The Consortium of Molecular Design at BYU provides cutting edge interdisciplinary research opportunities for students to push the envelope for protein engineering and drug discovery.
We use close collaboration between laboratories at BYU in Physics, Chemistry, Computer Science, LifeSciences, and Engineering to tackle these challenging topics from all angles.
We actively seek industrial collaboration and support for our efforts and are excited to explore mutually beneficial application of all state-of-the-art technologies to revolutionize molecular design.
News and Events
Selected Publications
Computational enzyme design remains a powerful yet imperfect tool for optimizing biocatalysts, especially when targeting non-natural substrates. Using design tools we investigated Pseudomonas aeruginosa LipA, a lipase with a flexible lid domain crucial for substrate binding and turnover, aiming to enhance its hydrolysis of the industrially relevant substrate Roche ester. We generated an initial set of single-point mutations based on structural proximity to the active site and evaluated their effects using a computational pipeline integrating molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ensemble-based energy scoring. While we identified several active variants, attempts to rank them by activity using structural features, such as hydrogen bond formation or residue flexibility, failed. Deep learning models, applied post hoc for structural analysis via AlphaFold3, produced nearly identical active site geometries across variants, irrespective of activity. Reaction pathway analysis revealed energy barriers varying by 5–15 kcal/mol depending on substrate conformation, with the nucleophile addition step consistently rate-limiting. However, these small energetic shifts, likely critical for incremental activity changes, were indistinguishable by current computational or deep learning methods. Our results highlight the limitations of existing approaches in resolving subtle functional differences and underscore the need for improved benchmarks, reactive force fields, and more sensitive ranking metrics. Advancing these areas will be essential for designing enzymes with gradual, evolution-like activity improvements and for bridging the gap between structural prediction and catalytic function.
Background
Response curves are widely used in biomedical literature to summarize time-dependent outcomes, yet raw data are not always available in published reports. Meta-analysts must frequently extract means and standard errors from figures and estimate outcome measures like the area under the curve (AUC) without access to participant-level data. No standardized method exists for calculating AUC or propagating error under these constraints.
Methods
We evaluate two methods for estimating AUC from figure-derived data: (1) a trapezoidal integration approach with extrema variance propagation, and (2) a Monte Carlo method that samples plausible response curves and integrates over their posterior distribution. We generated 3,920 synthetic datasets from seven functional response types commonly found in glycemic response and pharmacokinetic research, varying the number of timepoints (4–10) and participants (5–40). All response curves were normalized to a true AUC of 1.0.
Results
The standard method consistently underestimated the true AUC, especially in curves with skewed or long-tailed structures. Monte Carlo method produced near-unbiased estimates with tighter alignment to the known AUC across all settings. Increasing the number of datapoints and participants improved performance for both methods, but the Monte Carlo approach retained robustness even under sparse conditions.
Conclusion
This is the first large-scale benchmarking of AUC estimation accuracy from graphically extracted data. The Monte Carlo method outperforms standard approaches in both accuracy and uncertainty quantification. We recommend its adoption in meta-analytic contexts where only figure-derived data are available and advocate for improved data sharing practices in primary publications.
Background
The integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association (AIM), an extracurricular program designed to equip pre-medical students with foundational AI knowledge.
Methods
A quasi-experimental pretest-posttest control group design was employed, comparing knowledge acquisition between students participating in the AIM program (cohort group) and a control group of students not participating. The intervention spanned four weeks and included hands-on AI training, ethical considerations, data preprocessing, and model evaluation. Pretest and posttest assessments measured AI knowledge and pathology-related skills.
Results
Participants in the AIM program demonstrated significant improvements in both AI knowledge and pathology-related scores. The cohort group showed a large effect size across all measured domains, particularly in pathology, with Cohen’s d values ranging from 1.83 to 4.74. Statistical analysis confirmed robust, significant improvements in test scores (t-test and Mann-Whitney U test, p < 0.001). There was no significant correlation between previous AI experience or attitudes toward AI and overall score improvement.
Conclusions
The AIM program effectively improved pre-medical students’ understanding of AI and its application in medicine, particularly in pathology. This study highlights the potential of extracurricular programs to address the need for AI education in medical curricula, especially in the pre-medical phase, and suggests that such initiatives could serve as a model for other institutions seeking to integrate AI education into healthcare training.