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 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.
Therapeutic proteins face a critical pharmacokinetic challenge: rapid clearance from circulation limits their clinical efficacy. Albumin-binding domains (ABDs) offer an elegant solution by enabling therapeutic proteins to “hitchhike” on serum albumin’s favorable 19-day half-life through FcRn-mediated recycling. Clinical validation through approved therapeutics like ozoralizumab demonstrates the success of this approach, with preclinical studies showing fusion to an ABD extended half-life to 18 days. This review provides an analysis of ABD-fusion protein design, integrating structural biology, computational prediction, and rational engineering principles. We catalog the major classes of albumin-binding modalities, including bacterial three-helix bundle domains, engineered peptides, antibody-derived binders, and alternative scaffolds, comparing their binding properties, size contributions, cross-species reactivity, and production cost. Critical examination of linker architectures reveals that flexible glycine-serine linkers (particularly the widely successful (GGGGS)3 motif) provide optimal balance between domain independence and molecular economy, though linker choice profoundly influences not only spatial separation but also binding affinity, folding, stability, and pharmacokinetics. We evaluate the utility and limitations of the structure prediction tools for ABD-fusion design. We establish practical guidelines for integrating computational screening with experimental validation. This review provides protein engineers and synthetic biologists with a comprehensive framework for rational design of albumin-binding therapeutics, emphasizing the synergistic integration of structural insight, computational prediction, and systematic experimental validation to accelerate development of next-generation long-acting biotherapeutics.
Engineered luciferases have transformed biological imaging and sensing, yet optimizing NanoLuc luciferase (NLuc) remains challenging due to the inherent stability-activity trade-off and its limited sequence homology with characterized proteins. We report a hybrid approach that synergistically integrates deep learning with structure-guided rational design to develop enhanced NLuc variants that improve thermostability and thereby activity at elevated temperatures. By systematically analyzing libraries of engineered variants, we established that modifications to termini and loops distal from the catalytic center, combined with preservation of allosterically coupled networks, effectively increase thermal resilience while maintaining enzymatic function. Our optimized variants─notably B.07 and B.09─exhibit substantial thermostability enhancements (increased melting temperatures of 7.2 and 5.1 °C, respectively), leading to the sustained activity of a high-activity mutant at elevated temperatures. Molecular dynamics simulations and protein folding studies elucidate how these mutations favorably modulate conformational landscapes without perturbing the substrate binding architecture. Beyond providing a thermostabilized tool for bioluminescence applications, our integrated methodology presents a framework for engineering enzymes when traditional homology-based approaches fail and stability-activity constraints present formidable barriers to improvement.