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
Here we show that a solvent-exposed f-position (i.e., residue 14) within a well-characterized trimeric helix bundle can facilitate a stabilizing long-range synergistic interaction involving b-position Glu10 (i.e., i–4 relative to residue 14) and c-position Lys18 (i.e., i+4), depending the identity of residue 14. The extent of stabilization associated with the Glu10-Lys18 pair depends primarily on the presence of a side-chain hydrogen-bond donor at residue 14; the non-polar or hydrophobic character of residue 14 plays a smaller but still significant role. Crystal structures and molecular dynamics simulations indicate that Glu10 and Lys18 do not interact directly with each other but suggest the possibility that the proximity of residue 14 with Lys18 allows Glu10 to interact favorably with nearby Lys7. Subsequent thermodynamic experiments confirm the important role of Lys7 in the large synergistic stabilization associated with the Glu10-Lys18 pair. Our results highlight the exquisite complexity and surprising long range of synergistic interactions among b-, c-, and f-position residues within helix bundles, suggesting new possibilities for engineering hyper-stable helix bundles and emphasizing the need to consider carefully the impact of substitutions at these positions for application-specific purposes.
Purpose
The purpose of this technical paper is to evaluate the emerging standard “Allotrope Data Format (ADF)” in the context of digital preservation at a major US academic library hosted at Brigham Young University. In combination with the new information management system ZONTAL Space (ZS), archiving with the ADF is compared with currently used systems CONTENTdm and ROSETTA.
Design/methodology/approach
The approach is a workflow-based comparison in terms of usability, functionality and reliability of the systems. Current workflows are replaced by optimized target processes, which limit the number of involved parties and process steps. The connectors or manual solutions between the current workflow steps are replaced with automatic functions inside of ZS. Reporting functionalities inside of ZS are used to track system and file lifecycle to ensure stability and data preservation.
Findings
The authors find that the target processes leveraging ZS drastically reduce complexity compared to current workflows. Archiving with the ADF is found to decrease integration complexity and provide a more robust data migration path for the future. The possibility to enrich data automatically with metadata and to store this information alongside the content in the same information package increases reusability of the data.
Research limitations/implications
The practical implications of this work suggest the arrival of a new information management system that can potentially revolutionize the archiving landscape within libraries. Beyond the scope of the initial proof of concept, the potential for the system can be seen to replace existing data management tools and provide access to new data analytics applications, like smart recommender systems.
Originality/value
The value of this study is a systematic introduction of ZS and the ADF, two emerging solutions from the Pharmaceutical Industry, to the broader audience of digital preservation experts within US libraries. The authors consider the exchange of best practices and solutions between industries to be of high value to the communities.
Research Opportunities
- Protein Engineering
We develop and apply AI methods to the design of proteins.
- Data Science in Nutrition
We develop data science tools to understand the link between dietary intakes and health outcomes.
- AI in Medicine
We train AI models for applications in the medical field, particular emphasis on automatic prostate cancer diagnosis.