Comparison of the pH- and thermally-induced fluctuations of a therapeutic antibody Fab fragment by molecular dynamics simulation

Zhang, Cheng; Codina, Nuria; Tang, Jiazhi; Yu, Haoran; Chakroun, Nesrine; Kozielski, Frank; Dalby, Paul A.

doi:10.1016/j.csbj.2021.05.005

Cited by 11 publications

(11 citation statements)

References 71 publications

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“…[39] We speculated that the hydrophobic V H –V L and C H 1–C L interfaces [52] would be exposed upon breaking the native interfacial contacts and the inter-domain salt bridges. [53] All those effects could make non-canonical contacts between the Fab and Fc regions.…”

Section: Discussionmentioning

confidence: 99%

Getting Smaller by Denaturation: Acid-Induced Compaction of Antibodies

Imamura

Ooishi

Honda

2022

Preprint

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Protein denaturation is a ubiquitous process that occurs both in vitro and in vivo. While the molecular understanding of the denatured structures of proteins is limited, it is commonly accepted that the loss of unique intramolecular contacts makes proteins larger. Herein, we report compaction of the immunoglobulin G1 (IgG1) protein upon acid denaturation. Small-angle X-ray scattering coupled with size-exclusion chromatography revealed that IgG1 radii of gyration at pH 2 were ~75% of those at neutral pH. Scattering profiles showed a globular shape with approximately triaxial ellipsoids, supported by analytical ultracentrifugation. A theoretical model indicated that the acid denaturation of proteins with size reduction was energetically costly, and thus acid-induced compaction required an attractive force for domain reorientation. Such intramolecular aggregation may be widespread in multidomain proteins as non-canonical structures. Herein, we discuss the potential biological significance of these non-canonical structures of immunoglobulins.

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Section: Discussionmentioning

confidence: 99%

Getting Smaller by Denaturation: Acid-Induced Compaction of Antibodies

Imamura

Ooishi

Honda

2022

Preprint

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“…The increased Rg at low pH suggested a possible conformational instability of the A33 Fab, that exposed more aggregation-prone regions (APR) to the solvent and resulted in 10-fold faster in vitro aggregation at pH 4 compared to at pH 9 5 . Therefore, we evaluated all seven APRs of A33 Fab predicted previously 5 and their different responses to the various pH stresses, based on the current simulations starting from the two different crystal structures (Fig. 5a).…”

Section: Humanised Complementarity Determining Regions (Cdrs)mentioning

confidence: 99%

“…The A33 antigen expressed in metastatic colorectal cancers share 95% similarity 4 . Despite not progressing to a commercial release, A33 Fab is a highly characterised representative of the wider therapeutic Fab platform, due to its use as a testing ground for the development of analytics 5 , formulation strategies [6][7][8] , protein engineering tools 9 , and to gain a deeper understanding of destabilising mechanisms [10][11][12] . All of these studies have used the H/C226S variant which mutated cystein 226 in the heavy chain to serine, to eliminate intermolecular dimerisation 10 , and will be referred to simply as A33 Fab throughout.…”

Section: Introductionmentioning

confidence: 99%

pH-dependent crystal structures of a humanised therapeutic antibody Fab fragment: impact on molecular dynamics simulations and comparison to solution structures

Tang

Zhang

Castillo

et al. 2023

Preprint

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Antibody-fragment (Fab) therapy development has the potential to be accelerated by computational modelling and simulations that predict their target binding, stability, formulation, manufacturability, and the impact of further protein engineering. Such approaches are currently predicated on starting with good crystal structures that closely represent those found under the solution conditions to be simulated. A33 Fab, is an undeveloped immunotherapeutic antibody candidate that was targeted to the human A33 antigen homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. It is now used as a very well characterised testing ground for developing analytics, formulation and protein engineering strategies, and to gain a deeper understanding of mechanisms of destabilisation, representative of the wider therapeutic Fab platform. In this article, we report the structure of A33 Fab in two different crystal forms obtained at widely differing pH. These reveal important insights into the structural impact of pH on the ‘grafted’ complementarity-determining regions (CDRs) and the switch linker between the variable and the constant regions. It also enabled a direct evaluation of the impact of initial crystal structure selection on the outcomes of molecular dynamics simulations under different conditions, and their subsequent use for determining best fit solution structures using previously obtained small-angle x-ray scattering (SAXS) data. The differences in the two structures did not have a major impact on MD simulations regardless of the pH, other than a slight persistence of structure affecting the solvent accessibility of one of the predicted APR regions of A33 Fab. Interestingly, despite being obtained at pH 4 and pH 9, the two crystal structures were more similar to the SAXS solution structures obtained at pH 7, than to those at pH 4 or pH 9. Furthermore, the P65 crystal structure from pH 9 was also a better representation of the solution structures at any other pH, than was the P1 structure obtained at pH 4. Thus, while obtained at different pH, the two crystal structures may represent highly (P65) and lesser (P1) populated species that both exist at pH 7 in solution. These results now lay the foundation for confident MD simulations of A33 Fab that rationalise or predict behaviours in a range of conditions.

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“…To clarify the role of FXa in prothrombin activation, we focus on studying the binding modes of R271 and R320 cleavage site sequences to the catalytic triad of FXa, which comprises H236, D282 and S379. It is known that molecular dynamics (MD) simulations can be very helpful for studying biological processes at the molecular level [16] , [17] , [18] , [19] . However, MD simulations still suffer from high computational costs, which often leads to insufficient sampling for large systems.…”

Section: Introductionmentioning

confidence: 99%