AI is revolutionizing computational biology.  Our primary focus is on computations on cells, proteins, nucleic acids, and small molecules, and their interactions. The large protein language models change everything and suddenly make understanding proteins at a deep level immediately possible.  We work at the interface between sequence and structure and on the relationship between dynamics and mechanism; these studies now become more feasible and produce more meaningful results.  We find datamining the language models to enable most of our current projects aimed at understanding protein function and mechanism. 

We are located at the Department of Biochemistry, Biophysics, and Molecular Biology at Iowa State University Ames, Iowa. Our research is pushing toward the comprehension of the functions and mechanisms of all proteins.

Jernigan:

• 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
• Over 350 publications
• High impact publications – 1 paper over 2000 citations
• 22,000+ citations, h-index 70, i10-index 220
• Fellow, AAAS and Biophysical Society
• Book “Protein Actions” received the 2018 PROSE award for best textbook in the biological and life sciences.

Research Projects:

Our group scored in the top 1-2% in the CAFA5 competition for predicting protein function!

Grant from NIH-HGRI Novel Use of Genome Information to Understand Mutations

Grant from NIH-NIGMS Compensating Protein Mutations

2024 PhD Graduation of Mesih Kilinc

An interview with Ben Litterer (undergrad researcher in our lab) in an ISU LAS publication.

• Datamining Large Protein Language Models (AI)
• Assigning Functions to Proteins
• Identifying New Orthologs and New Paralogs
• Improving Protein Sequence Matching
• Improved Protein Potentials - especially Entropies
• Understanding Protein Mutants - Discriminating Bad from Good
• Protein Dynamics from Sequences
• Protein Mechanism from Dynamics
• Simplifying and Improving Protein Calculations
• Many-Body Correlations from Sequences
• Connecting Sequence Correlations to Dynamics