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\begin{document}

\title{In Silico Process Development via Computational Modeling: Insights into
Molecular Biophysics to Advance and Improve Biologics Purification}



\author[1]{Francis  Insaidoo}%
\author[1]{John Welsh}%
\author[2]{Karol Lacki}%
\author[3]{Thomas Linden}%
\author[1]{David Roush}%
\affil[1]{Merck \& Co Inc}%
\affil[2]{Avitide, Inc}%
\affil[3]{Takeda Bio Development Center Ltd}%


\vspace{-1em}



  
  \date{May 07, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The goal of this research is to leverage computational molecular
biophysics to guide process development, reduce experimental burden and
focus purification activities on feasible targets. Here, we distill a
complex separation problem (e.g. chromatographic retention of monoclonal
antibodies) into a tangible model (ligand/protein complex), which is
computationally feasible while preserving enough detail (atomistic level
for interaction site) to support industrially relevant separation
challenges. Computational docking, coupled with molecular dynamics
simulation, produces results that are directionally consistent with
chromatography for proteins (mAb). This approach is generalizable and
can be applied to a range of ligands (AEX, CEX, and Mixed Mode). A
detailed model of the chromatography base matrix (agarose) was
constructed to obtain a biophysical understanding of potential
protein/base matrix interactions. The base matrix was then modified in
silico with ligands over a range of ligand densities representative of
commercial chromatography resins to generate an agarose/ligand complex.
A generic approach was developed to model the impact of avidity and
ligand density on mAb/ligand interaction. The results revealed that
increasing ligand density mask contributions of base matrix binding.
Increasing the number of ligands that can interact with mAb results in
more favorable free energy of binding or \selectlanguage{greek}Δ\selectlanguage{english}G (more negative) with a
limited incremental increase in \selectlanguage{greek}Δ\selectlanguage{english}G by increasing N (number of ligands
per agarose cluster) above three. Additionally, for protein/ligand
interactions at each binding site, not all ligands contribute equally to
the binding affinities or interaction energies and a redistribution of
binding interactions/energies occur as N increases. These observations
yield insights into the impact of avidity on retention (macroscopic
affinity measurement via k'). The generic approach described in this
manuscript can be leveraged to inform resin selection and design as well
as targeted ligand selection/purification development in a rational
manner.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Technological advancement in cell culture has led to an increase in
therapeutic protein titers over the last decade. This advancement has
shifted the attention towards downstream processes as the bottleneck in
the manufacture and production of biopharmaceuticals (Guiochon \&
Beaver; Hanke \& Ottens, 2014). For biologics, antibodies are extremely
complex and provide an almost unlimited design space to engineer binding
with target molecules (antigens) (Maier \& Labute). This results in a
diverse array of biophysical properties and a challenge for separation.
The relative ease with which new antibody candidates are generated and
low toxicity of their degradation products make this class of molecules
an excellent therapeutic agent (Tiller et al., 2008). Yet, the
structural complexity of these molecules and diverse biophysical
properties (including conformational flexibility) pose a significant
challenge to the selection of candidates and development of appropriate
purification modalities. An approach is proposed that can be applied to
determine feasibility of separation of specific degraded products (e.g.
oxidation, deamidation sites) \emph{via in silico} screening of ligands
to a targeted location. This \emph{in silico} screening approach is
analogous to the experimental developability (Lorenz et al., 2014; Yang
et al., 2013) approach and hence can be viewed as \emph{in
silico}developability.

The process of developing therapeutic antibodies require the generation
of many variants. The number of molecules generated make empirical
biophysical characterization of each molecule difficult, resource
intensive, and time consuming (Jarasch et al., 2015; Shah et al., 2015).
In-depth biophysical characterization of antibodies requires a
significant amount of protein mass to determine stability profiles,
purification, and formulation conditions. In addition, this process can
be very time consuming even with the advent of high-throughput screening
(HTS) approaches (Petroff et al., 2016). Thus, the ability to
use\emph{in silico} computational modeling will not only save time, but
also optimize candidate selection for the best therapeutic candidate and
accelerate the path from discovery to first-in-human clinical trials.
Computational approaches may also be able to assess mechanisms and
pathways that are not readily accessible experimentally and hence
provide additional insights into molecular development.

Recently published work has demonstrated the utility of computational
algorithms to isolate antibody complementary determining region (CDR)
loops and associated structural features that infer biological and
biophysical properties when bound to receptors and antigens (Morea,
Lesk, \& Tramontano, 2000). Further, computational modeling algorithms
to generate 3D structure of any novel antibody based on currently
available protein databank (PDB) structures are becoming increasingly
routine (Morea, Leplae, \& Tramontano, 1998; Morea, Tramontano, Rustici,
Chothia, \& Lesk, 1997). \emph{In silico} docking studies have yielded
results that are consistent with relative chromatographic retention
(k'), and surface properties for a range of biologics (Insaidoo et al.,
2015). The next step is to combine these advances in antibody homology
modeling and \emph{in silico} computational docking to understand
chromatographic separation and select appropriate ligands for bioprocess
development for a specific separation.

In this paper, we investigate the biophysical principles governing
protein-chromatographic ligand interactions. We show \emph{in
silico}docking studies in combination with molecular dynamics
simulations can inform bio-process development and biologics
purification. Specifically, we connect antibody biophysical properties
to ligand selection and impact of ligand density on retention and
selectivity.

\emph{In silico} models of agarose fibers functionalized with
chromatographic ligands showed increased binding affinity as a function
of increasing number of interacting ligands (N). The observed increase
is consistent with experimental correlation between resin ligand density
and k'. These results have implications not only for bioprocess
development but also for resin design and our understanding of the
principal criteria for biologics design for therapeutic efficacy and
successful downstream development.

Currently, the selection of lead candidates from a range of highly
potent efficacious antibodies relies on limited downstream process
development data (Jarasch et al., 2015; Lorenz et al., 2014). Even with
high through-put screening, it would still require significant resources
to fully screen and develop the operational design space for the
process. This paper maps the interaction between chromatographic ligands
and monoclonal antibodies at an atomic level using a general, extendable
computational approach. This \emph{in silico} approach to process
development allows us to discern the impact of subtle differences on
candidate selection and process development.

\textbf{Results and Discussion}

\emph{Antibody Structure and Surface Properties}

Molecular operating environment (MOE) from Chemical Computing Group
(Montreal, Canada) (Maier \& Labute), was used to generate homology
models of three antibodies (referred to here as mAb-1, mAb-2, and
mAb-3). The isoelectric point (pI), percent surface area distribution of
hydrophobic and electrostatic surface properties (positive and negative
patches) were calculated. Patch analysis of these molecules indicate
that the total patches of the molecules are relatively constant (Table
1). Yet, the patch distribution for the complementary determining region
(CDR) varied amongst the three molecules (Table 1). This aligns with the
fact that the constant regions of the molecules are 98\% identical. The
variability of the target antigen and recognition site accounts for the
diversity in the CDR region, even though it forms a small fraction of
the entire molecule (approximately 10\%). Thus, any variations between
the molecules resulting from the CDR are diminished when averaged over
the entire molecule (e.g. pI). This effectively masks the specific
contribution of the CDR to chromatographic retention and separation if
their contribution is not decoupled from the entire molecule (Figure 1).

\emph{Ligand Density and Resin Surface Design}

Previous studies demonstrated a strong correlation between
chromatographic ligand docking binding affinities (surrogate for \selectlanguage{greek}Δ\selectlanguage{english}G) and
chromatographic separation (k') (Insaidoo et al., 2015). While these
results may hold true for a smaller biological molecule, the complexity
of possible ligand interaction sites on an antibody makes such an
approach challenging (Hanke \& Ottens, 2014). To address the complexity
in chromatographic separation, a molecular model of agarose ligand
complex was formulated to capture a broader range of ligand densities
and interaction sites. The ligands were attached to the resin surface
with a separation distance of 10-14 \selectlanguage{ngerman}Å, ranging from one attached ligand
to six ligands per resin area. From our internal calculation, these
agarose models span the range of low to medium and high-density resin
that is commercially available (Figure 2A).

\emph{Protein-Chromatographic Ligand Interaction}

For chromatographic separation, ligands are expected to interact with
different regions of the molecules based on their surface properties and
inherit affinity for a given region. To answer the question of preferred
protein-ligand interactions that lead to separation, different docking
studies were performed. Multiple single ligand docking assessments were
performed to the entire antibody surface, the Fab region, the CDR
region, and the Fc region. The contact sites for protein/ligand and
associated affinities were ranked based on interaction energy. Figure 1A
shows that the region with the highest (more negative \selectlanguage{greek}Δ\selectlanguage{english}G) protein-ligand
interaction energies for the top three binding sites differed for the
three antibodies. There is a correlation between \emph{in silico}binding
affinities (\selectlanguage{greek}Δ\selectlanguage{english}G) and experimentally determined protein retention.
Previous studies have demonstrated that a single post translational
modifications (PTM) in the CDR region not only affect antibody potency
but can have significant effect on chromatographic separation (Kern,
Mende, Denefeld, Sackewitz, \& Chelius, 2014). Thus, chromatographic
ligand binding to CDR region can elucidate potential chromatographic
separation (Figure 1B).

\emph{Effect of Ligand Density on Chromatographic Retention}

Despite the correlation between single chromatographic ligand binding
affinities and experimental data, chromatographic separation is much
more complex than a single ligand-protein interaction. A single ligand
binding utilizes both the head group and ligand backbone that attaches
to the resin (Figure 2 B and C). Yet, on a chromatographic scale, ligand
density, separation distance from resin to head group, multiple
interaction effect (potential avidity and binding optimization), and
compensatory protein surface properties could all be critical
contributions to protein retention and separation (Hanke \& Ottens,
2014). The flexibility of the spacer arm could also contribute to
interaction of the ligand and the protein. To understand this mechanism
and demonstrate the effect of the aforementioned factors on protein
binding, we utilized an agarose surface model which was functionalized
with different chromatographic ligands. This mimic a patch of
chromatographic resin and how it interacts with proteins. In addition,
the ligands were attached to the resin surface with a separation
distance of 10-14 \selectlanguage{ngerman}Å, ranging from one attached ligand to six ligands per
section of resin (supplementary figure 1). These agarose models span the
range of low to medium and high-density resins that are commercially
available (Figure 2A). The entire agarose-ligand complex could scan the
surface of the antibody using the sub-region classification outlined
above to identify potential binding sites and cumulative binding
affinities -- effectively a combination of avidity and
binding/interaction energies. A control binding study was done using
only the agarose resin to investigate the contribution of the agarose to
the calculated protein-ligand binding affinities as agarose has been
reported to support protein purification previously in the literature
(Crone, 1974).

Figure 3 shows a plot of the change in binding affinity over the
un-functionalized agarose. For each chromatographic modality,
the\emph{in silico} binding affinities (\selectlanguage{greek}Δ\selectlanguage{english}G) to the antibody-ligand
complex increased as the number of attached ligands per agarose
increased. Further, the different chromatographic ligands had different
binding affinities with Capto MMC showing the strongest interactions
(Figure 3).

\emph{Individual ligand Contribution to Increased Binding with
Increasing Ligand Density}

We next examined the structural conformation adopted by the
agarose-ligand complex to determine the compensatory binding
conformations adopted by both the protein and the ligand to achieve the
desired interaction. Table 2 indicates that as the number of ligands per
agarose increases, the contribution of each individual ligand to the
overall binding score decreases. This is because contributions from the
backbone of the ligand are sterically hindered from binding to the
antibody and increasing the interaction energy. At the highest ligand
density (six ligands per agarose), not all the ligands contact the
protein equally or at all. In addition, the ligands that made contact
differed in their binding score due to differences in the protein
interaction site. Thus, once a strong interaction is made by one ligand,
the remaining ligands need to interact with proximal sites and hence
limit optimal interaction for the remaining ligands. For the low ligand
density agarose, the docking can adjust to incorporate the ligand
backbone or agarose in the final binding conformation and affinity.
Figure 4A, shows the binding site in the context of the overall antibody
molecule. As the number of ligands attached to the agarose molecule
increases, the contribution of the agarose and ligand backbone to the
total binding affinity decreases (Figure 2B). In doing so, the ligand
backbone cannot contribute to the total and individual ligand binding
affinities. Further, beyond two ligand per agarose, there is a
sub-optimal utilization of the attached ligand. This means that in
making optimal contacts to the protein one side of the ligand complex is
directed away from the protein and cannot make contact, thus excluding
one ligand each time.

\emph{Ligand Density Docking Score Correlate Directionally to
Experimental Retention studies}

In a packed bed, the number of possible protein-ligand interaction sites
will exceed one. Yet, for each given patch of resin with a specific
ligand density, there is an optimal number of possible interactions and
associated interaction energy. Data from the computational docking
studies suggest that there is an optimal number of possible interactions
at each ligand density. To address this assumption, the interaction
energy for the agarose ligand complex was compared to experimentally
determined retention data. From the retention studies, the capacity
factor (k') for mAb-1 was determined for the same ligand (Capto MMC) and
at different ligand densities (low, medium, and high). Figure 5
indicates that there is a directional correlation between the
experimentally determined values of k' and the binding affinities
determined \emph{in silico} . As the ligand density increased the
binding affinities increased and the k' also increased. Figure 5A, shows
that the calculated increase in \emph{in silico} binding affinities for
the agarose-ligand complex are greater than the baseline agarose
interaction to the protein and higher than a single ligand docking.

To examine the effect of local perturbations and solvent components on
binding affinity, we performed a droplet molecular dynamics simulation
of the ligand binding site with a solvation shell ratio of 10 \selectlanguage{ngerman}Å. Using
droplet molecular dynamics simulation allowed for the exploration of
dynamics near and around the docking site. This method was preferred
over a solvation box because of the size and complexity of simulating a
fully solvated antibody system. Constraints were applied to residues
outside the solvation droplet to dampen out dynamics as we move outside
the water droplet. Figure 6 shows that during the course of a 30 ns
unconstrained atomistic molecular dynamics simulation (Insaidoo et al.,
2011), the six-ligand agarose remained bound to the antibody with an
average affinity of 18.26 (±1.05) kcal/mol. This value is very close to
the original induced fit binding score for Capto MMC bound to mAb1
(19.08 kcal/mol), indicating that the binding interactions retained
consistent.

\textbf{Conclusion}

In conclusion, our data shows that computational modeling can be used to
gain meaningful insight into structural properties of an antibody that
is critical for process development. This high through-put structural
information can be used as an additional parameter to select antibodies
that will lead to successful development downstream. While the overall
structural features are important, a better mechanistic understanding of
protein-chromatographic resin is gained by coupling \emph{in
silico}docking studies to molecular dynamics and experimental data. We
demonstrated using this approach that different regions of an antibody
contribute at different levels to the overall column retention and
selectivity. This is because different high affinity binding sites were
observed base on the ligand density of the agarose-ligand complex.
Further, we addressed the impact of ligand density on overall
protein-resin binding and how the attached ligands to the resin
compensate to maximize its interaction with the protein. Higher overall
binding affinities are achieved at higher ligand densities by a
cumulative effect of lower individual binding affinities (avidity)
(Figure 4). This computational approach to evaluating protein ligand
interaction has broader implications for biologics development (from
lead candidate selection through purification to formulation) and
commercial chromatographic resin design and head groups selection and
optimization.

\textbf{Materials and Methods}

\textbf{Antibody Modeling}

Homology modeling of antibodies used in this study was generated by
using BLAST algorithm (National Center for Biotechnology Information) to
compare our sequence against the non-redundant protein data bank (PDB)
for Fab and Fc structures that have a high level of sequence similarity
with the target molecule. High similarity structures were found for the
framework region and CDR loop regions separately. Once pdb files are
identified that best represent the molecule of interest, MOE software
was used to graft the structures together and mutated to the new
targeted sequence. The final structure was energy minimized. The final
structure phi and psi angles were plotted to ensure that they fall
within what is allowed for proteins (reference Ramachandran plot)
(Ramachandran, Ramakrishnan, \& Sasisekharan, 1963). The final Fab and
Fc structure were superimposed on a full antibody structure, the linker
sequence was connected, and disulfide bonds were formed. The overall
structure is energy minimized to reduce backbone and side chain strain
using steepest decent algorithm.

\textbf{Docking Studies}

Docking and protein structure preparation was performed on Molecular
Operating Environment (MOE 2015.10 and 2016.08) from Chemical Computing
Group, Canada. Protein-Ligand docking was designed such that the ligands
are placed in the site using the Triangle Matcher method, and ranked
with the London \selectlanguage{greek}Δ\selectlanguage{english}G (Wright, Anderson, Shadnia, Durst, \&
Katzenellenbogen, 2013) scoring function. The initial poses and score
were then refined for energy minimization in the binding pocket and
rescored using GBVI/WSA \selectlanguage{greek}Δ\selectlanguage{english}G (Corbeil, Williams, \& Labute, 2012) scoring
function under the Amber12:EHT forcefield set. The target protein and
ligands were allowed to sample alternative conformations to allow for
induced-fit binding of protein-ligand complex. The highest docking score
conformation was used in molecular dynamics (MD) simulation and a final
binding affinity reported from the ensemble average using GBVI/WSA \selectlanguage{greek}Δ\selectlanguage{english}G
calculation. Accessible solvent area (ASA) was calculated from a single
molecular structure using a lattice and a 0-potential contour of the van
der Waals potential. Using the median of each potential value as the
iso-contour cutoff, the surface was then extended to adjacent grid
triangles to smoothen the patch transition (Jorgensen, Maxwell, \&
TiradoRives, 1996; Wildman \& Crippen, 1999). Docking scores were
determined for antibody molecules 1, 2, and 3 all IgG1 (mAb1 (pI =
7.59), mAb2 (pI = 7.96), and mAb3 (pI = 8.38)) and protonated at pH 5.5.

\textbf{Retention Mapping Assessment}

Retention mapping was conducted with mAb-1 for all resins using 1 mL
HiTrap columns with a GE Healthcare \selectlanguage{ngerman}ÄKTA\selectlanguage{english} avant 25 system. Protein was
loaded with a 4 min residence time, and protein concentration was
monitored through A280 measurements. The feed material (purified
antibody) was used as the absorbance at 100\% breakthrough.

\textbf{Molecular Dynamics Simulation}

Molecular dynamics (MD) simulations were performed and analyzed with the
Molecular Operating Environment (MOE) (Chemical Computing Group, Canada)
and NAMD 2.7 suite {[}http://www.ks.uiuc.edu/Research/namd{]} (Phillips
et al., 2005). For molecular dynamics simulation with the
antibody-ligand complexes, starting coordinates were from homology
modeled structures. Hydrogen atoms were added to the starting structures
using the LigX protonation tool of the MOE 2016.08 suite. Using the
solvation algorithm in MOE, a 10 A region around the protein-ligand
complex was immersed in a TIP3P water droplet such that protein atoms
outside the droplet were fixed to dampen long range dynamic effect.
Sodium cations were added for electroneutrality. Additional Sodium and
Chloride ions were added to a final concentration of 0.15 M. This
resulted in systems consisting of 18,780 atoms for the
mAb1\emph{,}17,788 atoms for mAb2, and 16,469 atoms for mAb3. Dynamics
simulations were then performed using NAMD, with parameters from the
Amber10:EHT forcefield set. Equilibration consisted of 15, 000 steps of
conjugate gradient energy minimization, followed by 50 ps of MD with
restraints applied to the protein to equilibrate the water. The systems
were then warmed to 300 K over a total of 18 ns with a 50 K increment
and equilibration at each temperature step. This was followed by
unrestrained production run of 30 ns, which is enough for the
determination of empirically relevant binding affinities (Salvalaglio,
Zamolo, Busini, Moscatelli, \& Cavallotti, 2009). The SHAKE algorithm
was used, allowing a 2 fs time-step. Long-range electrostatics were
treated via the particle mesh Ewald method. Trajectory analysis was
carried out with the MOE analysis tool and in-house scripts.

\textbf{Reference}

Corbeil, C. R., Williams, C. I., \& Labute, P. (2012). Variability in
docking success rates due to dataset preparation. \emph{J Comput Aided
Mol Des, 26} (6), 775-786. doi:10.1007/s10822-012-9570-1

Crone, H. D. (1974). Ion-Exclusion Effects on the Chromatography of
Acetylcholinesterase and other Proteins on Agarose Columns at Low Ionic
Strength. \emph{Journal of Chromatography, 92} (1974).

Guiochon, G., \& Beaver, L. A. Separation science is the key to
successful biopharmaceuticals. \emph{J Chromatogr A, 1218} (49),
8836-8858. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=21982447

Hanke, A. T., \& Ottens, M. (2014). Purifying biopharmaceuticals:
knowledge-based chromatographic process development. \emph{Trends
Biotechnol, 32} (4), 210-220. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=24630477

Insaidoo, F. K., Borbulevych, O. Y., Hossain, M., Santhanagopolan, S.
M., Baxter, T. K., \& Baker, B. M. (2011). Loss of T cell antigen
recognition arising from changes in peptide and major histocompatibility
complex protein flexibility: implications for vaccine design. \emph{J
Biol Chem, 286} (46), 40163-40173. doi:10.1074/jbc.M111.283564

Insaidoo, F. K., Rauscher, M. A., Smithline, S. J., Kaarsholm, N. C.,
Feuston, B. P., Ortigosa, A. D., . . . Roush, D. J. (2015). Targeted
purification development enabled by computational biophysical
modeling.\emph{Biotechnol Prog, 31} (1), 154-164. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=25482184

Jarasch, A., Koll, H., Regula, J. T., Bader, M., Papadimitriou, A., \&
Kettenberger, H. (2015). Developability assessment during the selection
of novel therapeutic antibodies. \emph{J Pharm Sci, 104} (6), 1885-1898.
Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=25821140

Jorgensen, W. L., Maxwell, D. S., \& TiradoRives, J. (1996). Development
and testing of the OPLS all-atom force field on conformational
energetics and properties of organic liquids. \emph{Journal of the
American Chemical Society, 118} (45), 11225-11236. doi:Doi
10.1021/Ja9621760

Kern, W., Mende, R., Denefeld, B., Sackewitz, M., \& Chelius, D. (2014).
Ion-pair reversed-phase high performance liquid chromatography method
for the quantification of isoaspartic acid in a monoclonal
antibody.\emph{J Chromatogr B Analyt Technol Biomed Life Sci, 955-956} ,
26-33. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=24631807

Lorenz, T. P., Fiaux, J. P., Heitmann, D. P., Gupta, K. P., Kocher, H.
P. P., Knopf, H. P., \& Hartmann, S. P. (2014). Developability
Assessment of Biologics by Integrated Biologics Profiling.\emph{American
Pharmaceutical Review} .

Maier, J. K., \& Labute, P. Assessment of fully automated antibody
homology modeling protocols in molecular operating
environment.\emph{Proteins, 82} (8), 1599-1610. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=24715627

Morea, V., Leplae, R., \& Tramontano, A. (1998). Protein structure
prediction and design. \emph{Biotechnol Annu Rev, 4} , 177-214.
Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=9890141

Morea, V., Lesk, A. M., \& Tramontano, A. (2000). Antibody modeling:
implications for engineering and design. \emph{Methods, 20} (3),
267-279. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=10694450

Morea, V., Tramontano, A., Rustici, M., Chothia, C., \& Lesk, A. M.
(1997). Antibody structure, prediction and redesign. \emph{Biophys Chem,
68} (1-3), 9-16. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=9468606

Petroff, M. G., Bao, H., Welsh, J. P., van Beuningen-de Vaan, M.,
Pollard, J. M., Roush, D. J., . . . Linden, T. O. (2016). High
throughput chromatography strategies for potential use in the formal
process characterization of a monoclonal antibody. \emph{Biotechnol
Bioeng, 113} (6), 1273-1283. doi:10.1002/bit.25901

Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E.,
Villa, E., . . . Schulten, K. (2005). Scalable molecular dynamics with
NAMD. \emph{Journal of Computational Chemistry, 26} (16), 1781-1802.
doi:Doi 10.1002/Jcc.20289

Ramachandran, G. N., Ramakrishnan, C., \& Sasisekharan, V. (1963).
Stereochemistry of polypeptide chain configurations. \emph{J Mol Biol,
7} , 95-99. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=13990617

Salvalaglio, M., Zamolo, L., Busini, V., Moscatelli, D., \& Cavallotti,
C. (2009). Molecular modeling of Protein A affinity
chromatography.\emph{Journal of Chromatography A, 1216} (50), 8678-8686.
doi:DOI 10.1016/j.chroma.2009.04.035

Shah, K. A., Clark, J. J., Goods, B. A., Politano, T. J., Mozdzierz, N.
J., Zimnisky, R. M., . . . Love, K. R. (2015). Automated pipeline for
rapid production and screening of HIV-specific monoclonal antibodies
using pichia pastoris. \emph{Biotechnol Bioeng, 112} (12), 2624-2629.
Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=26032261

Tiller, T., Meffre, E., Yurasov, S., Tsuiji, M., Nussenzweig, M. C., \&
Wardemann, H. (2008). Efficient generation of monoclonal antibodies from
single human B cells by single cell RT-PCR and expression vector
cloning. \emph{J Immunol Methods, 329} (1-2), 112-124. Retrieved from
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=17996249

Wildman, S. A., \& Crippen, G. M. (1999). Prediction of physicochemical
parameters by atomic contributions. \emph{Journal of Chemical
Information and Computer Sciences, 39} (5), 868-873. doi:Doi
10.1021/Ci990307l

Wright, J. S., Anderson, J. M., Shadnia, H., Durst, T., \&
Katzenellenbogen, J. A. (2013). Experimental versus predicted affinities
for ligand binding to estrogen receptor: iterative selection and
rescoring of docked poses systematically improves the
correlation.\emph{J Comput Aided Mol Des, 27} (8), 707-721.
doi:10.1007/s10822-013-9670-6

Yang, X., Xu, W., Dukleska, S., Benchaar, S., Mengisen, S., Antochshuk,
V., . . . Ambrogelly, A. (2013). Developability studies before
initiation of process development: improving manufacturability of
monoclonal antibodies. \emph{MAbs, 5} (5), 787-794.
doi:10.4161/mabs.25269

\textbf{FIGURE LEGENDS:}

\textbf{FIGURE 1 Surface representation of antibodies indicating
different binding sites for chromatographic ligands.} (A) Shows the
highest affinity sites for Capto MMC to three different antibodies. The
typical interaction energy values for the top three binding sites range
from -5 to -7 kcal/mol. The region with the highest interaction energy
differs as a function of molecule. (B) The CDR region showed the highest
interaction for Capto MMC ligand outside of the top three binding sites
for each molecule. The interaction energies ranged from -4 to -5
kcal/mol. In addition, the interaction site, interacting residues, and
surface area explored are diverse as should be expected for a
hypervariable region.

\textbf{FIGURE 2 Structure of agarose base matrix and attached
ligands.}(A) A surface representation of the agarose base matrix and the
attached ligands. The ligands are spaced evenly around the resin from a
single ligand attachment to six ligands per resin. (B) A cartoon
representation of the attachment site of a chromatographic ligand to
resin. The head group of the ligand is separated from the base by a
linker (backbone). (C) Structure of the ligands used in this study.
Capto\textsuperscript{TM} adhere and Capto\textsuperscript{TM} MMC are
produced by GE Healthcare, USA and CEX (HyperD\textsuperscript{TM} F) is
produced by PALL Life Science, USA.

\textbf{FIGURE 3} \textbf{Comparison of the interaction energy between
different agarose-ligand densities against mAb-1 compared to
un-functionalized agarose.} The mAb-ligand docking was performed at pH
5.5 and compared between modalities. All agarose-ligand complex had an
improved interaction energy over un-functionalized agarose (base
matrix). The general trend is that, increasing the number of ligands
increase the interaction energy.

\textbf{FIGURE 4} \textbf{The interaction site of functionalized agarose
(agarose-ligand complex) bound to mAb-1.} (A) A close-up view of the
interaction site between agarose-ligand complex bound to antibody
(mAb-1). There is a range of interaction energies that is attributed to
the head group of each attached ligand making interactions with specific
sites instead of the entire ligand (head group and backbone) (B) Highest
affinity interaction site of agarose-ligand complex in the context of
the entire antibody. A solvation droplet was designed to cover the area
in blue to simulate any changes in interaction energy as a function of
molecular dynamics.

\textbf{FIGURE 5} \textbf{Comparison of experimental binding affinities
with \emph{in silico} interaction energies for mAb-1.} (A) Interaction
energy of Free Ligand (MMC) (Capto MMC ligand un-attached to a base
matrix), agarose, and agarose with Capto MMC attached (Agarose MMC also
called agarose-ligand complex). As the number of Capto MMC ligands
attached to the agarose increase, the overall interaction energy of the
functionalized agarose increases. (B) Chromatographic retention
(\emph{k'} ) determined from retention mapping assessment with
increasing ligand density shows a similar trend as docking interaction
energies.

\textbf{FIGURE 6} \textbf{Molecular dynamics simulation of mAb-1 ligand
complex compared to docking score.} The interaction energies from the
molecular simulation was slightly lower than the docking score but
remained constant for the entire length of the simulation.

\textbf{Hosted file}

\verb`Tables.docx`  available at \url{https://authorea.com/users/325488/articles/453482-in-silico-process-development-via-computational-modeling-insights-into-molecular-biophysics-to-advance-and-improve-biologics-purification}

\textbf{Hosted file}

\verb`Figures.docx`  available at \url{https://authorea.com/users/325488/articles/453482-in-silico-process-development-via-computational-modeling-insights-into-molecular-biophysics-to-advance-and-improve-biologics-purification}

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