pyPolyBuilder: Automated Preparation of Molecular Topologies and Initial Configurations for Molecular Dynamics Simulations of Arbitrary Supramolecules

High Charge Density in Peptide Dendrimers is Required to Destabilize Membranes: Insights into Endosome Evasion

Peptide dendrimers are a type of branched, symmetric, and topologically well-defined molecule that have already been used as delivery systems for nucleic acid transfection. Several of the most promising sequences showed high efficiency in many key steps of transfection, namely, binding siRNA, entering cells, and evading the endosome. However, small changes to the peptide dendrimers, such as in the hydrophobic core, the amino acid chirality, or the total available charges, led to significantly different experimental results with unclear mechanistic insights. In this work, we built a computational model of several of those peptide dendrimers (MH18, MH13, and MH47) and some of their variants to study the molecular details of the structure and function of these molecules. We performed CpHMD simulations in the aqueous phase and in interaction with a lipid bilayer to assess how conformation and protonation are affected by pH in different environments. We found that while the different peptide dendrimer sequences lead to no substantial structural differences in the aqueous phase, the total charge and, more importantly, the total charge density are key for the capacity of the dendrimer to interact and destabilize the membrane. These dendrimers become highly charged when the pH changes from 7.5 to 4.5, and the presence of a high charge density, which is decreased for MH47 that has four fewer titratable lysines, is essential to trigger membrane destabilization. These findings are in excellent agreement with the experimental data and help us to understand the high efficiency of some dendrimers and why the dendrimer MH47 is unable to complete the transfection process. This evidence provides further understanding of the mode of action of these peptide dendrimers and will be pivotal for the future design of new sequences with improved transfection capabilities.

Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities

The covalent modification of proteins with polymers is a well-established method for improving the pharmacokinetic properties of therapeutically valuable biologics. The conjugated polymer chains of the resulting hybrid represent highly flexible macromolecular structures. As the dynamics of such systems remain rather elusive for established experimental techniques from the field of protein structure elucidation, molecular dynamics simulations have proven as a valuable tool for studying such conjugates at an atomistic level, thereby complementing experimental studies. With a focus on new developments, this review aims to provide researchers from the polymer bioconjugation field with a concise and up to date overview of such approaches. After introducing basic principles of molecular dynamics simulations, as well as methods for and potential pitfalls in modeling bioconjugates, the review illustrates how these computational techniques have contributed to the understanding of bioconjugates and bioconjugation strategies in the recent past and how they may lead to a more rational design of novel bioconjugates in the future.

From Linear to Nets: Multiconfiguration Polymer Structure Generation with PolyFlin

Molecular modeling and simulations are essential tools in polymer science and engineering, enabling researchers to predict and understand the properties of macromolecules, including their structure, dynamics, thermodynamics, and overall material characteristics. However, one of the key challenges in polymer simulation and modeling lies in the initial topology design, as existing programs often lack the capability to generate all types of polymer forms. In this study, we present PolyFlin, a powerful Python module that addresses this limitation by allowing the generation of a wide range of polymer structures, from simple homopolymers to complex copolymers, including grafts, cyclic, star, dendrimers, and nets. PolyFlin offers a versatile and efficient tool for exploring and creating diverse polymer architectures, facilitating advancements in various fields that require precise polymer modeling and simulation.

Automatic Multiscale Method of Building up a Cross-linked Polymer Reaction System: Bridging SMILES to the Multiscale Molecular Dynamics Simulation

An automatic method is introduced to generate the initial configuration and input file from SMILES for multiscale molecular dynamics (MD) simulation of cross-linked polymer reaction systems. Inputs are a modified version of SMILES of all the components and conditions of coarse-grained (CG) and all-atom (AA) simulations. The overall process comprises the following steps: (1) Modified SMILES inputs of all the components are converted to 3-dimensional coordinates of molecular structures. (2) Molecular structures are mapped to the coarse-grained scale, followed by a CG reaction simulation. (3) CG beads are backmapped to the atomic scale after the CG reaction. (4) An AA productive run is finally performed to analyze volume shrinkage, glass transition, and atomic detail of network structure. The method is applied to two common epoxy resin reactions, that is, the cross-linking process of DGEVA (diglycidyl ether of vanillyl alcohol) and DHAVA (dihydroxyaminopropane of vanillyl alcohol) and that of DGEBA (diglycidyl ether of bisphenol A) and DETA (diethylenetriamine). These components form network structures after the CG cross-linking reaction and are then backmapped to calculate properties in the atomic scale. The result demonstrates that the method can accurately predict volume shrinkage, glass transition, and all-atom structure of cross-linked polymers. The method bridges from SMILES to MD simulation trajectories in an automatic way, which shortens the time of building up cross-linked polymer reaction model and suitable for high-throughput computations.

Unveiling the G4-PAMAM capacity to bind and protect Ang-(1-7) bioactive peptide by molecular dynamics simulations

Angiotensin-(1-7) re-balance the Renin-Angiotensin system affected during several pathologies, including the new COVID-19; cardiovascular diseases; and cancer. However, one of the limiting factors for its therapeutic use is its short half-life, which might be overcome with the use of dendrimers as nanoprotectors. In this work, we addressed the following issues: (1) the capacity of our computational protocol to reproduce the experimental structural features of the (hydroxyl/amino)-terminated PAMAM dendrimers as well as the Angiotensin-(1-7) peptide; (2) the coupling of Angiotensin-(1-7) to (hydroxyl/amino)-terminated PAMAM dendrimers in order to gain insight into the structural basis of its molecular binding; (3) the capacity of the dendrimers to protect Angiotensin-(1-7); and (4) the effect of pH changes on the peptide binding and covering. Our Molecular-Dynamics/Metadynamics-based computational protocol well modeled the structural experimental features reported in the literature and our double-docking approach was able to provide reasonable initial structures for stable complexes. At neutral pH, PAMAM dendrimers with both terminal types were able to interact stably with 3 Angiotensin-(1-7) peptides through ASP1, TYR4 and PRO7 key amino acids. In general, they bind on the surface in the case of the hydroxyl-terminated compact dendrimer and in the internal zone in the case of the amino-terminated open dendrimer. At acidic pH, PAMAM dendrimers with both terminal groups are still able to interact with peptides either internalized or in its periphery, however, the number of contacts, the percentage of coverage and the number of hydrogen bonds are lesser than at neutral pH, suggesting a state for peptide release. In summary, amino-terminated PAMAM dendrimer showed slightly better features to bind, load and protect Angiotensin-(1-7) peptides.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle.