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Liskin Swint-Kruse, Ph.D.

Professor and Chair
Department of Biochemistry and Molecular Biology

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- 2018
University of Kansas Medical Center, Director of Graduate Studies, Biochemistry and Molecular Biology, 2009 - 2018
University of Kansas - Lawrence, Molecular Biosciences, Courtesy Appointment, 2009 - present
University of Kansas Medical Center, Professor and Interim Chair, Biochemistry and Molecular Biology, 2018- present

Interests: Applications of protein evolution to personalized medicine and protein engineering; transcriptional regulation of bacterial metabolism.

Publications: Click here


Major Research Interests

Areas of research emphasis: Protein structure-function for personalized medicine; protein engineering; protein evolution; transcriptional control of bacterial metabolism.

1. Personalized medicine:  Mutations at nonconserved amino acid positions follow novel substitution rules.

Any two unrelated people can have as many as 10,000 amino acid differences between the protein coding regions of their genomes.  To reliably identify variations that alter protein function (and thus potentially alter health), computer prediction algorithms need improve­ment.  To that end, we have examined the assumptions that underlie current algorithms.  Nearly all incorporate (i) se­quence alignments of evolutionarily-related proteins; and (ii) amino acid substitution "rules" derived from laboratory experiments.  However, the decades of past experiments were biased towards positions that do not change during evolution (conserved).  In many proteins, more than half of their amino acids do change during evolution.  

We experimentally tested whether "text-book" assumptions apply to nonconserved positions.  Our first study used 10 LacI/GalR homologs in which 12 non­conserved positions were substituted with multiple amino acids each.  When the variants were assayed for function, outcomes for each position 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 ("toggle").   


   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.   Finally, test with prediction algorithms showed that mutation outcomes at rheostat positions were very poorly predicted by current algorithms. 

We are leading a large team of investigators to assess rheostatic positions in several other proteins and to test hypothe­ses about how to identify their locations.  We are also exploring the underlying biophysical properties that give rise to these com­plex mutational outcomes.  Results will be used to improve prediction algorithms and thereby enhance personalized medicine. 

This work has been funded by the W. M. Keck Foundation (abstract here)  and by the NIH (abstract here).


2. Building synthetic bacterial transcription circuitry using engineered repressor proteins.

In biotechnology, bacterial metabolic or synthesis pathways can be controlled using the rules of Boolean logic, via transcription factors that respond to external signals (e.g.the addition of small molecules).  In a collaboration with the Bennett lab at Rice University, we have designed synthetic transcription repressors that create "AND" logic gates.  These were created 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.  At least 4 chimeras can be co-expressed 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.  Experiments are in progress to use transcription activators to build "OR" gates.   

New studies are in progress to expand the range and types of synthetic circuits that can be built from chimeras for use in biotechnology.  This work has been funded by the NIH (abstract here).



 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 called "Cra" (Catabolite Repressor Activator protein). We identified a novel interaction between Cra and a metabolic kinase.  In collaboration with the Fenton lab (KUMC), 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.



Computational Tools and Resources Available for General Use

1. Link to MARS algorithm for inserting new sequences into manually-edited sequence alignments without disturbing the existing alignment.

2. Link to CoEvolution Utilities contains software for creating ensembles of randomly sampled protein sequence alignments. This software also enables (i) ensemble averaging of analysis scores; (ii) parallel implementation of 5 mathematically-divergent, commonly-used co-evolution packages; and (iii) comparisons of scores across all possible significance thresholds.  

 3. The same Link to CoEvolution Utilities contains software to determine "Eigenvector centrality" from co-evolution networks.  This analysis identifies which positions in a sequence alignment exhibit the greatest total evolutionary constraint from their interactions with all other protein positions, as opposed to the strongest individual constraints that occur among pairs of positions (calculated by co-evolution algorithms).   

4. Structure comparisons:  RESMAP License Agreement and Register to Download RESMAP 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.  A 2D network representation of protein interfaces allows many structures to be compared at an intermediate level of detail.  These analyses can be used to represent inter-domain and inter-subunit interactions, protein-ligand interactions (DNA and small molecule), and long-range, intra-domain amino acid interactions.  Such network representation is useful for (i) monitoring structural changes during molecular dynamics simulations, (ii) comparing alternatively liganded structures, and (iii) comparing homolog structures.  


 5. The AlloRep database of sequence, mutational, and structural information for LacI/GalR homologs.  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. We have surveyed decades of published literature to collect, categorize, and list citations for: (i) all known LacI/GalR amino acid variants, (ii) most of the available structures, and (iii) a gold-standard sequence alignment for bona fide LacI/GalR repressors.  The online database is searchable using SQL.

6.  The RheoScale calculator for quantitatively discriminating rheostat, toggle, and neutral protein positions.  Complex mutational behaviors can be identified for individual protein positions when a range of amino acid substitutions is considered.  The RheoScale calculator condenses experimental data from multiple amino acid substitutions into a set of scores that describe the overall mutation outcomes for that position.  These analyses can be helpful for analyzing data from deep mutational scanning experiments.  The condensed scores also facilitate correlations of functional data with the structural and bioinformatic analyses that are required to improve mutation predictions needed for personalized medicine.  The RheoScale calculator is encoded into a Microsoft® Excel workbook. 

The RheoScale calculator files can be found in the supporting information to Hodges et al, 2018, Human Mutation, 39:1814-1826. 


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


Last modified: Feb 04, 2020


Liskin Swint-Kruse, Ph.D.
Professor and Chair