Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles.

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R2 = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R2 = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R2 = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R2 = 0.97 and R2 = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

Structure prediction of cyclic peptides by molecular dynamics + machine learning.

Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.

Sugar Functionalization of Silks with Pathway-Controlled Substitution and Properties.

Silk biomaterials are important for applications in biomedical fields due to their outstanding mechanical properties, biocompatibility, and tunable biodegradation. Chemical functionalization of silk by various chemistries can be leveraged to enhance and tune these features and enable the expansion of silk-based biomaterials into additional fields. Sugars are particularly relevant for intracellular communication, signal transduction events, as well as in hydrated extracellular matrices such as in cartilage, vitreous, and brain tissues. Multiple reaction pathways are demonstrated (carboxylation of serines followed by carbodiimide coupling with glucosamine, carboxylation of tyrosines followed by carbodiimide coupling with glucosamine; direct carbodiimide coupling of the inherent carboxylic acids of silk (aspartic and glutamic acid) with glucosamine) for the covalent conjugation of glucosamine onto silk with characterization by proton nuclear magnetic resonance (1 H-NMR), liquid chromatography tandem mass spectroscopy (LC-MS), water contact angle (WCA), and Fourier transform infrared (FTIR) spectroscopy. The results indicate that different pathways substitute different amounts of glucosamine onto silk chains, with control over resulting material properties, including hydrophobicity/hydrophilicity and biological responses. The aqueous processability of these conjugates into functional material formats (films, sponges) is assessed. These new classes of bio-inspired materials can lead to multifunctional biomaterials for potential applications in different fields of biomedical engineering.

Silk degumming time controls horseradish peroxidase-catalyzed hydrogel properties.

Hydrogels provide promising applications in tissue engineering and regenerative medicine, with silk fibroin (SF) offering biocompatibility, biodegradability and tunable mechanical properties. The molecular weight (MW) distribution of SF chains varies from ∼80 to 400 kDa depending on the extraction and purification process utilized to prepare the protein polymer. Here, we report a fundamental study on the effect of different silk degumming (extraction) time (DT) on biomaterial properties of enzymatically crosslinked hydrogels, including secondary structure, mechanical stiffness, in vitro degradation, swelling/contraction, optical transparency and cell behaviour. The results indicate that DT plays a crucial role in determining material properties of the hydrogel; decrease in DT increases ß-sheet (crystal) formation and mechanical stiffness while decreasing degradation rate and optical transparency. The findings on the relationships between properties of silk hydrogels and DT should facilitate the more rational design of silk-based hydrogel biomaterials to match properties needed for diverse purpose in biomedical engineering.