACT: With the rise of antibody based therapeutics as successful medicines, there is emerging need to understand fundamental antibody conformational dynamics and its implications towards stability of these medicines. Both de-glycosylation and thermal stress have been shown to cause conformational destabilization and aggregation in monoclonal antibodies. Here, we study instabilities caused by de-glycosylation and by elevated temperature (400K) by performing molecular dynamic simulations on a full length murine IgG2a mAb whose crystal structure is available in Protein Data Bank (PDB). C(α) -atom root mean square deviation and backbone root mean square fluctuation calculations show that de-glycosylation perturbs quaternary and tertiary structures in the C(H) 2 domain. In contrast, thermal stress pervades throughout the antibody structure and both Fabs and Fc regions are destabilized. The thermal stress applied in this study was not sufficient to cause large scale unfolding within the simulation time and most amino acid residues showed similar average solvent accessible surface area and secondary structural conformations in all trajectories. C(H) 3 domain is most successful at resisting the conformational destabilization. The simulations helped identify aggregation prone regions (APRs) which may initiate cross β motif formation upon de-glycosylation and upon applying thermal stress. De-glycosylation leads to increased backbone fluctuations and solvent exposure of a highly conserved APR located in the edge β-strand A of the C(H) 2 domains. Aggregation upon thermal stress is most likely initiated by two APRs that overlap with the complementarity determining regions (CDRs). This study has important implications for rational design of antibody based therapeutics that are resistant towards aggregation. Proteins 2012. © 2012 Wiley Periodicals, Inc.
    Proteins Structure Function and Bioinformatics 10/2012; · 3.34 Impact Factor
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    ABSTRACT: Protein based biotherapeutics have emerged as a successful class of pharmaceuticals. However, these macromolecules endure a variety of physicochemical degradations during manufacturing, shipping, and storage, which may adversely impact the drug product quality. Of these degradations, the irreversible self-association of therapeutic proteins to form aggregates is a major challenge in the formulation of these molecules. Tools to predict and mitigate protein aggregation are, therefore, of great interest to biopharmaceutical research and development. In this chapter, a number of such computational tools developed to understand and predict the various steps involved in protein aggregation are described. These tools can be grouped into three general classes: unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure based aggregation liabilities. Chapter sections introduce each class by discussing how these predictive tools provide insight into the molecular events leading to protein aggregation. The computational methods are then explained in detail along with their advantages and limitations.
    Methods in molecular biology (Clifton, N.J.) 01/2012; 899:425-51. · 1.29 Impact Factor
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    ABSTRACT: Because of their large, complex, and conformationally heterogeneous structures, biotherapeutics are vulnerable to several physicochemical stresses faced during the various processing steps from production to administration. In particular, formation of protein aggregates is a major concern. The greatest risk with aggregates arises from their potential to give rise to immunogenic reactions. Hence, it is desirable to bring forward biotherapeutic drug candidates that show low propensity for aggregation and, thus, improved developability. Here, we present a comprehensive review of computational studies into the sequence and structural factors that underpin protein and peptide aggregation. A number of computational approaches have been applied including coarse grain models, atomistic molecular simulations, and bioinformatic approaches. These studies have focused on both the mechanism of aggregation and the identification of potential aggregation-prone sequence and structural motifs. We also survey the computational tools available to predict aggregation in therapeutic proteins. The findings communicated here provide insights that could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility. These sequence-structure-based approaches can be applied to both novel as well as follow-on biotherapeutics.
    Journal of Pharmaceutical Sciences 07/2011; 100(12):5081-95. · 3.13 Impact Factor
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    Xiaoling Wang, Sandeep Kumar, Satish K Singh
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    ABSTRACT: To explore potential non-canonical disulfide linkages feasible in human IgG2 mAbs via molecular dynamics simulations of a model system, Hinge(++). Hinge(++) is derived from the crystal structure of a full-length murine IgG2a antibody by replacing its core hinge region with human IgG2 hinge. Fv and C(H)3 domains were discarded to speed up calculations. Eight independent simulations, grouped in four sets, were performed. In the control set, disulfide bonding is identical to canonical human IgG2 mAb. Different numbers of disulfide bonds were broken in the remaining three sets. Two Fabs move towards Fc asymmetrically repeatedly leading to spatial proximity of LC.Cys214 and HC.Cys128 residues in one Fab with Cys residues in the upper hinge region, which could initiate disulfide scrambling. Local dynamics place the eight hinge region Cys residues in a large number of proximal positions which could facilitate non-canonical inter- and intra- heavy chain disulfide linkages in the hinge region. Consistent with experimental studies, our simulations indicate inter-chain disulfide linkages in human IgG2 mAbs are degenerate. Potential rational design strategies to devise hinge stabilized human IgG2 mAbs are gleaned.
    Pharmaceutical Research 06/2011; 28(12):3128-44. · 4.74 Impact Factor
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    ABSTRACT: Biotherapeutics, including recombinant or plasma-derived human proteins and antibody-based molecules, have emerged as an important class of pharmaceuticals. Aggregation and immunogenicity are among the major bottlenecks during discovery and development of biotherapeutics. Computational tools that can predict aggregation prone regions as well as T- and B-cell immune epitopes from protein sequence and structure have become available recently. Here, we describe a potential coupling between aggregation and immunogenicity: T-cell and B-cell immune epitopes in therapeutic proteins may contain aggregation-prone regions. The details of biological mechanisms behind this observation remain to be understood. However, our observation opens up an exciting potential for rational design of de-immunized novel, as well as follow on biotherapeutics with reduced aggregation propensity.
    Pharmaceutical Research 03/2011; 28(5):949-61. · 4.74 Impact Factor
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    Sandeep Kumar, Xiaoling Wang, Satish K. Singh
    08/2010: pages 103 - 118; , ISBN: 9780470769829
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    Xiaoling Wang, Satish K Singh, Sandeep Kumar
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    ABSTRACT: To analyze contribution of short aggregation-prone regions (APRs), which may self-associate via cross-beta motif and were earlier identified in therapeutic mAbs, towards antigen recognition via structural analyses of antibody-antigen complexes. A dataset of 29 publically available high-resolution crystal structures of Fab-antigen complexes was collected. Contribution of APRs towards the surface areas of the Fabs buried by the cognate antigens was computed. Propensities of amino acids to occur in APRs and to be involved in antigen binding were compared. Coincidence between APRs and individual CDR loops was examined. All Fabs in the dataset contain at least one APR in CDR loops and adjacent framework beta-strands. The average contribution of APRs towards buried surface area of Fabs is 16.0 +/- 10.7%. Aggregation and antigen recognition may be coupled via aromatic residues (Tyr, Trp), which occur with high propensities in both APRs and antigen binding sites. APRs are infrequent in the heavy chain CDR 3 (H3) loops (7%), but are frequent in H2 loops (45%). Co-incidence of APRs with antigen recognition sites can potentially lead to the loss of function upon aggregation. Rational structure-based design or selection strategies are suggested for biotherapeutics with improved druggability while maintaining potency.
    Pharmaceutical Research 08/2010; 27(8):1512-29. · 4.74 Impact Factor
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    ABSTRACT: Aggregation of a biotherapeutic is of significant concern and judicious process and formulation development is required to minimize aggregate levels in the final product. Aggregation of a protein in solution is driven by intrinsic and extrinsic factors. In this work we have focused on aggregation as an intrinsic property of the molecule. We have studied the sequences and Fab structures of commercial and non-commercial antibody sequences for their vulnerability towards aggregation by using sequence based computational tools to identify potential aggregation-prone motifs or regions. The mAbs in our dataset contain 2 to 8 aggregation-prone motifs per heavy and light chain pair. Some of these motifs are located in variable domains, primarily in CDRs. Most aggregation-prone motifs are rich in beta branched aliphatic and aromatic residues. Hydroxyl-containing Ser/Thr residues are also found in several aggregation-prone motifs while charged residues are rare. The motifs found in light chain CDR3 are glutamine (Q)/asparagine (N) rich. These motifs are similar to the reported aggregation promoting regions found in prion and amyloidogenic proteins that are also rich in Q/N, aliphatic and aromatic residues. The implication is that one possible mechanism for aggregation of mAbs may be through formation of cross-beta structures and fibrils. Mapping on the available Fab-receptor/antigen complex structures reveals that these motifs in CDRs might also contribute significantly towards receptor/antigen binding. Our analysis identifies the opportunity and tools for simultaneous optimization of the therapeutic protein sequence for potency and specificity while reducing vulnerability towards aggregation.
    mAbs 01/2009; 1(3):254-67. · 5.28 Impact Factor