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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

Atomic models of proteins built by homology modeling or from low-resolution experimental data may contain considerable local errors. The refinement success of molecular dynamics simulations is usually limited by both force field accuracy and by the substantial width of the conformational distribution at physiological temperatures. We propose a method to overcome both these problems by coupling homologous replicas during a molecular dynamics simulation, which narrows the conformational distribution, and smoothens and even improves the energy landscape by adding evolutionary information.
A novel protein refinement protocol is presented which utilizes molecular dynamics (MD) simulations of an ensemble of adaptively restrained homologous replicas. This approach adds evolutionary information to the force field and reduces random conformational fluctuations by coupling of several replicas. It is shown that this protocol refines the majority of models from the CASP11 refinement category and that larger conformational changes of the starting structure are possible than with current state of the art methods. The performance of this protocol in the CASP11 experiment is discussed. We found that the quality of the refined model is correlated with the structural variance of the coupled replicas, which therefore provides a good estimator of model quality. Furthermore, some remarkable refinement results are discussed in detail.
Joshua L. Ebbert and Dennis Della Corte (et al.)

Background: Deep learning systems for Gleason grading are developed almost entirely on high-end clinical scanners and on cohorts from a small number of Western institutions, yet deployment increasingly involves other devices and other populations. These two distribution shifts, device and population, are rarely tested together on the same physical slides. The PAR dataset, from Erbil, Iraq, digitizes each biopsy on three scanners and provides three distinct pathologist grades, so it permits both tests at once on a Middle Eastern cohort. A concurrent study by the dataset originators validated a task-specific model and two foundation models on PAR; we complement it by testing an independently developed detect-then-grade pipeline and by separating scanner effects on detection from scanner effects on grading. Methods: We applied one fixed model developed on North American and European material to all 1017 whole-slide images (339 slides from 185 patients, three scanners; 49.6% clinically significant cancer) with no scanner-specific or population-specific tuning. We measured cancer detection (area under the ROC curve of the predicted cancer tissue fraction), all-slide ISUP agreement of the deployed detect-then-grade pipeline (quadratic-weighted kappa, QWK), and grading agreement on pathologist-confirmed cancers, at the slide level and, because a case carries up to two slides, at the patient level. The reference reader was S.A.; thresholds and operating points were cross-validated leave-one-out; scanners were compared by paired within-biopsy bootstrap and confidence intervals confirmed by patient-cluster bootstrap. Results: Detection was statistically equivalent across scanners (AUC 0.987 to 0.991; paired differences at most 0.003) and transferred to this non-Western cohort with no per-population tuning. At a 95% sensitivity operating point, the deployed pipeline reached cross-validated all-slide QWK of 0.86, 0.81, and 0.86 (Grundium, Hamamatsu, Leica), matching the inter-pathologist ceiling of 0.81, against 0.23 to 0.62 for the ungated model. Grading of confirmed cancers was scanner-dependent: the compact Grundium (0.63) did not differ from the clinical Leica (0.67; paired difference 0.04, 95% CI −0.03 to 0.11), while both exceeded Hamamatsu (0.44). Results held at the patient level, with grading somewhat lower for two scanners; the two slides of a case disagreed in grade in 43% of cases, and patient clustering did not widen the intervals. Conclusions: Cancer tissue fraction is a triage signal robust across scanner and transferable to an underrepresented population for detection, while grading is the scanner-sensitive step. Prostate grading models should be deployed as a detect-then-grade pipeline, with grading validated per device and confirmed on the local population.