Jernigan Laboratory
Welcome!
Our primary focus is on computations on cells, proteins, nucleic acids, and small molecules, and their interactions. Often we work at the interface between sequence and structure and on the relationship between dynamics and mechanism. In this latter case we often use simple models to improve comprehension of mechanisms. We often perform datamining and data analyses.
We are located at Department of Biochemistry, Biophysics, and Molecular Biology at Iowa State University Ames, Iowa. Our research is pushing toward the comprehension of the functions of and mechanisms of larger structures, and the use of large, diverse data to construct models of these.
Jernigan's research background:
- Mining information from protein structures and using it to evaluate predicted structures – Miyazawa-Jernigan potentials, elastic network deformations of structures, protein and ribosome mechanism.
- Knowledgeable about a wide variety of research subjects, from the physical sciences to the biological sciences, from biomedicine to genomics, with a broad view of basic and applied research.
- A recognized authority in computational and structural biology, with broader interests in bioinformatics, genomics, structural biology, protein engineering and sub-cellular biology
- High impact publications – 1 paper ~ 1900 citations, and 49 papers with > 100 citations each
- 20,000+ citations, h-index 67
- Fellow, AAAS
- Fellow, Biophysical Society
- Book “Protein Actions” 2018 PROSE award for best textbook in the biological and life sciences.
Medical and Evolution Applications
Higher Order Correlations in Biology
Dynamics and Mechanisms
Current Projects
- Datamining Large Protein Language Models (AI)
- Assigning Functions to Proteins
- Identifying New Homologs and Paralogs
- Improving Protein Sequence Matching
- Improved Protein Potentials - especially Entropies
- Understanding Protein Mutants - Discriminating Bad from Good
- Protein Engineering
- Protein Mechanism from Dynamics
- Simplifying and Improving Protein Calculations
- Many-Body Correlations from Sequences
- Connecting Sequence Correlations to Dynamics
Using Protein Language Models for Remote Homolog Detection
Detection of a Remote Homolog for HPO30 and Validation with Predicted Structure Comparison. (a) Alignment of HPO30 and human homolog Q8NCR9 found by PROST. Sequence identity is 21.5%, with gap ratio of 32.8%. Identical residues are colored based on chemical features. (b) Structure matching of the two Alphafold2 predicted structures of HPO30 (red) and CLRN3 (blue) human homolog that is found by PROST. Structural alignment of 160 core residues has RSMD = 3.4 Å.