The Jernigan Laboratory - Personnel: Jernigan

Research Interests

Theoretical studies on the structures of proteins, nucleic acids, and small molecules, and their interactions. Overall the direction of his research has been to push toward the comprehension of the functions of extremely large structures. Applications are sometimes made to develop molecular models and to select new drugs.

Protein Datamining. One focus has been on developing simpler ways to assess protein structures and their folding patterns. We have evaluated interactions from available structures and other experimental data. We developed a standard way to view interaction energies between residues, based on sets of protein structures (Miyazawa & Jernigan, 1985). This approach led to useful ways to incorporate structural and hydrophobicity information into simulations. The 1996 update (Miyazawa & Jernigan, 1996) of this work proved its validity by showing that values are little changed, even over a decade during which the body of protein structures had increased by at least two orders of magnitude. We also demonstrated that polar interactions are more important when atoms come extremely close together (Bahar & Jernigan, 1996); whereas hydrophobic interactions are most effective at longer distances.

Machine learning methods are being developed (with V. Honavar and D. Dobbs) to determine what factors are most important for a given behavior; the advantage is that many combinations of factors can be evaluated rapidly and automatically.

Protein Threading. In an application of these interaction potentials, we demonstrated that they are directly useful for selecting the native forms from among various protein folds in one of the earlier demonstrations of threading a sequence through structures(Covell & Jernigan, 1990); more recent applications in this field have continued. Subsequently this approach, both for generating lattice conformations in restricted spaces, and for evaluating conformations and threadings has been widely taken up by others.

Protein Conformation Generation. In a recent study (Kloczkowski & Jernigan, submitted) we have developed a new way to enumerate protein conformations with high efficiency. This is particularly important for determining native protein conformations, where the problem is akin to searching for a needle in a haystack, and random searches are usually ineffective. This new approach opens the way for the computer generation of much larger numbers of protein conformations.

Libraries of protein-like conformations are being accumulated in which conformations with secondary structure biases are generated within compact spaces. These libraries can be utilized to supplement known structures to obtain structures that are consistent with a limited set of experimental constraints [with O. Tcherkasskaya (Georgetown Univ.) and E. Appella and S. Mazur (NCI, NIH)].

Nucleic Acid Conformations. In applications to nucleic acids, models of sequence specific triple helices were developed (Zhurkin, et al., 1994). This basic work demonstrated that DNA bases can be uniquely recognized in an alternative way to the standard Watson-Crick pairing scheme. By stretching the double helix, with the Watson-Crick pairs remaining intact, a third strand parallel to its identical strand can be iso-geometrically positioned in the major groove and interact uniquely in a sequence specific way with the DNA base pairs. The strand directionality and specificity are critical to DNA recombination where identical strands are broken and reformed. The critical aspect for achieving specificity was the realization that the redundancies of similarly favorable alternative forms where the third strand bridges adjacent base pairs are removed whenever the double helix is elongated.

In a novel study of the combinatorial binding between peptides and nucleotides we developed (Lustig & Jernigan, 1995) new ways to extract interaction energies from binding experiments using libraries of combinatorially synthesized DNA or peptide sequences, or even from the sequence database variability of functional sites. The work demonstrated strong correlations among interaction strengths derived from such diverse data. This approach is important for compiling and comprehending rapidly the cumulative results from combinatorial syntheses. It is an important extension to other types of experimental data of the structural approach for extracting interaction energies.

In other recent studies (unpublished), we have been developing ways to apply these interaction evaluations to the selection of new drugs against target proteins. Preliminary screenings based on these approaches are promising.

Elastic models of Proteins. Large-motions of proteins are being studied with simple inter-connected elastic models. These highly cohesive, highly cooperative models are most appropriate for considering the largest motions of proteins, which are necessarily independent of the structural details. The coarse-graining of structure is self-consistent with looking at these motions. Functional mechanisms for processing proteins or for protein machines can be developed. The methods lend themselves in straightforward ways to the investigation of the motions of extremely large biomolecular assemblages of more than 100,000 residues. Importantly, these results suggest that high resolution structures are not required in order to understand the functional motions of proteins.

Movie of GoEL/GroES. in its slowest motion, where the upper ring rotates in the opposite direction to the lower ring (one GroEL monomer shown in blue).

Also, these elastic models are appropriate for developing pathways for transitions between distinctive known forms of the same protein [with G. Chirikjian and T. Woolf (Johns Hopkins Univ.) and M. Gerstein (Yale Univ.)]. Pathways developed in this way are more realistic than ones obtained simply by coordinate interpolations, and can aid in directing atomic simulations along realistic pathways.

Maturation of Hk97 Viral Capsid, passing from immature, smaller more spherical form to the mature icosahedral expanded form.

Molecular Models and Structure Predictions. Incorporation of information about sequence conservation into structure prediction requires a comprehension of the sequence/structure/function interfaces. Sequence substitution matrices have been developed on the basis of structures. Sequence conservation has been treated in various ways to comprehend the cores of proteins. Sequence variation is a useful metric regarding the critical nature of pieces of structure, and has served to improve the predictions of secondary structures (with A. Kloczkowski and J. Garnier, INRA, Versailles). Various new molecular models are being developed, including one of the p53 tetramer bound to DNA (with V. Zhurkin, NCI, NIH).

Collaborators

At Iowa State University

Srinivas Aluru, Professor, Department of Electrical and Computer Engineering
Volker Brendel, Bergdahl Professor of Bioinformatics, Genetics, Development and Cell Biology
Julie Dickerson, Associate Professor, Department of Electrical and Computer Engineering
Drena Dobbs, Associate Professor, Genetics, Development and Cell Biology Department
Karin Dorman, Assistant Professor, Department of Statistics
Vasant Honavar, Professor, Department of Computer Science
Xiaoqiu Huang, Associate Professor, Department of Computer Science
James Reecy, Assistant Professor, Animal Science Department
Patrick S Schnable, Professor, Agronomy Department and Director, Center for Plant Genomics, Plant Sciences Institute
Arun K Somani, Department Chair and Professor, Department of Electrical and Computer Engineering
Guang Song, Assistant Professor, Department of Computer Science
Zhijun Wu, Associate Professor, Department of Mathematics

Elsewhere

Gregory S Chirikjian, Department Chair and Professor, Department of Mechanical Engineering, Johns Hopkins University
Daniel Flatow, Information Technology Specialist, Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda
Sanzo Miyazawa, Associate Professor, Department of Computer Science, Gunma University, Japan
Piotr Pokarowski, Assistant Professor, Institute of Applied Mathematics and Mechanics, University of Warsaw
Yongmei Wang, Associate Professor, University of Memphis