Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Structure Based Prediction of Asparagine Deamidation Propensity in Monoclonal Antibodies.

ABSTRACT: Identification of asparagine (Asn) sites that are prone to deamidation is critical for the development of therapeutic monoclonal antibodies (mAbs). Despite a common chemical degradation pathway, the rates of Asn deamidation can vary dramatically among different sites, and prediction of the sensitive deamidation sites is still challenging. In this study, characterization of Asn deamidation for five IgG1 and five IgG4 mAbs under both normal and stressed conditions revealed dramatic differences in the Asn deamidation rates. A comprehensive analysis of the deamidation sites indicated that the deamidation rate differences could be explained by differences in the local structure conformation, structure flexibility and solvent accessibility. A decision tree was developed to predict the deamidation propensity for all Asn sites in IgG mAbs based on the analysis of these three structural parameters. This decision tree will allow potential Asn deamidation hot spots to be identified early in development.

SUBMITTER: Yan Q

PROVIDER: S-EPMC6152450 | biostudies-literature | 2018 Aug/Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Structure Based Prediction of Asparagine Deamidation Propensity in Monoclonal Antibodies.

Yan Qingrong Q Huang Maggie M Lewis Michael J MJ Hu Ping P

mAbs 20180726 6

Identification of asparagine (Asn) sites that are prone to deamidation is critical for the development of therapeutic monoclonal antibodies (mAbs). Despite a common chemical degradation pathway, the rates of Asn deamidation can vary dramatically among different sites, and prediction of the sensitive deamidation sites is still challenging. In this study, characterization of Asn deamidation for five IgG1 and five IgG4 mAbs under both normal and stressed conditions revealed dramatic differences in ...[more]

PMID: 29958069

Similar Datasets

Protein asparagine deamidation prediction based on structures with machine learning methods.

Project description:Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

| S-EPMC5521779 | biostudies-literature

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

Project description:The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R2 = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.

| S-EPMC6923510 | biostudies-literature

Erratum: Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

Project description:[This corrects the article DOI: 10.1016/j.omtm.2019.09.008.].

| S-EPMC7593340 | biostudies-literature

Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions.

Project description:Monoclonal antibodies (mAbs) and proteins containing antibody domains are the most prevalent class of biotherapeutics in diverse indication areas. Today, established techniques such as immunization or phage display allow for an efficient generation of new mAbs. Besides functional properties, the stability of future therapeutic mAbs is a key selection criterion which is essential for the development of a drug candidate into a marketed product. Therapeutic proteins may degrade via asparagine (Asn) deamidation and aspartate (Asp) isomerization, but the factors responsible for such degradation remain poorly understood. We studied the structural properties of a large, uniform dataset of Asn and Asp residues in the variable domains of antibodies. Their structural parameters were correlated with the degradation propensities measured by mass spectrometry. We show that degradation hotspots can be characterized by their conformational flexibility, the size of the C-terminally flanking amino acid residue, and secondary structural parameters. From these results we derive an accurate in silico prediction method for the degradation propensity of both Asn and Asp residues in the complementarity-determining regions (CDRs) of mAbs.

| S-EPMC4069079 | biostudies-literature

Prediction of protein deamidation rates from primary and three-dimensional structure.

Project description:A method for the quantitative estimation of instability with respect to deamidation of the asparaginyl (Asn) residues in proteins is described. The procedure involves the observation of several simple aspects of the three-dimensional environment of each Asn residue in the protein and a calculation that includes these observations, the primary amino acid residue sequence, and the previously reported complete set of sequence-dependent rates of deamidation for Asn pentapeptides. This method is demonstrated and evaluated for 23 proteins in which 31 unstable and 167 stable Asn residues have been reported and for 7 unstable and 63 stable Asn residues that have been reported in 61 human hemoglobin variants. The relative importance of primary structure and three-dimensional structure in Asn deamidation is estimated.

| S-EPMC31841 | biostudies-literature

An antigenic peptide produced by reverse splicing and double asparagine deamidation.

Project description:A variety of unconventional translational and posttranslational mechanisms contribute to the production of antigenic peptides, thereby increasing the diversity of the peptide repertoire presented by MHC class I molecules. Here, we describe a class I-restricted peptide that combines several posttranslational modifications. It is derived from tyrosinase and recognized by tumor-infiltrating lymphocytes isolated from a melanoma patient. This unusual antigenic peptide is made of two noncontiguous tyrosinase fragments that are spliced together in the reverse order. In addition, it contains two aspartate residues that replace the asparagines encoded in the tyrosinase sequence. We confirmed that this peptide is naturally presented at the surface of melanoma cells, and we showed that its processing sequentially requires translation of tyrosinase into the endoplasmic reticulum and its retrotranslocation into the cytosol, where deglycosylation of the two asparagines by peptide-N-glycanase turns them into aspartates by deamidation. This process is followed by cleavage and splicing of the appropriate fragments by the standard proteasome and additional transport of the resulting peptide into the endoplasmic reticulum through the transporter associated with antigen processing (TAP).

| S-EPMC3142003 | biostudies-literature

Structure and specificity of lamprey monoclonal antibodies.

Project description:Adaptive immunity in jawless vertebrates (lamprey and hagfish) is mediated by lymphocytes that undergo combinatorial assembly of leucine-rich repeat (LRR) gene segments to create a diverse repertoire of variable lymphocyte receptor (VLR) genes. Immunization with particulate antigens induces VLR-B-bearing lymphocytes to secrete antigen-specific VLR-B antibodies. Here, we describe the production of recombinant VLR-B antibodies specific for BclA, a major coat protein of Bacillus anthracis spores. The recombinant VLR-B antibodies possess 8-10 uniform subunits that collectively bind antigen with high avidity. Sequence analysis, mutagenesis, and modeling studies show that antigen binding involves residues in the beta-sheets lining the VLR-B concave surface. EM visualization reveals tetrameric and pentameric molecules having a central core and highly flexible pairs of stalk-region "arms" with antigen-binding "hands." Remarkable antigen-binding specificity, avidity, and stability predict that these unusual LRR-based monoclonal antibodies will find many biomedical uses.

| S-EPMC2542867 | biostudies-literature

Prediction and Reduction of the Aggregation of Monoclonal Antibodies.

Project description:Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.

| S-EPMC5397608 | biostudies-literature

Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder.

Project description:Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage "Protein and nucleic acid structure and sequence analysis".

| S-EPMC4682632 | biostudies-literature

Effect of protein structure on deamidation rate in the Fc fragment of an IgG1 monoclonal antibody.

Project description:The effects of secondary structure on asparagine (N) deamidation in a 22 amino acid sequence (369-GFYPSDIAVEWESNGQPENNYK-390) of the crystallizable (Fc) fragment of a human monoclonal antibody (Fc IgG1) were investigated using high-resolution ultra performance liquid chromatography with tandem mass spectrometry (UPLC/MS). Samples containing either the intact Fc IgG (approximately 50 kD) ("intact protein"), or corresponding synthetic peptides ("peptide") were stored in Tris buffer at 37 degrees C and pH 7.5 for up to forty days, then subjected to UPLC/MS analysis with high energy MS1 fragmentation. The peptide deamidated only at N(382) to form the isoaspartate (isoD(382)) and aspartate (D(382)) products in the ratio of approximately 4:1, with a half-life of approximately 3.4 days. The succinimide intermediate (Su(382)) was also detected; deamidation was not observed for the other two sites (N(387) and N(388)) in peptide samples. The intact protein showed a 30-fold slower overall deamidation half-life of approximately 108 days to produce the isoD(382) and D(387) products, together with minor amounts of D(382). Surprisingly, the D(382) and isoD(387) products were not detected in intact protein samples and, as in the peptide samples, deamidation was not detected at N(388). The results indicate that higher order structure influences both the rate of N-deamidation and the product distribution.

| S-EPMC2776945 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data