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.

Efficacy of high dose tranexamic acid (TXA) for hemorrhage: A systematic review and meta-analysis.

BACKGROUND: Standard dose (≤ 1 g) tranexamic acid (TXA) has established mortality benefit in trauma patients. The role of high dose IV TXA (≥2 g or ≥30 mg/kg as a single bolus) has been evaluated in the surgical setting, however, it has not been studied in trauma. We reviewed the available evidence of high dose IV TXA in any setting with the goal of informing its use in the adult trauma population. METHODS: We searched MEDLINE, EMBASE and unpublished sources from inception until July 27, 2022 for studies that compared standard dose with high dose IV TXA in adults (≥ 16 years of age) with hemorrhage. Screening and data abstraction was done independently and in duplicate. We pooled trial data using a random effects model and considered randomized controlled trials (RCTs) and observational cohort studies separately. We assessed the individual study risk of bias using the Cochrane Risk of Bias for RCTs and the Newcastle-Ottawa Scale for observational cohort studies. The overall certainty of evidence was assessed using the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation). RESULTS: We included 20 studies with a combined total of 12,523 patients. Based on pooled RCT data, and as compared to standard dose TXA, high dose IV TXA probably decreases transfusion requirements (odds ratio [OR] 0.86, 95% confidence interval [CI] 0.76 to 0.97, moderate certainty) but with possibly no effect on blood loss (mean difference [MD] 43.31 ml less, 95% CI 135.53 to 48.90 ml less, low certainty), and an uncertain effect on thromboembolic events (OR 1.33, 95% CI 0.86 to 2.04, very low certainty) and mortality (OR 0.70, 95% CI 0.37 to 1.32, very low certainty). CONCLUSION: When compared to standard dose, high dose IV TXA probably reduces transfusion requirements with an uncertain effect on thromboembolic events and mortality. LEVEL OF EVIDENCE: Systematic review and meta-analysis, level IV.

Feedback-controlled dynamics of neuronal cells on directional surfaces.

The formation of neuronal networks is a complex phenomenon of fundamental importance for understanding the development of the nervous system. The basic process underlying the network formation is axonal growth, a process involving the extension of axons from the cell body and axonal navigation toward target neurons. Axonal growth is guided by the interactions between the tip of the axon (growth cone) and its extracellular environmental cues, which include intercellular interactions, the biochemical landscape around the neuron, and the mechanical and geometrical features of the growth substrate. Here, we present a comprehensive experimental and theoretical analysis of axonal growth for neurons cultured on micropatterned polydimethylsiloxane (PDMS) surfaces. We demonstrate that closed-loop feedback is an essential component of axonal dynamics on these surfaces: the growth cone continuously measures environmental cues and adjusts its motion in response to external geometrical features. We show that this model captures all the characteristics of axonal dynamics on PDMS surfaces for both untreated and chemically modified neurons. We combine experimental data with theoretical analysis to measure key parameters that describe axonal dynamics: diffusion (cell motility) coefficients, speed and angular distributions, and cell-substrate interactions. The experiments performed on neurons treated with Taxol (inhibitor of microtubule dynamics) and Y-27632 (disruptor of actin filaments) indicate that the internal dynamics of microtubules and actin filaments plays a critical role for the proper function of the feedback mechanism. Our results demonstrate that axons follow geometrical patterns through a contact-guidance mechanism, in which high-curvature geometrical features impart high traction forces to the growth cone. These results have important implications for our fundamental understanding of axonal growth as well as for bioengineering novel substrate to guide neuronal growth and promote nerve repair.

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.

Axonal growth on surfaces with periodic geometrical patterns.

The formation of neuron networks is a complex phenomenon of fundamental importance for understanding the development of the nervous system, and for creating novel bioinspired materials for tissue engineering and neuronal repair. The basic process underlying the network formation is axonal growth, a process involving the extension of axons from the cell body towards target neurons. Axonal growth is guided by environmental stimuli that include intercellular interactions, biochemical cues, and the mechanical and geometrical features of the growth substrate. The dynamics of the growing axon and its biomechanical interactions with the growing substrate remains poorly understood. In this paper, we develop a model of axonal motility which incorporates mechanical interactions between the axon and the growth substrate. We combine experimental data with theoretical analysis to measure the parameters that describe axonal growth on surfaces with micropatterned periodic geometrical features: diffusion (cell motility) coefficients, speed and angular distributions, and axon bending rigidities. Experiments performed on neurons treated Taxol (inhibitor of microtubule dynamics) and Blebbistatin (disruptor of actin filaments) show that the dynamics of the cytoskeleton plays a critical role in the axon steering mechanism. Our results demonstrate that axons follow geometrical patterns through a contact-guidance mechanism, in which high-curvature geometrical features impart high traction forces to the growth cone. These results have important implications for our fundamental understanding of axonal growth as well as for bioengineering novel substrates that promote neuronal growth and nerve repair.

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.

The evolution of PAs in the Canadian Armed Forces.

This study documents the growing role of the physician assistant (PA) in the Canadian Armed Forces. PAs have served as the backbone of the Royal Canadian Medical Services' frontline medical operations since 1984, on land, aboard ships and submarines, and domestically in garrison. Candidates begin as medical technicians and receive advanced training to become PAs at midcareer. The current rank of PAs as warrant officers is evolving and a commissioned status is under consideration.