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Phillips A, Srinivas A, Prentoska I, O'Dea M, Kustrup M, Hurley S, Bruno S, Nguyen V, Lai PK. Teaching biologics design using molecular modeling and simulations. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2024; 52:299-310. [PMID: 38197506 DOI: 10.1002/bmb.21813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 11/30/2023] [Accepted: 12/30/2023] [Indexed: 01/11/2024]
Abstract
Teaching chemistry and biology students about biologics design remains challenging despite its increasing importance in pharmaceutical development. Monoclonal antibodies, commonly called mAbs, are the most popular biologics. They have been developed into drugs to treat various diseases in the past decades. Multiple challenges exist for designing proper formulations to stabilize mAbs, such as preventing aggregation and mitigating viscosity. Molecular modeling and simulations can improve pharmaceutical products by examining the interactions between mAbs and other compounds, such as excipients. To introduce students to biopharmaceuticals, eight students at the Stevens Institute of Technology participated in a semester-long course to learn the challenges of pharmaceutical development and different computational skills to study biologics design. The students started with a limited background in this field. Throughout one semester, they were introduced to various literature and software tools for modeling antibodies and studying their interactions with excipients. This paper aims to develop a course structure to be replicated at other universities and institutions to teach biopharmaceutical development to students.
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Affiliation(s)
- Andrew Phillips
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Anusha Srinivas
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Ilina Prentoska
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Margaret O'Dea
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Matthew Kustrup
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Sarah Hurley
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Savannah Bruno
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Vy Nguyen
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
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2
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Nabi F, Ahmad O, Khan A, Hassan MN, Hisamuddin M, Malik S, Chaari A, Khan RH. Natural compound plumbagin based inhibition of hIAPP revealed by Markov state models based on MD data along with experimental validations. Proteins 2024. [PMID: 38497314 DOI: 10.1002/prot.26682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024]
Abstract
Human islet amyloid polypeptide (amylin or hIAPP) is a 37 residue hormone co-secreted with insulin from β cells of the pancreas. In patients suffering from type-2 diabetes, amylin self-assembles into amyloid fibrils, ultimately leading to the death of the pancreatic cells. However, a research gap exists in preventing and treating such amyloidosis. Plumbagin, a natural compound, has previously been demonstrated to have inhibitory potential against insulin amyloidosis. Our investigation unveils collapsible regions within hIAPP that, upon collapse, facilitates hydrophobic and pi-pi interactions, ultimately leading to aggregation. Intriguingly plumbagin exhibits the ability to bind these specific collapsible regions, thereby impeding the aforementioned interactions that would otherwise drive hIAPP aggregation. We have used atomistic molecular dynamics approach to determine secondary structural changes. MSM shows metastable states forming native like hIAPP structure in presence of PGN. Our in silico results concur with in vitro results. The ThT assay revealed a striking 50% decrease in fluorescence intensity at a 1:1 ratio of hIAPP to Plumbagin. This finding suggests a significant inhibition of amyloid fibril formation by plumbagin, as ThT fluorescence directly correlates with the presence of these fibrils. Further TEM images revealed disappearance of hIAPP fibrils in plumbagin pre-treated hIAPP samples. Also, we have shown that plumbagin disrupts the intermolecular hydrogen bonding in hIAPP fibrils leading to an increase in the average beta strand spacing, thereby causing disaggregation of pre-formed fibrils demonstrating overall disruption of the aggregation machinery of hIAPP. Our work is the first to report a detailed atomistic simulation of 22 μs for hIAPP. Overall, our studies put plumbagin as a potential candidate for both preventive and therapeutic candidate for hIAPP amyloidosis.
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Affiliation(s)
- Faisal Nabi
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Owais Ahmad
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Adeeba Khan
- Zakir Hussain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India
| | - Md Nadir Hassan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Malik Hisamuddin
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Sadia Malik
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Ali Chaari
- Premedical Division, Weill Cornell Medicine Qatar, Qatar Foundation, Doha, Qatar
| | - Rizwan Hasan Khan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
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3
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Chowdhury AA, Manohar N, Lanzaro A, Kimball WD, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Xu S, Gillilan RE, Huang Q, Truskett TM, Johnston KP. Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations. Mol Pharm 2023; 20:5563-5578. [PMID: 37782765 DOI: 10.1021/acs.molpharmaceut.3c00484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.
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Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Richard E Gillilan
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Qingqiu Huang
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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Prass T, Garidel P, Blech M, Schäfer LV. Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations. J Chem Inf Model 2023; 63:6129-6140. [PMID: 37757589 PMCID: PMC10565822 DOI: 10.1021/acs.jcim.3c00947] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Indexed: 09/29/2023]
Abstract
The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.
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Affiliation(s)
- Tobias
M. Prass
- Center
for Theoretical Chemistry, Ruhr University
Bochum, D-44780 Bochum, Germany
| | - Patrick Garidel
- Boehringer
Ingelheim Pharma GmbH & Co. KG, Innovation Unit, PDB, D-88397 Biberach
an der Riss, Germany
| | - Michaela Blech
- Boehringer
Ingelheim Pharma GmbH & Co. KG, Innovation Unit, PDB, D-88397 Biberach
an der Riss, Germany
| | - Lars V. Schäfer
- Center
for Theoretical Chemistry, Ruhr University
Bochum, D-44780 Bochum, Germany
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5
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Mosca I, Pounot K, Beck C, Colin L, Matsarskaia O, Grapentin C, Seydel T, Schreiber F. Biophysical Determinants for the Viscosity of Concentrated Monoclonal Antibody Solutions. Mol Pharm 2023; 20:4698-4713. [PMID: 37549226 DOI: 10.1021/acs.molpharmaceut.3c00440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Monoclonal antibodies (mAbs) are particularly relevant for therapeutics due to their high specificity and versatility, and mAb-based drugs are hence used to treat numerous diseases. The increased patient compliance of self-administration motivates the formulation of products for subcutaneous (SC) administration. The associated challenge is to formulate highly concentrated antibody solutions to achieve a significant therapeutic effect, while limiting their viscosity and preserving their physicochemical stability. Protein-protein interactions (PPIs) are in fact the root cause of several potential problems concerning the stability, manufacturability, and delivery of a drug product. The understanding of macroscopic viscosity requires an in-depth knowledge on protein diffusion, PPIs, and self-association/aggregation. Here, we study the self-diffusion of different mAbs of the IgG1 subtype in aqueous solution as a function of the concentration and temperature by quasi-elastic neutron scattering (QENS). QENS allows us to probe the short-time self-diffusion of the molecules and therefore to determine the hydrodynamic mAb cluster size and to gain information on the internal mAb dynamics. Small-angle neutron scattering (SANS) is jointly employed to probe structural details and to understand the nature and intensity of PPIs. Complementary information is provided by molecular dynamics (MD) simulations and viscometry, thus obtaining a comprehensive picture of mAb diffusion.
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Affiliation(s)
- Ilaria Mosca
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | - Kévin Pounot
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | - Christian Beck
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | - Louise Colin
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | - Olga Matsarskaia
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | | | - Tilo Seydel
- Institut Max von Laue - Paul Langevin, 71 Av. des Martyrs, Grenoble 38042, France
| | - Frank Schreiber
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany
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6
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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7
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Chowdhury AA, Manohar N, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Kimball WD, Xu S, Lanzaro A, Truskett TM, Johnston KP. Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations. Mol Pharm 2023. [PMID: 37191356 DOI: 10.1021/acs.molpharmaceut.3c00023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (KCDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor Seff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The KCDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (KCDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in KCDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.
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Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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8
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Natesan R, Agrawal NJ. IgG1 and IgG4 antibodies sample initial structure dependent local conformational states and exhibit non-identical Fab dynamics. Sci Rep 2023; 13:4791. [PMID: 36959284 PMCID: PMC10036467 DOI: 10.1038/s41598-023-32067-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/22/2023] [Indexed: 03/25/2023] Open
Abstract
We have investigated the dynamics of two [Formula: see text]-immunoglobulin molecules, IgG1 and IgG4, using long all atom molecular dynamics simulations. We first show that the de novo structures of IgG1 and IgG4 predicted using AlphaFold, with no interactions between the fragment crystallizable (Fc) domain and the antigen fragment binding domain (Fab), eventually relaxes to a state with persistent Fc-Fab interactions that mirrors experimentally resolved structures. We quantified the conformational space sampled by antibody trajectories spawned from six different initial structures and show that the individual trajectories only sample states bound by a local minimum and display very little mixing in their conformational states. Furthermore, the dynamics of the individual Fab domains are strongly dependent on the initial crystal structure and isotype. In all conditions, we observe non-identical dynamics between the Fab arms in an antibody. For a six-bead coarse grained model, we show that non-covalent Fc-Fab interactions can modulate the stiffnesses associated with Fc-Fab distances, angles, and dihedral angles by up to three orders of magnitude. Our results clearly illustrate the inherent complexities in studying antibody dynamics and highlight the need to include non-identical Fab dynamics as an inherent feature in computational models of therapeutic antibodies.
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Affiliation(s)
| | - Neeraj J Agrawal
- Process Development, Amgen Inc., 360 Binney St, Cambridge, MA, 02141, USA.
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9
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Chowdhury A, Manohar N, Guruprasad G, Chen AT, Lanzaro A, Blanco M, Johnston KP, Truskett TM. Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model. J Phys Chem B 2023; 127:1120-1137. [PMID: 36716270 DOI: 10.1021/acs.jpcb.2c07616] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.
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Affiliation(s)
- Amjad Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Geetika Guruprasad
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Amy T Chen
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Marco Blanco
- Analytical Enabling Capabilities, Analytical R&D, Merck & Co., Inc., Rahway, New Jersey07065, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States.,Department of Physics, The University of Texas at Austin, Austin, Texas78712, United States
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10
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Evers A, Malhotra S, Bolick WG, Najafian A, Borisovska M, Warszawski S, Fomekong Nanfack Y, Kuhn D, Rippmann F, Crespo A, Sood V. SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters. Methods Mol Biol 2023; 2681:383-398. [PMID: 37405660 DOI: 10.1007/978-1-0716-3279-6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.
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Affiliation(s)
- Andreas Evers
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany.
| | - Shipra Malhotra
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | | | - Ahmad Najafian
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | - Maria Borisovska
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | | | | | - Daniel Kuhn
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany
| | - Friedrich Rippmann
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany
| | - Alejandro Crespo
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | - Vanita Sood
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
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11
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Shahfar H, O'Brien CJ, Budyak IL, Roberts CJ. Predicting Experimental B22 Values and the Effects of Histidine Charge States for Monoclonal Antibodies Using Coarse-Grained Molecular Simulations. Mol Pharm 2022; 19:3820-3830. [PMID: 36194430 DOI: 10.1021/acs.molpharmaceut.2c00337] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Static light scattering (SLS) was used to characterize five monoclonal antibodies (MAbs) as a function of total ionic strength (TIS) at pH values between 5.5 and 7.0. Second osmotic virial coefficient (B22) values were determined experimentally for each MAb as a function of TIS using low protein concentration SLS data. Coarse-grained molecular simulations were performed to predict the B22 values for each MAb at a given pH and TIS. To include the effect of charge fluctuations of titratable residues in the B22 calculations, a statistical approach was introduced in the Monte Carlo algorithm based on the protonation probability based on a given pH value and the Henderson-Hasselbalch equation. The charged residues were allowed to fluctuate individually, based on the sampled microstates and the influence of electrostatic interactions on net protein-protein interactions during the simulations. Compared to static charge simulations, the new approach provided improved results compared to experimental B22 values at pH conditions near the pKa of titratable residues.
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Affiliation(s)
- Hassan Shahfar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware19716, United States
| | - Christopher J O'Brien
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware19716, United States
| | - Ivan L Budyak
- Bioproduct Research and Development, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana46285, United States
| | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware19716, United States
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