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.
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Selected Publications
Background
Recent personalized nutrition research has reported large inter-individual differences in postprandial glucose responses to identical foods, raising questions about whether these differences reflect food-specific personal effects or normal day-to-day variability in glucose tolerance.
Objectives
To quantify the relative contributions of measurement variability vs person-specific effects to inter-individual glycemic variation, and to define substitution thresholds for when glycemic index (GI) differences produce distinct physiological effects.
Methods
In this secondary analysis with simulated validation, data from 382 healthy adults (1,022 glucose reference tests, 1,116 food tests across 9 carbohydrate-rich foods) were analyzed using a direct comparison scaling model, in which an individual's food response equals their glucose reference response scaled by the food's average GI. Sensitivity analyses included single-reference predictions, restriction to participants with ≥3 reference tests, and exclusion of a protocol-deviating food.
Results
Predicted errors did not exceed the observed glucose reference test-retest variability (mean root mean square deviation [RMSD]: 0.78 vs. 1.02 mmol/L; Cohen's d = 0.54 [0.45, 0.63]), with ∼90% of predictions falling within each participant's own test-retest range. Bland-Altman analysis confirmed negligible systematic bias (-0.01 mmol/L). Synthetic datasets generated from glucose variability and average GI values reproduced observed response distributions without person-specific parameters. GI differences of ≥15 units produced reliably distinguishable responses in a given individual. All sensitivity analyses yielded equal or stronger effect sizes.
Conclusions
In healthy adults under standardized conditions, inter-individual variation in glycemic responses is predominantly accounted for by variability in day-to-day glucose tolerance, propagating through the GI ratio. The GI concept performs within the reproducibility limits of input data.
Machine learning is revolutionizing protein design by enabling the rapid generation of sequences with precise structural and functional properties. Controlling protein conformational states remains a major challenge, particularly for enzymes regulated by complex structural switches. Here, using high-resolution structural data and probabilistic sequence-structure models, a machine learning-driven framework for conformationally biased protein design is presented titled Conformation-Specific Design or CSDesign. This approach generates sequences predicted to favor a desired conformation while disfavoring alternative states. As a proof-of-concept, this approach is applied to extracellular signal-regulated kinase 2 (ERK2), generating variants predicted to favor the active or inactive state. Experimental validation of relative kinase activity in a controlled assay confirmed that an active-biased variant, CSD104, exhibits robust kinase activity without native upstream phosphorylation, while an inactive-biased variant, CSD101, remains inactivated. Structural analysis suggests that engineered interactions stabilize active-like features in place of phosphorylation. These results demonstrate machine learning control of protein conformational ensembles, with potential to design enzymes and other conformationally regulated proteins without relying on phosphomimetic mutations or extensive experimental screening.
Background and Aim
Allulose and tagatose are rare monosaccharides gaining attention as potential alternatives to added sugars. This systematic review and meta-analysis aimed to evaluate the effects of allulose and tagatose supplementation on glycemic, cardiometabolic, and anthropometric outcomes in adults.
Methods
The MEDLINE, EMBASE, and Cochrane libraries were searched through April 30, 2025, for controlled human intervention trials reporting the effects of tagatose or allulose on postprandial and fasting glucose and insulin levels, blood lipids, uric acid, and anthropometric measures of adiposity in adults. Meta-analyses were conducted using mean differences with random-effects models applied to all analyses. The risk of bias was evaluated using the Cochrane Risk of Bias 2 and ROBINS-I tools, and the certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.
Results
Of 4,905 initial reports, 20 trials were identified (12 allulose trials and 8 tagatose trials; 1,033 participants). Allulose significantly reduced postprandial glucose (iAUC: SMD = -0.66; 95% CI -0.92, -0.39; moderate certainty) and insulin (SMD = -1.27 (-2.14, -0.40); I2 = 96%; p = 0.03; moderate certainty), with no significant effects on HbA1c, fasting glucose or insulin, lipids, uric acid, or body composition (very low to moderate certainty). Tagatose intake similarly lowered postprandial glucose (SMD = –1.03 (-1.36, -0.71)) and insulin (SMD = -1.05 (-1.61, -0.49); both moderate certainty) and also reduced HbA1c (MD = -0.25 (-0.44, -0.06); moderate certainty) and fasting insulin (MD = -80.40 (-136.96, -23.84); very low certainty). No other significant pooled effects were observed.
Conclusion
Supplementation of allulose or tagatose attenuates postprandial glycemic and insulin responses, albeit with no improvements in blood lipids and body composition.