Biological hydrogels such as mucus, extracellular matrix, biofilms, and the nuclear pore have diverse functions and compositions, but all act as selectively permeable barriers to the diffusion of particles. Each barrier has a crosslinked polymeric mesh that blocks penetration of large particles such as pathogens, nanotherapeutics, or macromolecules. These polymeric meshes also employ interactive filtering, in which affinity between solutes and the gel matrix controls permeability. Interactive filtering affects the transport of particles of all sizes including peptides, antibiotics, and nanoparticles and in many cases this filtering can be described in terms of the effects of charge and hydrophobicity. The concepts described in this review can guide strategies to exploit or overcome gel barriers, particularly for applications in diagnostics, pharmacology, biomaterials, and drug delivery.

Mucus is a hydrogel that exhibits complex selective permeability, permitting the passage of some particles while restricting the passage of other particles including important therapeutics. In this review, we discuss biochemical mechanisms underlying mucus penetration and mucus binding, emphasizing the importance of steric, electrostatic, and hydrophobic interactions. We discuss emerging techniques for engineering nanoparticle surface chemistries for mucus penetration as well as recent advances in tuning mucus interactions with small molecule, peptide, or protein therapeutics. Finally, we highlight recent work suggesting that mucus permeability can serve as a biomarker for disease and physiological states such as pregnancy.

7Antimicrobial peptides (AMPs) are naturally occurring or synthetic peptides that show promise for treating 8 antibiotic-resistant pathogens. Machine learning techniques are increasingly used to identify naturally occurring 9AMPs, but there is a dearth of purely computational methods to design novel effective AMPs, which would speed 10 AMP development. We collected a large database, Giant Repository of AMP Activities (GRAMPA), containing 11 AMP sequences and associated MICs. We designed a convolutional neural network to perform combined 12 classification and regression on peptide sequences to quantitatively predict AMP activity against Escherichia coli. 13Our predictions outperformed the state of the art at AMP classification and were also effective at regression, for 14 which there were no publicly available comparisons. We then used our model to design novel AMPs and 15 experimentally demonstrated activity of these AMPs against the pathogens E. coli, Pseudomonas aeruginosa, and 16Staphylococcus aureus. Data, code, and neural network architecture and parameters are available at

Preterm birth is the leading cause of neonatal mortality, and is frequently associated with intra-amniotic infection hypothesized to arise from bacterial ascension across a dysfunctional cervical mucus plug. To study this dysfunction, we assessed the permeability of cervical mucus from non-pregnant ovulating (n = 20) and high- (n = 9) and low-risk (n = 16) pregnant women to probes of varying sizes and surface chemistries. We found that the motion of negatively charged, carboxylated microspheres in mucus from pregnant patients was significantly restricted compared to ovulating patients, but not significantly different between high- and low-risk pregnant women. In contrast, charged peptide probes small enough to avoid steric interactions, but sensitive to the biochemical modifications of mucus components exhibited significantly different transport profiles through mucus from high- and low-risk patients. Thus, although both microstructural rearrangements of the components of mucus as well as biochemical modifications to their adhesiveness may alter the overall permeability of the cervical mucus plug, our findings suggest that the latter mechanism plays a dominant role in the impairment of the function of this barrier during preterm birth. We expect that these probes may be readily adapted to study the mechanisms underlying disease progression on all mucosal epithelia, including those in the mouth, lungs, and gut.

Multivalent binding interactions are commonly found throughout biology to enhance weak monovalent binding such as between glycoligands and protein receptors. Designing multivalent polymers to bind to viruses and toxic proteins is a promising avenue for inhibiting their attachment and subsequent infection of cells. Several studies have focused on oligomeric multivalent inhibitors and how changing parameters such as ligand shape, size, linker length, and flexibility affect binding. However, experimental studies of how larger structural parameters of multivalent polymers, such as degree of polymerization, affect binding avidity to targets have mixed results, with some finding an improvement with longer polymers and some finding no effect. Here, we use Brownian dynamics simulations to provide a theoretical understanding of how the degree of polymerization affects the binding avidity of multivalent polymers. We show that longer polymers increase binding avidity to multivalent targets but reach a limit in binding avidity at high degrees of polymerization. We also show that when interacting with multiple targets simultaneously, longer polymers are able to use intertarget interactions to promote clustering and improve binding efficiency. We expect our results to narrow the design space for optimizing the structure and effectiveness of multivalent inhibitors as well as be useful to understand biological design strategies for multivalent binding.