Closing the Gap between Experiment and Simulation─A Holistic Study on the Complexation of Small Interfering RNAs with Polyethylenimine.

Rational design is pivotal in the modern development of nucleic acid nanocarrier systems. With the rising prominence of polymeric materials as alternatives to lipid-based carriers, understanding their structure-function relationships becomes paramount. Here, we introduce a newly developed coarse-grained model of polyethylenimine (PEI) based on the Martini 3 force field. This model facilitates molecular dynamics simulations of true-sized PEI molecules, exemplified by molecules with molecular weights of 1.3, 5, 10, and 25 kDa, with degrees of branching between 50.0 and 61.5%. We employed this model to investigate the thermodynamics of small interfering RNA (siRNA) complexation with PEI. Our simulations underscore the pivotal role of electrostatic interactions in the complexation process. Thermodynamic analyses revealed a stronger binding affinity with increased protonation, notably in acidic (endosomal) pH, compared to neutral conditions. Furthermore, the molecular weight of PEI was found to be a critical determinant of binding dynamics: smaller PEI molecules closely enveloped the siRNA, whereas larger ones extended outward, facilitating the formation of complexes with multiple RNA molecules. Experimental validations, encompassing isothermal titration calorimetry and single-molecule fluorescence spectroscopy, aligned well with our computational predictions. Our findings not only validate the fidelity of our PEI model but also accentuate the importance of in silico data in the rational design of polymeric drug carriers. The synergy between computational predictions and experimental validations, as showcased here, signals a refined and precise approach to drug carrier design.

Generation of mAb Variants with Less Attractive Self-Interaction but Preserved Target Binding by Well-Directed Mutation.

Strongly attractive self-interaction of therapeutic protein candidates can impose challenges for manufacturing, filling, stability, and administration due to elevated viscosity or aggregation propensity. Suitable formulations can mitigate these issues to a certain extent. Understanding the self-interaction mechanism on a molecular basis and rational protein engineering provides a more fundamental approach, and it can save costs and efforts as well as alleviate risks at later stages of development. In this study, we used computational methods for the identification of aggregation-prone regions in a mAb and generated mutants based on these findings. We applied hydrogen-deuterium exchange mass spectrometry to identify distinct self-interaction hot spots. Ultimately, we generated mAb variants based on a combination of both approaches and identified mutants with low attractive self-interaction propensity, minimal off-target binding, and even improved target binding. Our data show that the introduction of arginine in spatial proximity to hydrophobic patches is highly beneficial on all these levels. For our mAb, variants that contain more than one aspartate residue flanking to the hydrophobic HCDR3 show decreased attractive self-interaction at unaffected off-target and target binding. The combined engineering strategy described here underlines the high potential of understanding self-interaction in the early stages of development to predict and reduce the risk of failure in subsequent development.