Robert Jernigan

Robert Jernigan

Position
  • Charles F. Curtiss Distinguished Professor
Dr. Jernigan has pursued studies of protein structure and sequence with the goal of comprehending the essential aspects of their function. He has an extremely broad interdisciplinary scientific research and research administrative background. He trained as a physical chemist, and was many years at the NIH in Bethesda, where he was Deputy Laboratory Chief of the Laboratory of Experimental and Computational Biology and head of the Molecular Structure Section in the National Cancer Institute. He moved in 2002 to direct the Baker Center for Bioinformatics and Biological Statistics at Iowa State University. He is pursuing improved sequence matches, using large protein models as data useful for many predictive purposes, and more reliable and more specific function identification from protein sequences.

Contact

Email
jernigan@iastate.edu
Phone
515-294-3833

Contact Info

4104 Molecular Biology Bu
2437 Pammel Drive
Ames
,
IA
50011-1079
Social Media and Websites

Education

  • B.S., Chemistry, California Institute of Technology, 1963
  • Ph.D., Physical Chemistry, Stanford University, 1968
  • Postdoctoral Fellow, University of California,San Diego 1970

Interdepartmental Programs

His research career has focused primarily on molecular studies, ranging broadly from small drug molecules to proteins, and most recently to larger molecular assemblages. His early analyses of protein structures yielded the Miyazawa-Jernigan contact potentials. He pioneered the application of elastic networks to compute functional protein motions, showed that these models yield important information about the slow, large-scale domain motions of proteins, that these motions depend mostly on the overall shapes, and that these models indicate what are the largest, most important motions of proteins. These domain motions relate closely to protein mechanisms, as he showed for reverse transcriptase, the ribosome and other protein structures. He has worked extensively on datamining from protein structures, to derive interaction information, contact energies and entropies, and structural information to improve sequence matching. He demonstrated that representing interactions between collective groups of atoms in proteins is a more effective way to represent their interactions than is the use of atomic interactions. He has a long-standing interest in using molecular computations to aid in interpreting experiments, recently for single molecule experiments on cell adhesion. Recent focus has been on using large protein language models to infer function and other protein behaviors from sequences. This is leading to better ways to understand mutations and to distinguish the most important ones.

Area of Expertise: 

  • Structural Bioinformatics
  • Computational Biology
  • Sequence Analyses
  • Datamining
  • Molecular Mechanisms
  • Molecular Dynamics
  • Protein/Gene Functions