New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input parameters. Extensive analysis of the PepVAE-generated sequences paired with antimicrobial activity prediction models supports this modular design framework as a promising system for development of novel AMPs, demonstrating controlled production of AMPs with experimental validation of predicted antimicrobial activity.

Single-domain antibodies (sdAbs) or nanobodies have received widespread attention due to their small size (~ 15 kDa) and diverse applications in bio-derived therapeutics. As many modern biotechnology breakthroughs are applied to antibody engineering and design, nanobody thermostability or melting temperature (T_m) is crucial for their successful utilization. In this study, we present TEMPRO which is a predictive modeling approach for estimating the T_m of nanobodies using computational methods. Our methodology integrates various nanobody biophysical features to include Evolutionary Scale Modeling (ESM) embeddings, NetSurfP3 structural predictions, pLDDT scores per sdAb region from AlphaFold2, and each sequence’s physicochemical characteristics. This approach is validated with our combined dataset containing 567 unique sequences with corresponding experimental T_m values from a manually curated internal data and a recently published nanobody database, NbThermo. Our results indicate the efficacy of protein embeddings in reliably predicting the T_m of sdAbs with mean absolute error (MAE) of 4.03 °C and root mean squared error (RMSE) of 5.66 °C, thus offering a valuable tool for the optimization of nanobodies for various biomedical and therapeutic applications. Moreover, we have validated the models’ performance using experimentally determined T_ms from nanobodies not found in NbThermo. This predictive model not only enhances nanobody thermostability prediction, but also provides a useful perspective of using embeddings as a tool for facilitating a broader applicability of downstream protein analyses.

Calcium (Ca²⁺) sparks are the elementary events of excitation–contraction coupling, yet they are not explicitly represented in human ventricular myocyte models. A stochastic ventricular cardiomyocyte human model that adapts to intracellular Ca²⁺ ([Ca²⁺]_i) dynamics, spark regulation, and frequency-dependent changes in the form of locally controlled Ca²⁺ release was developed. The 20,000 CRUs in this model are composed of 9 individual LCCs and 49 RyRs that function as couplons. The simulated action potential duration at 1 Hz steady-state pacing is ~0.280 s similar to human ventricular cell recordings. Rate-dependence experiments reveal that APD shortening mechanisms are largely contributed by the L-type calcium channel inactivation, RyR open fraction, and [Ca²⁺]_myo concentrations. The dynamic slow-rapid-slow pacing protocol shows that RyR open probability during high pacing frequency (2.5 Hz) switches to an adapted “nonconducting” form of Ca²⁺-dependent transition state. The predicted force was also observed to be increased in high pacing, but the SR Ca²⁺ fractional release was lower due to the smaller difference between diastolic and systolic [Ca²⁺]_SR. Restitution analysis through the S1S2 protocol and increased LCC Ca²⁺-dependent activation rate show that the duration of LCC opening helps modulate its effects on the APD restitution at different diastolic intervals. Ultimately, a longer duration of calcium sparks was observed in relation to the SR Ca²⁺ loading at high pacing rates. Overall, this study demonstrates the spontaneous Ca²⁺ release events and ion channel responses throughout various stimuli.

Previous studies have observed alterations in excitation–contraction (EC) coupling during end-stage heart failure that include action potential and calcium (Ca²⁺) transient prolongation and a reduction of the Ca²⁺ transient amplitude. Underlying these phenomena are the downregulation of potassium (K⁺) currents, downregulation of the sarcoplasmic reticulum Ca²⁺ ATPase (SERCA), increase Ca²⁺ sensitivity of the ryanodine receptor, and the upregulation of the sodium–calcium (Na⁼-Ca²⁺) exchanger. However, in human heart failure (HF), debate continues about the relative contributions of the changes in calcium handling vs. the changes in the membrane currents. To understand the consequences of the above changes, they are incorporated into a computational human ventricular myocyte HF model that can explore the contributions of the spontaneous Ca²⁺ release from the sarcoplasmic reticulum (SR). The reduction of transient outward K⁺ current (I_to) is the main membrane current contributor to the decrease in RyR2 open probability and L-type calcium channel (LCC) density which emphasizes its importance to phase 1 of the action potential (AP) shape and duration (APD). During current-clamp conditions, RyR2 hyperphosphorylation exhibits the least amount of Ca²⁺ release from the SR into the cytosol and SR Ca²⁺ fractional release during a dynamic slow–rapid–slow (0.5–2.5–0.5 Hz) pacing, but it displays the most abundant and more lasting Ca²⁺ sparks two-fold longer than a normal cell. On the other hand, under voltage-clamp conditions, HF by decreased SERCA and upregulated I_NCX show the least SR Ca²⁺ uptake and EC coupling gain, as compared to HF by hyperphosphorylated RyR2s. Overall, this study demonstrates that the (a) combined effect of SERCA and NCX, and the (b) RyR2 dysfunction, along with the downregulation of the cardiomyocyte’s potassium currents, could substantially contribute to Ca²⁺ mishandling at the spark level that leads to heart failure.

Background/Objectives: Venezuelan equine encephalitis virus (VEEV) represents a significant biothreat with no FDA-approved vaccine currently available, highlighting the need for alternative therapeutic strategies. Single-domain antibodies (sdAbs) present a potential alternative to conventional antibodies, due to their small size and ability to recognize cryptic epitopes. Methods: This research describes the development and preliminary evaluation of VEEV-binding sdAbs generated using a generative artificial intelligence (AI) platform. Using a dataset of known alphavirus-binding sdAbs, the AI model produced sequences with predicted affinity for the E2 glycoprotein of VEEV. These candidate sdAbs were expressed in a bacterial periplasmic system and purified for initial assessment. Results: Enzyme-linked immunosorbent assays (ELISAs) indicated binding activity of the sdAbs to VEEV antigens. In vitro neutralization tests suggested inhibition of VEEV infection in cultured cells for some of the candidates. Conclusions: This study demonstrates how generative AI can expedite antiviral therapeutic development and establishes a framework for quick responses to emerging viral threats when extensive example databases are unavailable. Additional refinement and validation of AI-generated sdAbs could establish effective VEEV therapeutics.