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.

Molecular Insights into the Improved Bioactivity of Interferon Conjugates Attached to a Helical Polyglutamate

Attaching polymers, especially polyethylene glycol (PEG), to protein drugs has emerged as a successful strategy to prolong circulation time in the bloodstream. The hypothesis is that the flexible chain wobbles on the protein's surface, thus resisting potential nonspecific adsorption. Such a theoretical framework may be challenged when a helical polyglutamate is used to conjugate with target proteins. In this study, we investigated the structure-activity relationships of polyglutamate-interferon conjugates P(EG₃Glu)-IFN using molecular simulations. Our results show that the local crowding effect induced by oligoethylene glycols (i.e., EG₃) is the primary driving force for helix formation in P(EG₃Glu), and its helicity can be effectively increased by reducing the free volume of the two termini. Furthermore, it was found that the steric hindrance induced by IFN is not conductive to the helicity of P(EG₃Glu) but contributes to its dominant orientation relative to interferon. The orientation of IFN relative to the helical P(EG₃Glu) can help to protect the protein drug from neutralizing antibodies while maintaining its bioactivity. These findings suggest that the helical structure and its orientation are critical factors to consider when updating the theoretical framework for protein-polymer conjugates.

Advances of Antimicrobial Peptide-Based Biomaterials for the Treatment of Bacterial Infections

Owing to the increase in multidrug-resistant bacterial isolates in hospitals globally and the lack of truly effective antimicrobial agents, antibiotic resistant bacterial infections have increased substantially. There is thus an urgent need to develop new antimicrobial drugs and their related formulations. In recent years, natural antimicrobial peptides (AMPs), AMP optimization, self-assembled AMPs, AMP hydrogels, and biomaterial-assisted delivery of AMPs have shown great potential in the treatment of bacterial infections. In this review, it is focused on the development prospects and shortcomings of various AMP-based biomaterials for treating animal model infections, such as abdominal, skin, and eye infections. It is hoped that this review will inspire further innovations in the design of AMP-based biomaterials for the treatment of bacterial infections and accelerate their commercialization.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Molecular simulations explain the exceptional thermal stability, solvent tolerance and solubility of protein-polymer surfactant bioconjugates in ionic liquids

Proteins complexed electrostatically with polymer surfactants constitute a viscous liquid by themselves, called the solvent-free protein liquid (SFPL). A solution of SFPL in a room temperature ionic liquid (PS-IL) offers the protein hyperthermal stability, higher solubility and greater IL tolerance. A generic understanding of these protein-polymer systems is obtained herein through extensive atomistic molecular dynamics simulations of three different enzymes (lipase A, lysozyme and myoglobin) under various conditions. Along with increased intra-protein hydrogen bonding, the surfactant coating around the proteins imparts greater thermal stability, and also aids in screening protein-IL interactions, endowing them IL tolerance. The reduced surface polarity of the protein-polymer bioconjugate and hydrogen bonding between the ethylene glycol groups of the surfactant and the IL cation contribute to the facile solvation of the protein in its PS-IL form. The results presented here rationalize several experimental observations and will aid in the improved design of such hybrid materials for sustainable catalysis.

Design, Synthesis and Characterization of [G10a]-Temporin SHa Dendrimers as Dual Inhibitors of Cancer and Pathogenic Microbes

As the technologies for peptide synthesis and development continue to mature, antimicrobial peptides (AMPs) are being widely studied as significant contributors in medicinal chemistry research. Furthermore, the advancement in the synthesis of dendrimers’ design makes dendrimers wonderful nanostructures with distinguishing properties. This study foregrounds a temporin SHa analog, [G10a]-SHa, and its dendrimers as globular macromolecules possessing anticancer and antibacterial activities. These architectures of temporin SHa, named as [G10a]-SHa, its dendrimeric analogs [G10a]₂-SHa and [G10a]₃-SHa, and [G10a]₂-SHa conjugated with a polymer molecule, i.e., Jeff-[G10a]₂-SHa, were synthesized, purified on RP-HPLC and UPLC and fully characterized by mass, NMR spectroscopic techniques, circular dichroism, ultraviolet, infrared, dynamic light scattering, and atomic force microscopic studies. In pH- and temperature-dependent studies, all of the peptide dendrimers were found to be stable in the temperature range up to 40–60 °C and pH values in the range of 6–12. Biological-activity studies showed these peptide dendrimers possessed improved antibacterial activity against different strains of both Gram-positive and Gram-negative strains. Together, these dendrimers also possessed potent selective antiproliferative activity against human cancer cells originating from different organs (breast, lung, prostate, pancreas, and liver). The high hemolytic activity of [G10a]₂-SHa and [G10a]₃-SHa dendrimers, however, limits their use for topical treatment, such as in the case of skin infection. On the contrary, the antibacterial and anticancer activities of Jeff-[G10a]₂-SHa, associated with its low hemolytic action, make it potentially suitable for systemic treatment.

Antimicrobial coating strategy to prevent orthopaedic device-related infections: recent advances and future perspectives

The rapid development of multidrug-resistant (MDR) bacteria and biofilm-related infections (BRIs) has urgently called for new strategies to combat severe orthopaedic device-related infections (ODRIs). Antimicrobial coating has emerged as a promising strategy in halting the incidence of ODRIs and treating ODRIs in long term. With the advancement of material science and biotechnology, numerous antimicrobial coatings have been reported in literature, showing superior antimicrobial and osteogenic functions. This review has specifically discussed the currently developed antimicrobial coatings in the perspective of drug release from the coating system, focusing on their realization of controlled and on demand antimicrobial agents release, as well as multi-functionality. Acknowledging the multidisciplinary nature of antimicrobial coating, the conceptual design, the deposition method and the therapeutic effect of the antimicrobial coatings have been described in detail and discussed critically. Particularly, the challenges and opportunities on the way toward the clinical translation of antimicrobial coatings have been highlighted.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.

Data-Driven Methods for Accelerating Polymer Design

Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.

Recent advances in peptide-based nanomaterials for targeting hypoxia

Hypoxia is a prominent feature of many severe diseases such as malignant tumors, ischemic strokes, and rheumatoid arthritis. The lack of oxygen has a paramount impact on angiogenesis, invasion, metastasis, and chemotherapy resistance. The potential of hypoxia as a therapeutic target has been increasingly recognized over the last decade. In order to treat these disease states, peptides have been extensively investigated due to their advantages in safety, target specificity, and tumor penetrability. Peptides can overcome difficulties such as low drug/energy delivery efficiency, hypoxia-induced drug resistance, and tumor nonspecificity. There are three main strategies for targeting hypoxia through peptide-based nanomaterials: (i) using peptide ligands to target cellular environments unique to hypoxic conditions, such as cell surface receptors that are upregulated in cells under hypoxic conditions, (ii) utilizing peptide linkers sensitive to the hypoxic microenvironment that can be cleaved to release therapeutic or diagnostic payloads, and (iii) a combination of the above where targeting peptides will localize the system to a hypoxic environment for it to be selectively cleaved to release its payload, forming a dual-targeting system. This review focuses on recent developments in the design and construction of novel peptide-based hypoxia-targeting nanomaterials, followed by their mechanisms and potential applications in diagnosis and treatment of hypoxic diseases. In addition, we address challenges and prospects of how peptide-based hypoxia-targeting nanomaterials can achieve a wider range of clinical applications.

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

Application of Thermoresponsive Intrinsically Disordered Protein Polymers in Nanostructured and Microstructured Materials

Modulation of inter- and intramolecular interactions between bioinspired designer molecules can be harnessed for developing functional structures that mimic the complex hierarchical organization of multicomponent assemblies observed in nature. Furthermore, such multistimuli-responsive molecules offer orthogonal tunability for generating versatile multifunctional platforms via independent biochemical and biophysical cues. In this review, the remarkable physicochemical and mechanical properties of genetically engineered protein polymers derived from intrinsically disordered proteins, specifically elastin and resilin, are discussed. This review highlights emerging technologies which use them as building blocks in the fabrication of highly programmable structured biomaterials for applications in delivery of biotherapeutic cargo and regenerative medicine.

Supramolecular peptide nano-assemblies for cancer diagnosis and therapy: from molecular design to material synthesis and function-specific applications

Peptide molecule has high bioactivity, good biocompatibility, and excellent biodegradability. In addition, it has adjustable amino acid structure and sequence, which can be flexible designed and tailored to form supramolecular nano-assemblies with specific biomimicking, recognition, and targeting properties via molecular self-assembly. These unique properties of peptide nano-assemblies made it possible for utilizing them for biomedical and tissue engineering applications. In this review, we summarize recent progress on the motif design, self-assembly synthesis, and functional tailoring of peptide nano-assemblies for both cancer diagnosis and therapy. For this aim, firstly we demonstrate the methodologies on the synthesis of various functional pure and hybrid peptide nano-assemblies, by which the structural and functional tailoring of peptide nano-assemblies are introduced and discussed in detail. Secondly, we present the applications of peptide nano-assemblies for cancer diagnosis applications, including optical and magnetic imaging as well as biosensing of cancer cells. Thirdly, the design of peptide nano-assemblies for enzyme-mediated killing, chemo-therapy, photothermal therapy, and multi-therapy of cancer cells are introduced. Finally, the challenges and perspectives in this promising topic are discussed. This work will be useful for readers to understand the methodologies on peptide design and functional tailoring for highly effective, specific, and targeted diagnosis and therapy of cancers, and at the same time it will promote the development of cancer diagnosis and therapy by linking those knowledges in biological science, nanotechnology, biomedicine, tissue engineering, and analytical science.

Antimicrobial peptides towards clinical application: Delivery and formulation

Antimicrobial peptides hold promise to supplement small molecules antibiotics and combat the multidrug resistant microbes. There are however technical hurdles towards the clinical applications, largely due to the inherent limitations of peptides including stability, cytotoxicity and bioavailability. Here we review recent studies concerning the delivery and formulation of antimicrobial peptides, by categorizing the different strategies as driven by physical interactions or chemical conjugation reactions, and carriers ranging from inorganic based ones (including gold, silver and silica based solid nanoparticles) to organic ones (including micelle, liposome and hydrogel) are covered. Besides, targeted delivery of antimicrobial peptides or using antimicrobial peptides as the targeting moiety, and responsive release of the peptides after delivery are also reviewed. Lastly, strategies towards the increase of oral bioavailability, from both physical or chemical methods, are highlighted. Altogether, this article provides a comprehensive review of the recent progress of the delivery and formulation of antimicrobial peptides towards clinical application.

Combining simulations and experiments for the molecular engineering of multifunctional collagen mimetic peptide-based materials

Assembling peptides allow the creation of structurally complex materials, where amino acid selection influences resulting properties. We present a synergistic approach of experiments and simulations for examining the influence of natural and non-natural amino acid substitutions via incorporation of charged residues and a reactive handle on the thermal stability and assembly of multifunctional collagen mimetic peptides (CMPs). Experimentally, we observed inclusion of charged residues significantly decreased the melting temperature of CMP triple helices with further destabilization upon inclusion of the reactive handle. Atomistic simulations of a single CMP triple helix in explicit water showed increased residue-level and helical structural fluctuations caused by the inclusion of the reactive handle; however, these atomistic simulations cannot be used to predict changes in CMP melting transition. Coarse-grained (CG) simulations of CMPs at experimentally relevant solution conditions, showed, qualitatively, the same trends as experiments in CMP melting transition temperature with CMP design. These simulations show that when charged residues are included electrostatic repulsions significantly destabilize the CMP triple helix and that an additional inclusion of a reactive handle does not significantly change the melting transition. Based on findings from both experiments and simulations, the sequence design was refined for increased CMP triple helix thermal stability, and the reactive handle was utilized for the incorporation of the assembled CMPs within covalently crosslinked hydrogels. Overall, a unique approach was established for predicting stability of CMP triple helices for various sequences prior to synthesis, providing molecular insights for sequence design towards the creation of bulk nanostructured soft biomaterials.

Development of Coarse-Grained Models for Poly(4-vinylphenol) and Poly(2-vinylpyridine): Polymer Chemistries with Hydrogen Bonding

In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries-poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.