Divining Deamidation and Isomerization in Therapeutic Proteins: Effect of Neighboring Residue

Irudayanathan, Flaviyan Jerome; Zarzar, Jonathan; Lin, Jasper; Izadi, Shahrokh

doi:10.1101/2021.07.26.453885

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…The occurrence of a deamidation site, however, cannot be simply derived from sequence alone and thus remains unpredictable without experimental characterization. For therapeutic proteins the rate of deamidation can strongly influence both shelf life and persistence time in the body, through either loss of function or stability, and therefore render them ineffective [ 30 , 31 ]. Therapeutic proteins affected include, but are not limited to, antibodies [ 32 ], vaccine antigens [ 33 , 34 , 35 ], peptides [ 36 ], adeno-associated virus (AAV) serotypes used for human gene therapy [ 37 ], human hormones [ 25 , 38 ] and enzymes [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Ertelt,

Mulligan,

Maguire

et al. 2024

PLoS Comput Biol

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Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta’s protein engineering toolbox that allow for the rational design of PTMs.

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Section: Introductionmentioning

confidence: 99%

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Ertelt,

Mulligan,

Maguire

et al. 2024

PLoS Comput Biol

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Two‐Dimensional Correlation and Two‐Dimensional Co‐Distribution Spectroscopies of Proteins

Pastrana¹,

Meuse²

2022

Encyclopedia of Analytical Chemistry

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Given the complexity of proteins, the broad nature of infrared bands, and the overlap of the HOH bending mode of water within the 1700–1600 cm −1 spectral region, it was often considered too difficult to include side‐chain vibrational modes in a description of a protein's dynamic behavior in solution. Yet, it is the roles of side‐chains that allow a description of a protein's dynamics to become a site‐specific understanding of stability and provide a basis for the development of protein drugs. This article describes how, in certain cases, site‐specific analysis of protein infrared spectra can be performed using two‐dimensional infrared correlation spectroscopy ( 2D‐COS ) developed by Dr. Isao Noda. The 2D‐COS algorithm spreads overlapped peaks within a spectral region of interest into a second dimension that allows for a complete evaluation of all of the peaks that change during a perturbation. 2D‐COS analysis has been implemented successfully for the study of proteins under different perturbations such as pressure, chemical, pH, concentration, and most commonly temperature. Initially, the interest was in determining conformational changes during thermal unfolding, which then led to evaluating solvent accessibility through hydrogen/deuterium exchange, leading to the use of isotope editing to probe local structural changes and interactions. Herein, we will explore the extent that both 2D‐COS and two‐dimensional co‐distribution spectroscopy ( 2D‐CDS ) have been implemented in the study of proteins. However, we were also keen to include recent advances to site‐specifically monitor protein deamidation in aqueous solution. Protein deamidation, like oxidation and glycosylation, is one of the processes that can initiate protein degradation pathways which could lead to decreased target binding, decreased stability, and possibly aggregation. These concerns have become part of the critical quality attributes ( CQAs ) evaluated during therapeutic protein development because they can potentially impact drug safety and efficacy. To date, tandem liquid chromatography–mass spectrometry ( LC‐MS ) techniques have been the primary method used to ascertain the presence and sites of deamidation in protein samples. However, the LC‐MS process requires multiple steps, including enzymatic digestion and peptide mapping. 2D‐COS and 2D‐CDS analyses provide an alternative that allows the determination of the deamidation sites and the evaluation of the stability of the protein populations within a sample without any sample preparation steps. The 2D‐COS method utilizes an innovative platform comprised of a quantum cascade laser microscope ( QCLM ), slide cell array, and software. The QCLM provides an enhanced signal/noise ratio and real‐time acquisition of hyperspectral images of an array of proteins in solution under accurate thermal control. The method monitors the deamidation process during thermal stress. Spectral data is then subject to 2D‐COS analysis to determine the extent of and site at which both asparagine and glutamine deamidation occur. In addition, 2D‐CDS analysis allows assessment of changes in stability in the distributions of the protein populations during the perturbation. The results presented here for the deamidation of the NISTmAb have been validated by LC‐MS to evaluate this new analytical tool. We foresee this approach as a required step in a therapeutic protein's developability assessment and release testing.

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Immunogenicity of Therapeutic Proteins

Yasir

Tripathi

Shukla

et al. 2023

Protein-Based Therapeutics

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Divining Deamidation and Isomerization in Therapeutic Proteins: Effect of Neighboring Residue

Cited by 3 publications

References 67 publications

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Two‐Dimensional Correlation and Two‐Dimensional Co‐Distribution Spectroscopies of Proteins

Immunogenicity of Therapeutic Proteins

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