- Theoretical studies on the structures of proteins, nucleic acids, and small molecules
- Applications of theoretical studies to develop molecular models and to select new drugs
112 Office and Laboratory Building
Dept. of Biochemistry, Biophysics & Molecular Biology
Iowa State University
Ames, IA 50011
Phone: (515) 294-3833
B.S., Chemistry, California Institute of Technology, 1963
Ph.D., Physical Chemistry, Stanford University, 1967
Postdoctoral Fellow, University of California – San Diego
Computational 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 large structures. Applications are sometimes made to develop molecular models and to select new drugs.
Protein Datamining is used 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. This approach led to useful ways to incorporate structural and hydrophobicity information into simulations. We also showed that polar interactions are important when residues come close together; whereas hydrophobic interactions are effective at longer distances. New approaches are being developed to associate sequences and structures.
Protein Threading. In application of interaction potentials, we demonstrated that they are directly useful for selecting the native forms from among various protein folds.
Conformation Generation. We developed a new approach to enumerate protein lattice conformations with full 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 largely 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
Elastic models of Proteins. Large-domain motions of proteins are computed with simple inter-connected elastic models. These highly cohesive, cooperative models are most appropriate for considering the largest functional motions of proteins, which are necessarily independent of the structural details. 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. The results suggest that high resolution structures are not required in order to understand the functional motions of proteins. Also, these elastic models are appropriate for developing pathways for transitions between distinctive known forms of the same protein. 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. Most recent applications have been to the complete ribosome structure; correlated motions are observed by which the tRNA’s and mRNA pass through the ribosome structure.
Systems Biology. We have begun several projects to integrate the large volumes of data accumulating from genomes, gene expression, proteomics, and metabolomics by considering various types of network models.
Machine learning methods are being used to determine the factors and their relative importance for a given behavior; the advantage of this approach is that many combinations of factors can be evaluated rapidly and automatically.
1: Flatow D, Leelananda SP, Skliros A, Kloczkowski A, Jernigan RL. Volumes and Surface Areas: Geometries and Scaling Relationships Between Coarse-Grained and Atomic Structures. Curr Pharm Des. 2013 May 22. [Epub ahead of print] PubMed PMID: 23713774.
2: Jernigan RL. Possible alternative and extension to the use of dynamics for structure matching: comment on “Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments” by C. Micheletti. Phys Life Rev. 2013 Mar;10(1):37-8; discussion 39-40. doi: 10.1016/j.plrev.2013.01.013. Epub 2013 Feb 1. PubMed PMID: 23394959.
3: Park JK, Jernigan R, Wu Z. Coarse grained normal mode analysis vs. refined Gaussian Network Model for protein residue-level structural fluctuations. Bull Math Biol. 2013 Jan;75(1):124-60. doi: 10.1007/s11538-012-9797-y. Epub 2013 Jan 8. PubMed PMID: 23296997.
4: Gu Y, Sun W, Wang G, Zimmermann MT, Jernigan RL, Fang N. Revealing rotational modes of functionalized gold nanorods on live cell membranes. Small. 2013 Mar 11;9(5):785-92. doi: 10.1002/smll.201201808. Epub 2012 Nov 1. PubMed PMID: 23124917.
5: Huang Y, Bonett S, Kloczkowski A, Jernigan R, Wu Z. P.R.E.S.S.–an R-package for exploring residual-level protein structural statistics. J Bioinform Comput Biol. 2012 Jun;10(3):1242007. doi: 10.1142/S0219720012420073. PubMed PMID: 22809383.
6: Zimmermann MT, Leelananda SP, Kloczkowski A, Jernigan RL. Combining statistical potentials with dynamics-based entropies improves selection from protein decoys and docking poses. J Phys Chem B. 2012 Jun 14;116(23):6725-31. doi: 10.1021/jp2120143. Epub 2012 Apr 23. PubMed PMID: 22490366.
7: Skliros A, Zimmermann MT, Chakraborty D, Saraswathi S, Katebi AR, Leelananda SP, Kloczkowski A, Jernigan RL. The importance of slow motions for protein functional loops. Phys Biol. 2012 Feb 7;9(1):014001. [Epub ahead of print] PubMed PMID: 22314977.
8: Gniewek P, Kolinski A, Jernigan RL, Kloczkowski A. How noise in force fields can affect the structural refinement of protein models? Proteins. 2011 Nov 3. doi: 10.1002/prot.23240. [Epub ahead of print] PubMed PMID: 22223184; PubMed Central PMCID: PMC3326201.
9: Zimmermann MT, Skliros A, Kloczkowski A, Jernigan RL. Immunoglobulin structure exhibits control over CDR motion. Immunome Res. 2011 Nov 8;7(2):5. PubMed PMID:
10: Zimmermann MT, Kloczkowski A, Jernigan RL. MAVENs: motion analysis and visualization of elastic networks and structural ensembles. BMC Bioinformatics. 2011 Jun 28;12:264. doi: 10.1186/1471-2105-12-264. PubMed PMID: 21711533; PubMed Central PMCID: PMC3213244.
11: Leelananda SP, Towfic F, Jernigan RL, Kloczkowski A. Exploration of the relationship between topology and designability of conformations. J Chem Phys. 2011 Jun 21;134(23):235101. doi: 10.1063/1.3596947. PubMed PMID: 21702580; PubMed Central PMCID: PMC3133807.
12: Zimmermann MT, Leelananda SP, Gniewek P, Feng Y, Jernigan RL, Kloczkowski A. Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models. J Struct Funct Genomics. 2011 Jul;12(2):137-47. doi: 10.1007/s10969-011-9113-3. Epub 2011 Jun 15. PubMed PMID: 21674234; PubMed Central PMCID: PMC3227679.
13: Huang Y, Bonett S, Kloczkowski A, Jernigan R, Wu Z. Statistical measures on residue-level protein structural properties. J Struct Funct Genomics. 2011 Jul;12(2):119-36. doi: 10.1007/s10969-011-9104-4. Epub 2011 Mar 31. PubMed PMID: 21452025; PubMed Central PMCID: PMC3694722.
14: Su CC, Long F, Zimmermann MT, Rajashankar KR, Jernigan RL, Yu EW. Crystal structure of the CusBA heavy-metal efflux complex of Escherichia coli. Nature. 2011 Feb 24;470(7335):558-62. doi: 10.1038/nature09743. PubMed PMID: 21350490;
PubMed Central PMCID: PMC3078058.
15: Skliros A, Jernigan RL, Kloczkowski A. Models to Approximate the Motions of Protein Loops. J Chem Theory Comput. 2010 Oct 12;6(10):3249-3258. PubMed PMID: 21031141; PubMed Central PMCID: PMC2963458.
16: Long F, Su CC, Zimmermann MT, Boyken SE, Rajashankar KR, Jernigan RL, Yu EW. Crystal structures of the CusA efflux pump suggest methionine-mediated metal transport. Nature. 2010 Sep 23;467(7314):484-8. doi: 10.1038/nature09395. PubMed PMID: 20865003; PubMed Central PMCID: PMC2946090.
17: Katebi AR, Kloczkowski A, Jernigan RL. Structural interpretation of protein-protein interaction network. BMC Struct Biol. 2010 May 17;10 Suppl 1:S4. doi: 10.1186/1472-6807-10-S1-S4. PubMed PMID: 20487511; PubMed Central PMCID: PMC2873827.
18: Feng Y, Kloczkowski A, Jernigan RL. Potentials ‘R’ Us web-server for protein energy estimations with coarse-grained knowledge-based potentials. BMC Bioinformatics. 2010 Feb 17;11:92. doi: 10.1186/1471-2105-11-92. PubMed PMID: 20163737; PubMed Central PMCID: PMC3098114.
19: Sun X, Di W, Jernigan R, Wu Z. PRTAD: a database for protein residue torsion angle distributions. Int J Data Min Bioinform. 2009;3(4):469-82. PubMed PMID: 20052908; PubMed Central PMCID: PMC3018885.
20: Cui F, Mukhopadhyay K, Young WB, Jernigan RL, Wu Z. Refinement of under-determined loops of Human Prion Protein by database-derived distance constraints. Int J Data Min Bioinform. 2009;3(4):454-68. PubMed PMID: 20052907; PubMed Central PMCID: PMC3018887.