Department of Biochemistry and Molecular Biology
EDUCATION AND APPOINTMENTS
Baylor University, Waco, TX, B.S. Chemistry, 1990
University of Iowa, Iowa City, Ph.D., Biochemistry, 1995
W.M. Keck Center for Computational Biology, Postdoctoral Fellow, 1995-99
Rice University, Biochemistry & Cell Biology Robert A. Welch Postdoctoral Fellow, 2000-2002
Rice University, Biochemistry & Cell Biology, Research Scientist, 2002-2004
University of Kansas Medical Center, Assistant Professor, Biochemistry and Molecular Biology, 2004 - 2009
University of Kansas Medical Center, Associate Professor, Biochemistry and Molecular Biology, 2009 - 2017
University of Kansas Medical Center, Professor, Biochemistry and Molecular Biology, 2017- present
University of Kansas - Lawrence, Associate Professor, Molecular Biosciences, Courtesy Appointment, 2009 - present
University of Kansas Medical Center, Director of Graduate Studies, Biochemistry and Molecular Biology, 2009 - present
Publications: Click here
Major Research Interests
Areas of research emphasis: Protein structure-function for personalized medicine; protein engineering; protein evolution; transcription control of bacterial metabolism
1. Personalized medicine: Mutations at nonconserved amino acid positions follow novel substitution "rules"
Each patient genome can have as many as 10,000 variations in protein coding regions. To filter for amino acid changes that alter protein function, high-performing computer algorithms are needed. Many algorithms have been developed, but their success rates need improvement. Nearly all algorithms incorporate (i) sequence alignments of evolutionarily-related proteins (homologs); and (ii) amino acid substitution "rules" derived from laboratory experiments. However, experiments have been highly biased towards positions that are conserved during evolution. In many proteins, more than half of their amino acids change during evolution. Thus, the accepted substitution rules might not apply to nonconserved positions.
We are carrying out experimental studies to bridge this gap. In our first study, we used 10 LacI/GalR homologs in which we substituted 12 nonconserved positions with multiple amino acids each. When the variants at each position were assayed for function, outcomes ranged progressively over several orders of magnitude. Thus, these positions appear to act as functional rheostats during the evolution of functional variation. This contrasts with mutation outcomes at conserved positions, which usually destroy function unless the starting and substituted amino acids are similar. In further contrast, mutational outcomes at rheostatic positions (i) were not explained by physico-chemical similarities among amino acids; (ii) did not correlate with amino acid presence in evolutionary data; and (iii) could not be extrapolated from one homolog to another. Thus, mutation outcomes at rheostatic, nonconserved positions are likely to be falsely predicted by current algorithms.
The functional data for these ~1100 variants are freely available to the scientific community for testing new algorithms.
We are now assessing rheostatic positions in other proteins and testing hypotheses about how to identify them. In the near future, we will explore the underlying physical properties that give rise to these complex mutational outcomes, so that improved algorithms can be developed to enhance personalized medicine.
2. Synthetic bacterial transcription circuitry using engineered repressor proteins
To compare mutational outcomes among LacI/GalR transcription regulatory proteins (project 1), we had to overcome a problem in data interpretation: If natural homologs were used, transcription differences could arise from either differences among the LacI/GalR proteins or from differences among their cognate DNA operators. To eliminate the DNA variable, we designed chimeric repressors by mixing-and-matching the DNA binding and regulatory domains of 10 LacI/GalR homologs. The resulting chimeras all bind the same DNA (lacO) but are allosterically regulated by the small molecules specific to each regulatory domain.
We are now using these chimeras in a collaboration with the Bennett lab at Rice University to build synthetic transcription "circuits". We successfully combined up to 4 chimeras to create Boolean "AND" logic gates. For example, if an E. coli expressed LacI along with the TreR and RbsR chimeras, then IPTG AND trehalose AND ribose were required to induce the target gene. In addition, we demonstrated functionality that can be used to create a "NOT" logic gate. These new tools greatly expand the range of synthetic circuits that can be used in biotechnology.
New studies are in progress to expand the range and types of synthetic circuits that can be built from chimeras.
3. A Cra-Kinase complex alters regulation of central metabolism of γ-proteobacteria
For γ-proteobacteria, several key processes are regulated by the LacI/GalR homolog "Cra" (Catabolite Repressor Activator protein). We identified a novel interaction between Cra and a kinase. We are working to identify the functional significance of the Cra-kinase interaction. Results will identify new ways to perturb central metabolism in γ-proteobacteria, which might be exploited to target a select group of enteric bacteria.
4. In vivo - in vitro correlations of transcription regulator function
The fields of molecular biology and physical biochemistry measure protein function in different contexts - the cell and the test tube. Each approach has potential drawbacks: In the cell, is the measured function clearly assignable to one protein/process? In the test tube, is the protein environment too far from that of the cell? We designed and directed studies to reconcile these environments for the experiments described in contribution 1 (above).
These studies conclusively showed that, for the LacI/GalR proteins, altered DNA binding affinity impacted transcription regulation in a predictable manner. Results also indicated a difference between in vitro and in vivo conditions that altered affinity by ~25-fold; however, all repressor variants were equally affected. Further, we showed that even 2-fold changes in affinity/repression altered the growth of the normal E. coli host for these proteins. Along with collaborators Dr. Dorothy Beckett (U. Maryland-College Park) and Dr. David Bain (U. Colorado-Denver), we developed generalized theories that allow similar studies of other protein-DNA binding pairs.
Thermodynamic measurements of protein-DNA binding interactions are ongoing for many projects in the Swint-Kruse lab.
5. Evolution of the LacI/GalR and other protein families
The LacI/GalR family of transcription regulators is widely used as a model system for developing computer algorithms to model protein evolution or predict mutational outcomes. However, the sequence set used in many studies was compiled before the explosion of new bacterial genomes and contained < 60 sequences. I directed several studies to collect, categorize, manually align, and analyze >2000 sequences of this family; these data are freely available to the scientific community. We developed a software pipeline for co-evolution analyses that is freely available for download. This software is notable because it easily enables (i) ensemble averaging of analysis scores; (ii) parallel implementation of 5 mathematically-divergent, commonly-used co-evolution packages; and (iii) comparisons of scores for full-length alignments, which avoids setting arbitrary thresholds in analysis scores.
In addition, our analyses yielded important information about the LacI/GalR proteins. Our studies identified: (i) structural regions required for dimerization; (ii) a sequence motif that co-evolves with one of the two types of cognate DNA operators; and (iii) structural plasticity of the tertiary structure that appears to allow each paralog group to utilize different "key" amino acid positions.
We are now applying these calculations to other protein families, including the pyruvate kinase family (in collaboration with Dr. Aron Fenton, KUMC). In addition, we are continuing to develop new calculations for detecting patterns of amino acid change in evolutionary data. https://sourceforge.net/projects/coevolutils/
6. A simple strategy for comparing multiple, complex protein structures
For simple comparisons of protein structure, super-impositions of 3D structures can be useful. However, if one wishes to compare large regions or more than two structures, 3D overlays quickly become uninterpretable. We developed a 2D network representation of protein interfaces that allows many structures to be compared at an intermediate level of detail. We used this method to effectively represent inter-domain and inter-subunit interactions as well as protein-ligand interactions (DNA and small molecule). This method can also be used to represent long-range, intra-domain amino acid interactions. We found network representation to be useful for (i) monitoring structural changes during molecular dynamics simulations, (ii) comparing alternatively liganded structures, and (iii) comparing homolog structures.
To automate this analysis, we developed the Resmap software, which is freely available to the scientific community for download. http://www.kumc.edu/resmap.html
RESMAP License Agreement and Register to Download RESMAP
Link to CoEvolution Utilities
Link to MARS algorithm for expanding manually-edited sequence alignments
The AlloRep database of sequence, mutational, and structural information for Laci/GalR homologs
Liskin Swint-Kruse, Ph.D.