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Creators/Authors contains: "Jernigan, Robert L."

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  1. Kinesin-mediated transport along microtubules is critical for axon development and health. Mutations in the kinesin Kif21a, or the microtubule subunit β-tubulin, inhibit axon growth and/or maintenance resulting in the eye-movement disorder congenital fibrosis of the extraocular muscles (CFEOM). While most examined CFEOM-causing β-tubulin mutations inhibit kinesin–microtubule interactions, Kif21a mutations activate the motor protein. These contrasting observations have led to opposed models of inhibited or hyperactive Kif21a in CFEOM. We show that, contrary to other CFEOM-causing β-tubulin mutations, R380C enhances kinesin activity. Expression of β-tubulin-R380C increases kinesin-mediated peroxisome transport in S2 cells. The binding frequency, percent motile engagements, run length and plus-end dwell time of Kif21a are also elevated on β-tubulin-R380C compared with wildtype microtubules in vitro. This conserved effect persists across tubulins from multiple species and kinesins from different families. The enhanced activity is independent of tail-mediated kinesin autoinhibition and thus utilizes a mechanism distinct from CFEOM-causing Kif21a mutations. Using molecular dynamics, we visualize how β-tubulin-R380C allosterically alters critical structural elements within the kinesin motor domain, suggesting a basis for the enhanced motility. These findings resolve the disparate models and confirm that inhibited or increased kinesin activity can both contribute to CFEOM. They also demonstrate the microtubule’s role in regulating kinesins and highlight the importance of balanced transport for cellular and organismal health. 
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    Free, publicly-accessible full text available March 1, 2025
  2. Abstract Motivation

    Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.

    Results

    Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein’s global dynamics.

    Availability and implementation

    APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

     
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  3. Abstract Summary

    A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/).

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  4. null (Ed.)
    Abstract Glycosyltransferases (GTs) are a large family of enzymes that add sugars to a broad range of acceptor substrates, including polysaccharides, proteins, and lipids, by utilizing a wide variety of donor substrates in the form of activated sugars. Individual GTs have generally been considered to exhibit a high level of substrate specificity, but this has not been thoroughly investigated across the extremely large set of GTs. Here we investigate Xyloglucan Xylosyltransferase 1 (XXT1), a GT involved in synthesis of the plant cell wall polysaccharide, xyloglucan. Xyloglucan has a glucan backbone, with initial side chain substitutions exclusively composed of xylose from UDP-Xylose. While this conserved substitution pattern suggests a high substrate specificity for XXT1, our in vitro kinetic studies elucidate a more complex set of behavior. Kinetic studies demonstrate comparable kcat values for reactions with UDP-Xylose and UDP-Glucose, while reactions with UDP-Arabinose and UDP-Galactose are over 10-fold slower. Using kcat/Km as a measure of efficiency, UDP-Xylose is 8-fold more efficient as a substrate than the next best alternative, UDP-Glucose. To the best of our knowledge, we are the first to demonstrate that not all plant XXTs are highly substrate specific, and some do show significant promiscuity in their in vitro reactions. Kinetic parameters alone likely do not explain the high substrate selectivity in planta, suggesting there are additional control mechanisms operating during polysaccharide biosynthesis. Improved understanding of substrate specificity of the GTs will aid in protein engineering, development of diagnostic tools, and understanding of biological systems. 
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  5. null (Ed.)
    Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials. 
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  6. Abstract

    Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small‐large pair changing to a large‐small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for “twilight zone” protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation.

     
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  7. Entropy should directly reflect the extent of disorder in proteins. By clustering structurally related proteins and studying the multiple-sequence-alignment of the sequences of these clusters, we were able to link between sequence, structure, and disorder information. We introduced several parameters as measures of fluctuations at a given MSA site and used these as representative of the sequence and structure entropy at that site. In general, we found a tendency for negative correlations between disorder and structure, and significant positive correlations between disorder and the fluctuations in the system. We also found evidence for residue-type conservation for those residues proximate to potentially disordered sites. Mutation at the disorder site itself appear to be allowed. In addition, we found positive correlation for disorder and accessible surface area, validating that disordered residues occur in exposed regions of proteins. Finally, we also found that fluctuations in the dihedral angles at the original mutated residue and disorder are positively correlated while dihedral angle fluctuations in spatially proximal residues are negatively correlated with disorder. Our results seem to indicate permissible variability in the disordered site, but greater rigidity in the parts of the protein with which the disordered site interacts. This is another indication that disordered residues are involved in protein function. 
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  8. With the growth of the PDB and simultaneous slowing of the discovery of new protein folds, we may be able to answer the question of how discrete protein fold space is. Studies by Skolnick et al. (PNAS, 106, 15690, 2009) have concluded that it is in fact continuous. In the present work we extend our initial observation (PNAS, 106(51) E137, 2009) that this conclusion depends upon the resolution with which structures are considered, making the determination of what resolution is most useful of importance. We utilize graph theoretical approaches to investigate the connectedness of the protein structure universe, showing that the modularity of protein domain architecture is of fundamental importance for future improvements in structure matching, impacting our understanding of protein domain evolution and modification. We show that state-of-the-art structure superimposition algorithms are unable to distinguish between conformational and topological variation. This work is not only important for our understanding of the discreteness of protein fold space, but informs the more critical question of what precisely should be spatially aligned in structure superimposition. The metric-dependence is also investigated leading to the conclusion that fold usage in homology reduced datasets is very similar to usage across all of PDB and should not be ignored in large scale studies of protein structure similarity. 
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