StickWRLD was made possible in part through funding provided to Dr. Ray by the Research Institute at Nationwide Children's Hospital, and by NSF grant DBI-1262457.

1. Software Download &#38; Installation
<ol>
	<li>Use a computer has an Intel i5 or better processor with at least 4 Gb of RAM, and is running Mac OS X or GNU/Linux (e.g., Ubuntu) OS. In addition, Python 2.7.610 and the wxPython 2.811, SciPy12, and PyOpenGL13 python libraries are required - download and install each from their respective repositories.</li>
	<li>Download <a href="http://www.stickwrld.org" target="_blank">StickWRLD</a> as a zip archive containing all of the relevant Python scripts. Download the &#8220;fasta2stick.sh&#8221; script for converting standard FASTA DNA/protein sequence alignments to StickWRLD format.</li>
	<li>Extract the archive and put the resulting StickWRLD folder on your Desktop. Place the &#8220;fasta2stick.sh&#8221; script on the desktop as well.</li>
</ol>
2. Prepare the Alignment
<ol>
	<li>Create an alignment of the protein sequences using any standard alignment software (e.g., ClustalX14). Save the alignment on the desktop in FASTA format.</li>
	<li>Open the terminal application on the Mac or GNU/Linux computer and navigate to the desktop (the location of the &#8220;fasta2stick.sh&#8221; shell script) by typing cd ~/Desktop and pressing return. Execute the &#8220;fasta2stick.sh&#8221; script by typing ./fasta2stick.sh in the terminal. If the script does not execute, ensure that it is executable &#8211; in the terminal type chmod +x fasta2stick.sh to make the script executable.</li>
	<li>Follow the onscreen instructions provided by the script to specify the input file name (the file created in 1.2 above) and the desired output name. Save the output file (which is now in the correct format for StickWRLD) on the desktop.</li>
</ol>
3. Launching StickWRLD
<ol>
	<li>Navigate into the StickWRLD executables folder using the terminal application of the Mac or GNU/Linux computer. For example, if the StickWRLD folder is on the desktop, type cd ~/Desktop/StickWRLD/exec in the terminal.</li>
	<li>Launch StickWRLD by typing python-32 stickwrld_demo.py in the terminal.</li>
	<li>Verify that the StickWRLD Data Loader panel is visible on the screen (Figure 3).</li>
</ol>
4. Loading the Data
<ol>
	<li>Load the converted protein sequence alignment by pressing the &#8220;Load Protein&#8230;&#8221; button.</li>
	<li>Select the file created in step 3 above and press &#8220;Open&#8221;. StickWRLD will open several new windows, including &#8220;StickWRLD Control&#8221; (Figure 4) and &#8220;StickWRLD - OpenGL&#8221; (Figure 5).</li>
	<li>Select the &#8220;StickWRLD &#8211; OpenGL&#8221; window. Choose &#8220;Reset View&#8221; from the &#8220;OpenGL&#8221; menu to display the default StickWRLD visualization in a &#8220;top-down&#8221; view through the cylinder representing the data in the resizable OpenGL windows..</li>
</ol>
5. View Options
<ol>
	<li>Select the boxes for &#8220;Column Labels&#8221; and &#8220;Ball Labels&#8221; in the &#8220;StickWRLD Control&#8221; pane (Figure 4) to display values for columns and balls.</li>
	<li>Deselect the box for &#8220;Column Edges&#8221; in the &#8220;StickWRLD Control&#8221; pane to hide the column edge lines.</li>
	<li>Set the &#8220;Column Thickness&#8221; to 0.1 in the &#8220;StickWRLD Control&#8221; pane to draw a thin line through the columns, making it easier to navigate the 3D view. Press return to accept the change.</li>
	<li>Reset the view in the &#8220;StickWRLD &#8211; OpenGL&#8221; window as in step 5.3 above, then press the &#8220;full screen&#8221; button to maximize the view.</li>
</ol>
6. Navigation
<ol>
	<li>Rotate the 3D StickWRLD display by holding down the left mouse button while moving the mouse in any direction.</li>
	<li>Zoom the 3D StickWRLD display by holding down the right mouse button while moving the mouse up or down.</li>
</ol>
7. Finding Interpositional Dependencies (IPDs)
<ol>
	<li>Browse the view by panning and zooming as described in step 6. Coevolving residues exceeding the threshold requirements of both p and residual are connected via edge lines as seen in Figure 6. If there are too many or too few edges connecting residues, change the Residual threshold (on the &#8220;StickWRLD Control&#8221; pane) to show fewer, or more, edges.</li>
	<li>Increase the residual threshold on the StickWRLD Control Pane until no IPD edge lines are shown and slowly ramp down until relationships appear. Continue increasing the residual until you have a sufficient number of relationships to examine.</li>
	<li>Identify relationships that involve either residues of known interest (e.g., within a motif or binding/functional site) or residues that are distal to one another within the alignment (suggesting that they are proximal in the folded protein)</li>
</ol>
8. Selecting and Saving Findings
<ol>
	<li>Using command + left click on any edges of interest. The StickWRLD Control pane will indicate the columns and connect specific residues, e.g., &#8220;(124|G) (136|H)&#8221; (Figure 7). Solid lines represent positive associations; dashed lines represent negative associations.</li>
	<li>Press the &#8220;Output Edges&#8221; button on the &#8220;StickWRLD Control&#8221; panel to save a plain-text formatted file (edge_residual.csv) of all of the visible edges, including the joined residues and their actual residual values, in the /StickWRLD/exec/ directory.</li>
</ol>

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	<li>Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Addressing+the+unmet+need+for+visualizing+conditional+random+fields+in+biological+data.">Addressing the unmet need for visualizing conditional random fields in biological data.</a> BMC. 15, 202 (2014).</li><li>Sullivan, B. J., Durani, V., Magliery, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Triosephosphate+isomerase+by+consensus+design:+dramatic+differences+in+physical+properties+and+activity+of+related+variants.">Triosephosphate isomerase by consensus design: dramatic differences in physical properties and activity of related variants.</a> Journal of molecular biology. 413, 195-208 (2011).</li><li>Smith, M. A., Bedbrook, C. N., Wu, T., Arnold, F. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hypocrea+jecorina+cellobiohydrolase+I+stabilizing+mutations+identified+using+noncontiguous+recombination.">Hypocrea jecorina cellobiohydrolase I stabilizing mutations identified using noncontiguous recombination.</a> ACS synthetic biology. 2, 690-696 (2013).</li><li>Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+the+sequence+requirements+of+protein+families:+insights+from+the+BioVis+2013+contests.">Understanding the sequence requirements of protein families: insights from the BioVis 2013 contests.</a> BMC proceedings. 8, S1 (2014).</li><li>Eddy, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+a+hidden+Markov+model?.">What is a hidden Markov model?.</a> Nature biotechnology. 22, 1315-1316 (2004).</li><li>Ray, W. C., Ozer, H. G., Armbruster, D. W., Daniels, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+identity+-+when+classical+homology+searching+fails,+why,+and+what+you+can+do+about+it.">Beyond identity - when classical homology searching fails, why, and what you can do about it.</a> Proceedings of the 4th Ohio Collaborative Conference on Bioinformatics. , 51-56 (2009).</li><li>Ovchinnikov, S., Kamisetty, H., Baker, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+and+accurate+prediction+of+residue-residue+interactions+across+protein+interfaces+using+evolutionary+information.">Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information.</a> eLife. 3, e02030 (2014).</li><li>Trudeau, D. L., Lee, T. M., Arnold, F. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Engineered+thermostable+fungal+cellulases+exhibit+efficient+synergistic+cellulose+hydrolysis+at+elevated+temperatures.">Engineered thermostable fungal cellulases exhibit efficient synergistic cellulose hydrolysis at elevated temperatures.</a> Biotechnology and bioengineering. 111, 2390-2397 (2014).</li><li>Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAVL+and+StickWRLD:+visually+exploring+relationships+in+nucleic+acid+sequence+alignments.">MAVL and StickWRLD: visually exploring relationships in nucleic acid sequence alignments.</a> Nucleic acids research. 32, W59-W63 (2004).</li><li>. Python Language Reference v.2.7.6 Available from: <a target="_blank" href="https://www.python.org/download/releases/2.7.6/">https://www.python.org/download/releases/2.7.6/</a> (2014)</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PyOpenGL The Python OpenGL Binding. , (2014).</li><li>Larkin, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clustal+W+and+Clustal+X+version+2.0.">Clustal W and Clustal X version 2.0.</a> Bioinformatics. 23, 2947-2948 (2007).</li><li>Ozer, H. G., Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAVL/StickWRLD:+analyzing+structural+constraints+using+interpositional+dependencies+in+biomolecular+sequence+alignments.">MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.</a> Nucleic acids research. 34, W133-W136 (2006).</li><li>Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAVL/StickWRLD+for+protein:+visualizing+protein+sequence+families+to+detect+non-consensus+features.">MAVL/StickWRLD for protein: visualizing protein sequence families to detect non-consensus features.</a> Nucleic acids research. 33, W315-W319 (2005).</li><li>Ray, W. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Visual+Analytics+approach+to+identifying+protein+structural+constraints.">A Visual Analytics approach to identifying protein structural constraints.</a> IEEE. , 249-250 (2010).</li><li>Wheeler, T. J., Clements, J., Finn, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Skylign:+a+tool+for+creating+informative,+interactive+logos+representing+sequence+alignments+and+profile+hidden+Markov+models.">Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models.</a> BMC bioinformatics. 15, 7 (2014).</li><li>Perrier, V., Burlacu-Miron, S., Bourgeois, S., Surewicz, W. K., Gilles, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetically+engineered+zinc-chelating+adenylate+kinase+from+Escherichia+coli+with+enhanced+thermal+stability.">Genetically engineered zinc-chelating adenylate kinase from Escherichia coli with enhanced thermal stability.</a> The Journal of biological chemistry. 273, 19097-19101 (1998).</li></ol>

The authors declare that they have no competing financial interests.

StickWRLD has been successfully used to identify such IPDs in the Adenylate Kinase Lid domain16, as well as associated DNA bases in rho-dependent terminators9, and a novel splice-site specificity in Archaeal tRNA intron endonuclease6 target sites. These IPDs were not detectable via a direct examination of the alignments.
StickWRLD displays each position of an alignment as a column of 20 &#8220;spheres&#8221;, where each sphere represents one of the 20 amino acid residues and the size of the sphere indicates the frequency of occurrence of that particular residue within that column (Figure 4). Columns are arranged in a cylinder, with edge lines connecting residues in different columns (indicating an IPD). These edge lines are only drawn if the corresponding residues are covarying at a frequency surpassing both the p-value (significance) and residual (expected - observed) thresholds.
Detection of co-occurring interdependent residues, or IPDs, in distal regions of a DNA or protein sequence alignment is difficult using standard sequence alignment tools6. While such tools generate a consensus, or motif, sequence, this consensus is in many cases a simple majority-rule averaging and does not convey covariation relationships that may form one or more sub-motifs &#8211; groups of residues that tend to co-evolve. Even HMM models, which are capable of detecting neighboring dependencies, cannot accurately model sequence motifs with distal IPDs5. The result is that the calculated consensus may in fact be a &#8220;synthetic&#8221; sequence not found in nature &#8211; and engineered proteins based on such computational consensus may not, in fact, be optimal. In fact, the Pfam HMM for ADK would suggest that a chimeric protein containing half of the tetracysteine motif, and half of the H,S,D,T motif, is functionally just as acceptable as any actually existing ADK. This is not the case, as such chimeras (and many other blendings of these motifs) Are catalytically dead4,19.
When looking for correlations, it is critical that the residual threshold be adjusted to allow for the discovery of relevant correlations by setting the threshold above the level at which any edges are seen and then gradually ramping the threshold back down. This ensures that only the most significant edges are considered initially.
An alternate approach is to start with the residual threshold set extremely low. This results in the display of all significant edges. From here the residual threshold can slowly be increased, allowing edges to drop out until patterns emerge. While this approach is less useful when looking for the inclusion of specific nodes (e.g., application of domain knowledge), it allows for the discovery of unexpected relationships using StickWRLD as a visual analytical tool to discover emerging patterns in the data visualization.
StickWRLD is limited primarily by the available memory of the system on which it is run as well as the resolution of the display device. While there is no theoretical limit to the number of data points StickWRLD can examine, and sequences up to 20,000 positions have been tested, in practice StickWRLD performs best with sequences up to around 1,000 positions.
The primary advantage of StickWRLD lies in its ability to identify groups of residues that covary with one another. This is a significant advantage over the traditional approach of the statistical consensus sequence, which is a simple statistical averaging and does not take coevolution into account. While in some cases covarying residues may simply be an artifact of phylogeny, even these residues have withstood the &#8220;test of selection&#8221;, and as such are unlikely to detract from the functionality of any protein engineered to include them.
While using StickWRLD to identify IPDs in a canonical DNA or protein sequence consensus/motif prior to engineering synthetic variants will reduce the potential for error and support rapid optimization of function, it should be noted that StickWRLD can be used as a generalized correlation identification tool and is not limited exclusively to protein data. StickWRLD can be used to visually discover the co-occurance of any variables in any properly encoded data set.

Protein alignments have long been used to evaluate the similarity of residues in a protein family. Frequently the most interesting features of a protein (e.g., catalytic or other binding sites) are the result of protein folding bringing distal regions of the linear sequence into contact, and as a result these apparently unrelated regions in the alignment tend to evolve and change in a coordinated fashion. In other cases, the function of a protein can be dependent on its electrostatic signature, and mutations that affect the electronic dipole are compensated for by changes to distant charged residues. Allosteric effects can also induce long-range sequential and spatial dependencies between residue identities. Regardless of their origin, these functionally-required covariations of residues - inter-positional dependencies (IPDs) - may not be obvious with visual examination of the alignment (Figure 1). Identification of IPDs - as well as of which specific residues within those positions tend to covary as a unit - can reveal important clues about the processes of protein folding and the formation of functional sites. This information can then be used to optimize synthetic (engineered) proteins in terms of thermostability and activity. It has long been known that not all point mutations toward consensus provide improved stability or activity. More recently, proteins designed to take advantage of known IPDs in their sequence have been shown to result in greater activity than the same protein designed strictly from consensus1,2 (manuscript in preparation), similar to the idea of stabilizing point mutations3.
Unfortunately, traditional consensus-building models (e.g., majority rule) only capture IPDs by accident. Consensus and Position Specific Scoring Matrix methods are ignorant of IPDs and only &#39;correctly&#39; include them in models, when the dependent residues are also the most popular residues for those positions in the family. Markov Chain models can capture IPDs when they are sequentially proximal, but their typical implementation ignores everything except immediate sequential neighbors, and even at their best, Hidden Markov Model calculations (see Figure 2) become intractable when dependencies are separated in the sequence by more than a dozen or so positions4. Since these IPDs essentially form &#8220;sub-motifs&#8221; which cannot be predicted by simple &#8220;majority rule&#8221; or even HMM-based consensus models5,6 the result can be a biologically invalid &#8220;consensus&#8221; which is not only never seen in nature but is less viable than any extant protein. Systems based on Markov Random Fields, such as GREMLIN7, attempt to overcome these problems. Additionally while sophisticated biological/biochemical techniques such as noncontiguous recombination3,8 can be used to identify essential protein elements by region, they require considerable time and bench work for single-base-pair precision to be achieved.
StickWRLD9 is a Python based program that creates an interactive 3D representation of a protein alignment that makes IPDs clear and easy to understand. Each position in the alignment is represented as a column in the display, where each column is comprised of a stack of spheres, one for each of the 20 amino acids that could be present in that position within the alignment. The sphere size is dependent on the frequency of occurrence of the amino acid, such that the user can immediately glean the consensus residue or the relative distribution of amino acids within that position by simply looking at the size of the spheres. The columns representing each position are wrapped around a cylinder. This gives every sphere representing a possible amino acid at each position in the alignment, a clear &#39;line of sight&#39; to every other amino acid possibility at every other position. Prior to visualization, StickWRLD calculates the the correlation strength between all possible combinations of residues to identify the IPDs9. To represent IPDs, lines are drawn between residues which are coevolving at a higher, or lower than would be expected if the residues present in the positions were independent(IPDs).
Not only does this visualization show which sequence positions interact evolutionarily, but as the IPD edge lines are drawn between the amino acid spheres in each column, the user can quickly determine which specific amino acids tend to be coevolving at each position. The user has the ability to rotate and explore the visualized IPD structure, as well as dynamically change the statistical thresholds controlling the display of correlations, making StickWRLD a powerful discovery tool for IPDs.
Applications such as GREMLIN7 similarly display complex relational information between residues &#8211; but these relationships are computed via more traditional Markov models, which are not designed to determine any conditional relationships. As such, these are capable of being displayed as 2D projections. By contrast, StickWRLD can compute and display multi-node conditional dependencies, which may be obfuscated if rendered as a 2D graph (a phenomenon known as edge occlusion).
StickWRLD&#8217;s 3D view also has several other advantages. By allowing users to manipulate the visual &#8211; panning, rotating, and zooming &#8211; features that may be obfuscated or unintuitive in a 2D representation can be more easily seen in the 3D cylinder of StickWRLD. StickWRLD is essentially a visual analytics tool, harnessing the power of the human brain&#8217;s pattern recognition ability to see patterns and trends, and the ability to explore the data from various perspectives lends itself to this.

<table><tbody><tr><td>Mac or Ubuntu OS computer</td><td>Various</td><td></td><td>Any Mac or GNU/Linux (&lt;em&gt;e.g.,&lt;/em&gt; Ubuntu) computer capable of running Python &amp;amp; associated shell scripts</td></tr><tr><td>Python programming language</td><td>python.org</td><td></td><td>Python version 2.7.6 or greater recommended</td></tr><tr><td>wxPython library</td><td>wxpython.org</td><td></td><td>Latest version recommended</td></tr><tr><td>SciPy library</td><td>scipy.org</td><td></td><td>Latest version recommended</td></tr><tr><td>PyOpenGL library</td><td>pyopengl.sourceforge.net</td><td></td><td>Latest version recommended</td></tr><tr><td>StickWRLD Python scripts</td><td>NCH BCCM</td><td></td><td>Available from &lt;a href=&quot;http://www.stickwrld.org&quot; target=&quot;_blank&quot;&gt;http://www.stickwrld.org&lt;/a&gt;</td></tr><tr><td>fasta2stick.sh file converter</td><td>NCH BCCM</td><td></td><td>Available from &lt;a href=&quot;http://www.stickwrld.org&quot; target=&quot;_blank&quot;&gt;http://www.stickwrld.org&lt;/a&gt;</td></tr><tr><td>Protein and/or DNA sequence data</td><td></td><td></td><td>Samples available at &lt;a href=&quot;http://www.stickwrld.org&quot; target=&quot;_blank&quot;&gt;http://www.stickwrld.org&lt;/a&gt;</td></tr></tbody></table>

optimization of synthetic proteins: identification of interpositional dependencies indicating structurally and/or functionally linked residues

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional units (e.g., domains). Traditional consensus-building models fail to account for interpositional dependencies &#8211; functionally required covariation of residues that tend to appear simultaneously throughout evolution and across the phylogentic tree. These relationships can reveal important clues about the processes of protein folding, thermostability, and the formation of functional sites, which in turn can be used to inform the engineering of synthetic proteins. Unfortunately, these relationships essentially form sub-motifs which cannot be predicted by simple &#8220;majority rule&#8221; or even HMM-based consensus models, and the result can be a biologically invalid &#8220;consensus&#8221; which is not only never seen in nature but is less viable than any extant protein. We have developed a visual analytics tool, StickWRLD, which creates an interactive 3D representation of a protein alignment and clearly displays covarying residues. The user has the ability to pan and zoom, as well as dynamically change the statistical threshold underlying the identification of covariants. StickWRLD has previously been successfully used to identify functionally-required covarying residues in proteins such as Adenylate Kinase and in DNA sequences such as endonuclease target sites.

StickWRLD has been used previously to detect interpositional dependencies (IPDs) between residues in both DNA3 and protein15-17 alignments. These co-evolving residues, while often distal from one another in the sequence alignment, are often proximal to one another in the folded protein. StickWRLD allows rapid discovery of residue-specific co-occurrence at such sites, e.g., an alanine at position &#8220;x&#8221; is strongly correlated to a threonine at position &#8220;y&#8221;. Such correlations can be indicative of provable structural relationships, and typically are sites that, by necessity, co-evolve. StickWRLD is able to detect these relationships even when more &#8220;traditional&#8221; approaches using HMMs to describe motifs fail. For example, analysis of the PFAM alignment of the ADK lid domain using StickWRLD reveals a strong positive correlation between cysteines (C) at positions 4 and 8 and a coordinated pair of C at positions 35 and 38. At the same time, StickWRLD showed a similar strong positive relationship between histidine (H) and serine (S) at 4 and 8, with a strong negative relationships between these and the C quartet at 4, 8, 35, and 38, and a strong positive relationship with aspartic acid (D) and threonine (T) at positions 35 and 38 respectively. Additional IPDs exist between the H,S,D,T motif and a T and G at position **** 10 and 29 in b subtilis **** highlighting the conditional nature of these IPDs - the tetracysteine motif does not &#39;care&#39; about the identities at these two positions, while the hydrophilic H,S,D,T triad requires specific residues in these positions almost absolutely. These two completely different position-dependent residue motifs can fulfill the same role the ADK lid. As can be seen in Figure 6, a large cluster of IPDs, including a 3-node association between G (glycine) at position 132, Y (tyrosine) at position 135, and a P (proline) at position 141, is visible in the foreground (Figure 6A). In Figure 6B, the view has been skewed to position the user slightly above the cylinder, revealing an IPD between an H (histidine) at position 136 and an M (methionine) at position 29, 107 residues distant. A PFAM HMM-derived motif of the same domain (Figure 2), meanwhile, not only does not detect these as specifically co-occuring motif variants, but also defines the overall groupings in a biologically unsupported scheme16.
<img alt="Figure 1" src="/files/ftp_upload/52878/52878fig1highres.jpg" width="500" /> 
Figure 1. &#8220;Subway Map&#8221; representation of the B. subtilis Adenosine Kinase (ADK) Lid domain structure. Arrows indicate IPDs identified in the PFAM alignment of ADK Lid domain by StickWRLD. StickWRLD is able to correctly identify IPDs within a cluster of residues which are in close proximity in the folded protein. Of particular interest are the T and G pair at positions 9 and 29, which only form an IPD when the tetrad of residues at 4, 7, 24, and 27 is not C,C,C,C). Residue numbers displayed represents B. subtilis position and not PFAM alignment positions. <a href="https://www.jove.com/files/ftp_upload/52878/52878fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52878/52878fig2highres.jpg" width="700" /> 
Figure 2. Skylign18 Hidden Markov Model (HMM) Sequence Logo for the ADK lid domain. While HMMs are powerful tools for determining probabilities at each position as well as the contribution of each site to the overall model, the positional independence of HMMs makes them unsuitable for detecting IPDs. This model does not suggest any of the dependencies seen in the StickWRLD representations (Figure 6). <a href="https://www.jove.com/files/ftp_upload/52878/52878fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52878/52878fig3.jpg" /> 
Figure 3. The StickWRLD Data Loader. Users can choose from existing demo data or load their own data in the form of DNA or Protein sequence alignments.
<img alt="Figure 4" src="/files/ftp_upload/52878/52878fig4highres.jpg" width="600" /> 
Figure 4. The StickWRLD Control window. The Control pane allows the user to change various view properties as well as regulate the thresholds controlling the display of edge lines indicating relationships between residues (IPDs). Circled in red are the defaults that typically need to be adjusted for best viewing of any dataset. The Residual value sets the threshold of (observed-expected) for which connector/association lines are drawn. The controls for Column and Ball labels control whether or not the column position and residue values (e.g., &#8220;A&#8221; for arginine) are displayed. The Column Edge Line control toggles on and off the display of edge lines connecting columns &#8211; for dense data sets this is better turned off. The Column Thickness controls whether or not the column itself is displayed &#8211; setting this to a very small value (e.g., 0.1) will draw a line through the spheres in the column, making it easy to distinguish the columns from one another. <a href="https://www.jove.com/files/ftp_upload/52878/52878fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/52878/52878fig5highres.jpg" width="600" /> 
Figure 5. Initial view of the StickWRLD OpenGL window with the Adenylate Kinase lid domain protein data set loaded. The initial perspective looks &#8220;down&#8221; through the cylinder comprised of the sequence alignment positions. The user can rotate the cylinder using left-mouse-click-drag, and zoom in/out using right-mouse-click-drag. The initial view is quite dense because the default display shows even small rates of co-evolution. For many proteins, at this setting, distinct modules can be detected, but even in densely co-evolving proteins the display can be rapidly and interactively simplified to find the most important IPDs using the StickWRLD interface. <a href="https://www.jove.com/files/ftp_upload/52878/52878fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/52878/52878fig6highres.jpg" width="700" /> 
Figure 6. Closeup view of a StickWRLD visualization of the Adenylate Kinase lid domain protein. Here we have changed the default Residual to 0.2. This increases the threshold for display of inter-residue edges, showing fewer edges. The edges that remain indicate strongly associated IPDs. In addition the view has been rotated and zoomed to allow for easier viewing of the edges. (A) A large cluster of IPDs is visible in the foreground, including a 3-node association between G (glycine) at position 132, Y (tyrosine) at position 135, and a P (proline) at position 141. (B) The view has been skewed to position the user slightly above the cylinder, revealing an IPD between an H (histidine) at position 136 and an M (methionine) at position 29, 107 residues distant. <a href="https://www.jove.com/files/ftp_upload/52878/52878fig6highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/52878/52878fig7.jpg" /> 
Figure 7. StickWRLD Control window lower-right information view. CTRL+Left clicking on an object (e.g., sphere or edge) in the OpenGL window displays the information for the object in the lower right of the StickWLRD Control window. Here we see the information for an IPD edge between a methionine at position 29 and a histidine at position 136.

Watch this Scientific Journal Video about Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues at JoVE.com

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Synthetic protein sequences based on consensus motifs typically ignore co-evolving residues, that imply interpositional dependencies (IPDs). IPDs can be essential to activity, and designs that disregard them may result in suboptimal results. This protocol uses StickWRLD to identify IPDs and help inform rational protein design, resulting in more efficient results.

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional ...

optimization-synthetic-proteins-identification-interpositional

Research

JoVE Journal

Chemistry

7.2K Views.  The Research Institute at Nationwide Children's Hospital. Synthetic protein sequences based on consensus motifs typically ignore co-evolving residues, that imply interpositional dependencies (IPDs). IPDs can be essential to activity, and designs that disregard them may result in suboptimal results. This protocol uses StickWRLD to identify IPDs and help inform rational protein design, resulting in more efficient results.

Video: Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing metabolic reactions, DNA repair and replication. Therefore, a detailed understanding of these processes provides critical information on how cells function. Integrative structural MS methods offer structural and dynamical information on protein complex assembly, complex connectivity, subunit stoichiometry, protein oligomerization and ligand binding. Recent advances in integrative structural MS have allowed for the characterization of challenging biological systems including large DNA binding proteins and membrane proteins. This protocol describes how to integrate diverse MS data such as native MS and ion mobility-mass spectrometry (IM-MS) with molecular dynamics simulations to gain insights into a helicase-nuclease DNA repair protein complex. The resulting approach provides a framework for detailed studies of ligand binding to other protein complexes involved in important biological processes.

Mass spectrometry (MS) has emerged as an important tool for the investigation of structure and dynamics of macromolecular assemblies. Here, we integrate MS-based approaches to interrogate protein complex formation and ligand binding.

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing ...

Monitoring the Assembly of a Secreted Bacterial Virulence Factor Using Site-specific Crosslinking

This article describes a method to detect and analyze dynamic interactions between a protein of interest and other factors in vivo. Our method is based on the amber suppression technology that was originally developed by Peter Schultz and colleagues1. An amber mutation is first introduced at a specific codon of the gene encoding the protein of interest. The amber mutant is then expressed in E. coli together with genes encoding an amber suppressor tRNA and an amino acyl-tRNA synthetase derived from Methanococcus jannaschii. Using this system, the photo activatable amino acid analog p-benzoylphenylalanine (Bpa) is incorporated at the amber codon. Cells are then irradiated with ultraviolet light to covalently link the Bpa residue to proteins that are located within 3-8 &#197;. Photocrosslinking is performed in combination with pulse-chase labeling and immunoprecipitation of the protein of interest in order to monitor changes in protein-protein interactions that occur over a time scale of seconds to minutes. We optimized the procedure to study the assembly of a bacterial virulence factor that consists of two independent domains, a domain that is integrated into the outer membrane and a domain that is translocated into the extracellular space, but the method can be used to study many different assembly processes and biological pathways in both prokaryotic and eukaryotic cells. In principle interacting factors and even specific residues of interacting factors that bind to a protein of interest can be identified by mass spectrometry.

This article illustrates the use of pulse-chase radio labeling in combination with site-specific photocrosslinking to monitor interactions between a protein of interest and other factors in E. coli. Unlike traditional chemical cross-linking methods, this approach generates high resolution &#8220;snapshots&#8221; of an ordered assembly pathway in a living cell.

This article describes a method to detect and analyze dynamic interactions between a protein of interest and other factors in vivo. Our method is based on ...

Immunology and Infection

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the ...

Biochemistry

In Silico Structural Analysis of Protein Carbonylation by Lipid Derivatives

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing intracellular damage, impaired cell functions, and, ultimately, cell death. It has been described in aging and several age-related chronic conditions. However, the basis of structural changes related to the loss of function in protein targets is still not well understood. Hence, a route to the in silico construction of new parameters for amino acids carbonylated with reactive carbonyl species derived from fatty acid oxidation is described. The Michael adducts for Cys, His, and Lys with 4-hydroxy-2-nonenal (HNE), 4-hydroxy-2-hexenal (HHE), and a furan ring form for 4-Oxo-2-nonenal (ONE), were built, while malondialdehyde (MDA) was directly attached to each residue. The protocol describes details for the construction, geometry optimization, assignment of charges, missing bonds, angles, dihedral angles parameters, and its validation for each modified residue structure. As a result, structural effects induced by the carbonylation with these lipid derivatives have been measured by molecular dynamics simulations on different protein systems such as the thioredoxin enzyme, bovine serum albumin and the membrane Zu-5-ankyrin domain employing root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), structural secondary prediction (DSSP) and the solvent-accessible surface area analysis (SASA), among others.

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Here, we describe a protocol for the optimization and parameterization of amino acid residues modified with reactive carbonyl species, adaptable to protein systems. The protocol steps include structure design and optimization, charge assignments, parameter construction, and preparation of protein systems.

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

Biology

Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often detrimental to protein folding and, in particular, chromophore maturation in green fluorescent proteins and related variants. A reasonable alternative is to manipulate the translation of the protein so that all Pro residues are replaced residue-specifically by analogs, a method known as selective pressure incorporation (SPI). The built-in chemical modifications can be used as a kind of &#34;molecular surgery&#34; to finely dissect measurable changes or even rationally manipulate different protein properties. Here, the study demonstrates the usefulness of the SPI method to study the role of prolines in the organization of the typical &#946;-barrel structure of spectral variants of the green fluorescent protein (GFP) with 10-15 prolines in their sequence: enhanced green fluorescent protein (EGFP), NowGFP, and KillerOrange. Pro residues are present in connecting sections between individual &#946;-strands and constitute the closing lids of the barrel scaffold, thus being responsible for insulation of the chromophore from water, i.e., fluorescence properties. Selective pressure incorporation experiments with (4R)-fluoroproline (R-Flp), (4S)-fluoroproline (S-Flp), 4,4-difluoroproline (Dfp), and 3,4-dehydroproline (Dhp) were performed using a proline-auxotrophic E. coli strain as expression host. We found that fluorescent proteins with S-Flp and Dhp are active (i.e., fluorescent), while the other two analogs (Dfp and R-Flp) produced dysfunctional, misfolded proteins. Inspection of UV-Vis absorption and fluorescence emission profiles showed few characteristic alterations in the proteins containing Pro analogs. Examination of the folding kinetic profiles in EGFP variants showed an accelerated refolding process in the presence of S-Flp, while the process was similar to wild-type in the protein containing Dhp. This study showcases the capacity of the SPI method to produce subtle modifications of protein residues at an atomic level (&#34;molecular surgery&#34;), which can be adopted for the study of other proteins of interest. It illustrates the outcomes of proline replacements with close chemical analogs on the folding and spectroscopic properties in the class of &#946;-barrel fluorescent proteins.

To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often ...

Bioengineering

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism&#39;s survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide chain or string.
Various computational and biochemical methods are used to study protein interfaces. Laboratory methods, such as affinity purification, mass spectrometry, and protein microarrays, can be used to identify new interactions. Co-immunoprecipitation of proteins and yeast two-hybrid screening are widely used to provide evidence on whether two proteins interact in vitro.&#160; Computer programs can predict PPIs based on similar interactions found in other proteins by comparing protein sequences and three-dimensional structures. Other computational approaches, such as phylogenetic profiling, predict PPIs based on the coevolution of binding partners. Additionally, gene fusion analysis is used to predict interaction partners by finding protein pairs that are fused in the genome of other organisms.&#160;
Proteins typically interact with multiple partners either at the same or different times, and they may contain more than one interaction interface.&#160; Many proteins form large complexes that perform specific functions that can only be carried out by the complete complex. In some cases, these protein interactions are regulated; that is, a protein may interact with different partners based on cellular needs. Further computational and statistical analyses sort such interactions into networks, which are curated in online interactome databases. These searchable databases enable users to study specific protein interactions, as well as design drugs that can enhance or disrupt interactions at the interface.

Protein Complexes and Protein-Protein Interactions

Interfejsy białko-białko

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism&#39;s survival. Most PPIs ...

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Monitoring the Assembly of a Secreted Bacterial Virulence Factor Using Site-specific Crosslinking

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

A Protocol for Computer-Based Protein Structure and Function Prediction

Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

An Integrated Approach for Microprotein Identification and Sequence Analysis

Interfejsy białko-białko