Overview

Protein Modeling

Protein Structure Prediction

To predict the binding efficiency of our Medi8852 scFv sequence against the HA molecule of H1N1, we aimed to model and assess the binding complex through protein modeling techniques. Initially, we procured the PDB structure of H1N1 (PDB: 1RUZ) and proceeded to construct a homology model of Medi8852 scFv using Swiss-Model.

The structure of Medi8852 scFv was built based on the template X-ray diffraction structure of the MEDI8852 Fab Fragment (PDB: 5JW5), as depicted in Figure 1. Following the model construction, a quality assessment of the Medi8852 structure was conducted using the PROCHECK module of the SAVES v6.1 server, as shown in Figure 2.

The model demonstrated good stereochemical geometry, with no residues falling into disallowed regions and an overall acceptable G-factor. Consequently, it was deemed suitable for subsequent docking and simulation steps.

Structural validation was performed using PROCHECK, and the results are summarized below:

Overall,these results indicate that the selected complex had good stereochemical quality, with no residues falling in the disallowed region and only minimal deviations detected.

Molecular Docking of MEDI8852 scFv with H1N1 Hemagglutinin

The pre-docking phase involved preparing both the receptor (H1N1) and the ligand (Medi8852 scFv) for molecular docking. Using AutoDock, the PDB file of H1N1 (PDB: 1RUZ) was imported, water molecules were removed, hydrogens were added, and the structure was prepared as the receptor. It was then exported as a PDBQT file, completing the receptor preparation. Similarly, the PDB file of Medi8852 (PDB: 5JW5) was imported, hydrogens were added, the structure was prepared as the ligand, torsion bonds were identified, and the file was exported as a PDBQT file. This process completed the preparation (cleaning) of both docking molecules.

The initial attempt to perform docking using AutoDock failed due to the large molecular weight of the ligand. Large ligands, with their high flexibility and numerous rotatable bonds, significantly increase computational complexity, often exceeding AutoDock's capabilities for conformational sampling. Consequently, AutoDock was unable to complete the docking process successfully. To address this, we switched to ZDOCK, an alternative docking tool that provides better support for large molecules.

ZDOCK 3.0.2 was employed for the initial rigid-body docking of the MEDI8852 scFv and the H1N1 HA protein. The cleaned structures (as prepared in AutoDock,) were used as input for ZDOCK.

Parameters:

Rotational sampling: 6°

Translational sampling: 1.2 Å

Number of poses generated: 2,000

Scoring function: ZRank

Top clusters retained: 10

The best pose from the top-ranked cluster had a ZRank score of −1308.4 and an interface area of 1,584 Ų. This pose was selected for subsequent molecular dynamics simulations.

Previous studies have demonstrated that the MEDI8852 scFv binds to the stem region of the H1N1 HA protein. Consistent with these findings, we examined the docking interface in PyMOL and confirmed that the predicted binding site was located in the HA stem. Among the generated docking poses, the Top 1 predicted complex was selected for further quality assessment.

PyMOL visualization confirmed that the MEDI8852 scFv binds to the stem region of HA (cyan), consistent with previous studies. The left panel shows the overall docking orientation, while the right panel highlights the interaction interface. Several hydrogen bonds and hydrophobic contacts were observed, with residues such as TYR659 and ASN628 involved in stabilizing the binding. This stem-targeting binding mode is significant because it prevents the conformational rearrangements of HA necessary for membrane fusion, thereby neutralizing the virus.

Eigenvalue (Principal Mode): 3.38×10⁻⁵

Interpretation: The low eigenvalue indicates the complex has sufficient flexibility for binding-induced conformational changes.

The iMODS analysis confirms that the scFv–HA complex is flexible enough to adapt to binding-induced conformational changes while maintaining overall stability. This validated structure serves as a reliable input for further molecular dynamics simulations and epidemiological modeling.

Binding Affinity Prediction

To further evaluate the interaction strength of the docked complex, we used PRODIGY after removing small molecules and retaining only the two protein chains.These results indicate that the MEDI8852 scFv–HA stem complex exhibits a predicted binding free energy of –11.4 kcal/mol and a nanomolar dissociation constant (Kd = 6.8 nM), suggesting a high-affinity interaction. The interface is stabilized by a combination of charged–hydrophobic and hydrophobic–hydrophobic contacts, which together account for over 60% of the binding interactions.

Antibody-Antigen Binding Model Construction

This simulation is based on the Langmuir binding model and aims to predict the relationship between antigen concentration (0.1 pM–10 nM) and binding ratio (θ) under varying antibody concentrations (50 nM–1 µM). The core equation used is:θ = [Ag] / (Kd / [Ab] + [Ag]).In the preliminary protein docking results, one scfv binds to one HA molecule at a ratio of 1:1.Clinically relevant ranges of antibody and antigen concentrations were defined, and the dissociation constant (Kd = 6.8 nM) was obtained through molecular docking analysis. MATLAB(R2024a) was used for computational simulation and visualization to generate binding curves at different antibody concentrations.Effective early detection of influenza A is often hampered by low viral antigen concentrations. Commercial ELISA kits (for example, one with a detection range of 101.563 pg/mL to 6500 pg/mL) may lack sufficient sensitivity for the very earliest stages of infection. Given that low antibody concentrations are typically encountered during early-stage detection using anti-influenza scFvs, we aimed to verify whether effective binding could be achieved under these conditions of low antigen concentration. The significance of this work is to aid the experimental team in pre-experimental screening and to validate that the designed scFv sequences can achieve the goal of early recognition.

When the binding ratio θ₀ = 0.5, the system reaches a half-saturated state, where the antigen concentration equals the dissociation constant of the antibody. This point reflects the binding affinity between the antibody and antigen and serves as one of the key indicators for evaluating antibody efficacy.Since clinical studies（reference：A phase 1 study in healthy volunteers to investigate the safety, tolerability, and pharmacokinetics of VIR-2482: a monoclonal antibody for the prevention of severe influenza A illness） have shown that the effective serum concentration of MEDI8852 maintained in vivo is approximately 50–300 nM (0.05–0.3 µM), setting the antibody concentration range at 0.05–1 µM not only covers the literature-supported protective threshold but also provides a reasonable extended range for simulation purposes.

Growth Curve Simulation

To accurately characterize the growth dynamics of bacteria, we fitted the experimental OD measurements over time to three classical growth models: Logistic, Gompertz, and Richards. By comparing the goodness-of-fit (R²) across different datasets, the Gompertz model was found to provide the best fit. Based on this model, we extended the growth curve prediction from the original 25 days to 35 days, successfully forecasting the future growth trend of the bacteria. This provides a quantitative basis for further experimental design and microbial population management.

Infectious Disease Dynamics SEIAR model

Fitting of Real-World Data for an Influenza A Outbreak

We employ the SEIAR model to simulate influenza transmission, which classifies the population into five compartments: S (Susceptible): Individuals at risk of infection E (Exposed): Infected individuals in the latent period I (Infectious): Symptomatic infected individuals A (Asymptomatic): Asymptomatic infected individuals R (Recovered): Recovered individuals

Fitting Equation of SEIAR

SEIAR Model Fitting Equations (System of Differential Equations): dS/dt = -β*(I + k*A)*S/N dE/dt = β*(I + k*A)*S/N - ω*E dI/dt = (1-p)*ω*E - γ*I dA/dt = p*ω*E - γ₁*A dR/dt = γ*I + γ₁*A

Fitted Parameters:

β (Symptomatic transmission rate): 1.1743 k (Relative transmission coefficient for asymptomatic cases): 0.1211 Fixed Parameters: ω=0.53, p=0.14, γ=0.23, γ₁=0.24

An iterative optimization strategy is adopted to estimate parameters β and k: 1. Objective Function: Minimize the mean squared error (MSE) between the modelpredicted (I+A) values and the actual data. 2. Optimization Algorithm: LBFGSB (a quasiNewton method with bound constraints). 3. Iterative Strategy: (1) Initial parameter ranges: β ∈ [0.2, 1.5], k ∈ [0.1, 0.8]. (2) Perform 3 iterations, narrowing the search range by 20% in each iteration. (3) Update the central parameter values after each iteration. 4. Final Selection: Choose the parameter combination with the smallest MSE from all iteration results.

1. Optimized Parameters (beta, k) beta (Symptomatic Transmission Rate): 1.1743

Rationale: Within the reference range of 0.5–1.5, consistent with the transmission characteristics of influenza. The model's MSE = 114534.46 indicates a good fit, supporting the validity of this value. k (Relative Transmission Coefficient for Asymptomatic Cases): 0.1211

Rationale: Within the reference range of 0.1–0.8. The value aligns with the principle that asymptomatic transmission is weaker than symptomatic transmission. When combined with beta, it accurately reflects realworld data trends, demonstrating logical consistency.

2. Fixed Parameters omega (Inverse of Incubation Period): 0.53

Rationale:Corresponds to an incubation period of 1/omega ≈ 1.9 days, within the typical influenza incubation range (1–3 days). p (Proportion of Asymptomatic Cases): 0.14

Rationale:14.000000000000002% falls within the documented range for influenza asymptomatic infections (10%–40%), matching epidemiological findings. gamma (Symptomatic Recovery Rate): 0.23

Corresponds to a disease duration of 1/gamma ≈ 4.3 days, consistent with the 3–7day recovery period for symptomatic influenza cases. gamma1 (Asymptomatic Recovery Rate): 0.24