Financial support by Sistema Nacional de Investigaci&#243;n (SNI) (grant numbers SNI-043-2023 and SNI-170-2021), Secretar&#237;a Nacional de Ciencia, Tecnolog&#237;a e Innovaci&#243;n (SENACYT) of Panama and Instituto para la Formaci&#243;n y Aprovechamiento de Recursos Humanos (IFARHU) are gratefully acknowledged. Authors would like to thank Dr. Miguel Rodr&#237;guez for carefully reviewing the manuscript.

1. Initial preparation of the protein-peptide interface
<ol>
	<li>Downloading the structure of the protein-protein complex
	<ol>
		<li>Navigate to the Protein Data Bank (PDB) homepage (https://www.rcsb.org/)&#160;and type the PDB ID for the structure of the protein-protein complex in the main search box (Figure 2A). The PDB ID for the structure of the IRF5 dimer, used as an example in this work, is 3DSH19.</li>
		<li>In the main page for the desired structure, click on Download Files (Figure 2B) and then on Biological Assembly 1 (PDB - gz) (Figure 2C). 
		NOTE: In the PDB database, structures of many protein complexes formed by identical monomers are represented as biological assemblies, in which only the structure of one monomer (asymmetric unit) is stored in the PDB file. The structure of the multimer, in this case, the IRF5 dimer, must be downloaded as the biological assembly containing two instances of the asymmetric unit. To facilitate the next steps of this protocol, the two monomers are first separated, and different chain IDs are assigned to them.</li>
		<li>Open the downloaded structure in UCSF Chimera20 and click on Tools &#62; Structure Editing &#62; Change Chain IDs. In this example, both chains in the biological assembly are named A. Rename the second chain (labeled #0.2) to B and click OK.</li>
		<li>Click on Favorites &#62; Model Panel and then select the model containing the two chains. Click on the Group/Ungroup button to separate each chain into a different model. Then, select the two models and click on the Copy/Combine button. Enter a new name for the combined model, check Close Source Models, and click OK.</li>
		<li>Click on Select &#62; Chain and confirm that each chain in the dimer is now identified by a different letter, namely, A and B.</li>
		<li>Use File &#62; Save PDB to save the edited structure to a different PDB file, which will be used in the next steps of the protocol (here, the name IRF5_dimer.pdb was used).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66314/66314fig02.jpg" /> 
Figure 2: The Protein Data Bank (PDB) page for the structure used as a representative example in this work. (A) Search box to introduce the PDB accession code of the target structure. (B) Menu to download the structure in several formats. (C) Options to download biological assemblies when the structure has been saved as an asymmetric unit (see step 1.1.2 for more details). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66314/66314fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Identifying the target segment in the ligand protein
	<ol>
		<li>Navigate to the BUDE Alanine Scan server (https://pragmaticproteindesign.bio.ed.ac.uk/balas/). Click on the Choose File button under Structure Upload and select the PDB file saved in step 1.1.6.</li>
		<li>On the next page, check that the structure was correctly loaded (Figure 3A) and enter a name for the job in the server (Figure 3B).</li>
		<li>Set the chains from the PDB that will be treated as receptor (A) and ligand (B) (Figure 3C). Then, click on the Start Scan button to submit the job.</li>
		<li>Once the job is finished, click on Show Results to open the results page (Figure 4). 
		NOTE: In the results page, residues from the ligand structure are colored according to their estimated change in free energy (&#916;&#916;G), and those with higher values are colored in red.</li>
		<li>From the residue list, select the stretch of residues predicted to better interact with the target binding surface. Ensure these residues cluster the higher values for the difference in free energy (&#916;&#916;G). In this example, the segment between residues Leu424 and Ser436 was selected (highlighted with a red box in the right panel of Figure 4).</li>
	</ol>
	</li>
	<li>Preparing the protein-peptide interface for sequence diversification
	<ol>
		<li>Open the PDB file saved in step 1.1.6 in Chimera and check that there are no missing atoms or bonds in the structure of target subunits.</li>
		<li>Delete all small molecules, ions, and solvents that were co-crystalized with the original structure. To do this, click on Select &#62; Residues and then select all molecules other than standard amino acids. Then, click on Actions &#62; Atoms/Bonds and Delete.</li>
		<li>Crop the ligand chain to the best-interacting segment chosen in step 1.2.5. To do this, click on Favorites and Sequence and then click on the chain considered as the ligand (B). In the Sequence panel, drag the mouse to select all residues except those between positions 424 and 436. To delete these residues, click on Actions &#62; Atoms/Bonds and Delete.</li>
		<li>Use File &#62; Save PDB to save the edited structure to a different PDB file, which is used in the next steps of the protocol (here, the name IRF5_interface.pdb was used).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66314/66314fig03.jpg" /> 
Figure 3: Selection of receptor and ligand in the BUDE Alanine Scan server. (A) Graphic representation of the protein-protein complex. (B) Text box to enter the name of the job in the server. (C) Panel to interactively select the chains that will be considered as receptor and ligand (see step 1.2 for more details). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66314/66314fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66314/66314fig04.jpg" /> 
Figure 4: Results page of the BUDE Alanine Scan server. The potential best-interacting segment in the ligand sequence is indicated with a red box. In the left panel, the residue with the higher predicted energy contribution (Leu433) is highlighted in green. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66314/66314fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Sequence diversification
NOTE: In the following steps, rosetta_main refers to the main Rosetta installation directory, which is typically located at /opt/rosetta_src_&#60;version&#62;_bundle/main/, where &#60;version&#62; indicates the installed Rosetta version. Also, it is assumed that Rosetta applications are accessible system-wide; if this is not the case, the full path to the executables has to be provided. When compiled from the source, these executables are located in the /rosetta_main/source/bin/ directory.
<ol>
	<li>Initial optimization of amino acid side chains
	<ol>
		<li>Copy the edited structure saved in step 1.3.4 to a Linux location accessible by the Rosetta applications.</li>
		<li>Use Rosetta&#39;s FixBB application to perform a repack of all the amino acid side chains of the base structure before sequence diversification. In this operation, the orientation of all amino acid side chains is optimized to minimize energy and improve the stability of the complex. To do this, run the following command: 
		<img alt="Equation 1" src="/files/ftp_upload/66314/66314eq01v2.jpg" style="margin: auto;" /> 
		NOTE: This command outputs a PDB file named after the original structure with an additional numerical suffix (IRF5_interface_0001.pdb in this example).</li>
		<li>To facilitate the next step of the protocol, rename the repacked PDB file with the _repack suffix using the following command: 
		mv IRF5_interface_0001.pdb IRF5_repack.pdb</li>
	</ol>
	</li>
	<li>Sequence diversification
	<ol>
		<li>Run Pepspec in design mode to perform the actual sequence diversification step using the following command: 
		<img alt="Equation 2" src="/files/ftp_upload/66314/66314eq02.jpg" style="margin: auto;" /> 
		The following are general options:
		<ul style="list-style-type: square;">
			<li>-s indicates the input file (the repacked PDB file generated in step 2.1.3).</li>
			<li>-o indicates the prefix to name output files.</li>
			<li>-&#160;database indicates the path to the main Rosetta 3 database.</li>
			<li>-ex1, -ex2, and extrachi_cutoff are rotamer library options (see Pepspec documentation for more details).</li>
			<li>-overwrite tells the application to overwrite possible pre-existing outputs generated by previous iterations.</li>
		</ul>
		The following are options pertaining to sequence diversification per se:
		<ul style="list-style-type: square;">
			<li>-pepspec:pep_chain indicates the PDB chains considered as ligand (&#39;b&#39; in this example).</li>
			<li>-pepspec:native_pep_anchor indicates the amino acid residue used as an anchor (in this example, the Leu residue at position 10 of the ligand peptide).</li>
			<li>-pepspec:n_peptides indicates the number of peptide structures to output.</li>
			<li>-pepspec:no_prepack_prot tells the application to skip repacking in the input base structure (since this was previously performed in step 2.1). 
			NOTE: The main Pepspec output is a directory containing the PDB files for peptides resulting from the design phase, named using the output prefix&#160;with the .pdbs suffix (IRF5.pdbs in the example). Additionally, Pepspec outputs all the accepted peptide sequences tested as part of the sequence diversification step and their corresponding Rosetta energy scores in a tab-delimited text file named after the output prefix, with the .spec suffix (IRF5.spec in the example). Since the protocol described in this work aims to estimate amino acid preferences rather than the actual peptide design, the next steps use IRF5.spec instead of the PDB structures in the .pdbs directory.</li>
		</ul>
		</li>
	</ol>
	</li>
</ol>
3. Estimation of amino acid preferences
<ol>
	<li>Computing a PWM
	<ol>
		<li>To generate a PWM, use the gen_pepspec_pwm.py script included in the Rosetta suite. To run this script, use the following command: 
		<img alt="Equation 3" src="/files/ftp_upload/66314/66314eq03v3.jpg" style="margin: auto;" /> 
		where:
		<ul style="list-style-type: square;">
			<li>IRF5.spec is the Pepspec output file generated in step 2.2.</li>
			<li>-1 indicates that there are not additional N-terminal residues in the sequence and, therefore, positions in the PWM are 1-based.</li>
			<li>0.2 tells the script to only consider the top 20% best-scoring peptides from the Pepspec output (the default value is 0.1, corresponding to the 10%)</li>
			<li>interface_score tells the script to rank peptides based on the interface score, which is one of the various Rosetta scores included in the Pepspec output file. 
			NOTE: This script generates two output files, one for the computed PWM (with the .pwm suffix) and the other for the sequences of the subset of peptides used to compute the PWM (with the .seq suffix). The names of these files also include the score and the fraction of peptides used for ranking. In this example, these files are respectively named IRF5_interface_score_0.2.pwm and IRF5_interface_score_0.2.seq.</li>
		</ul>
		</li>
	</ol>
	</li>
	<li>Generating a sequence logo
	<ol>
		<li>Navigate to the WebLogo server (https://weblogo.berkeley.edu/logo.cgi)21 and click on the&#160;Choose File button next to Upload Sequence Data. Upload the file with peptide sequences generated in step 3.1.1 (IRF5_interface_score_0.2.seq in this example).</li>
		<li>Choose the desired format and size of the logo according to the input length. The example uses PDF format and a size of 15 cm x 12 cm. Click on Create Logo.</li>
	</ol>
	</li>
</ol>

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

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</ol>

Authors have nothing to disclose.

The present article describes a protocol to estimate the amino acid preferences of potentially multispecific binding sites based on in silico sequence diversification. Few computational tools have been developed to estimate amino acid preferences of protein-peptide interfaces14,25,26. These tools have a predictive nature, but they differ in the computational algorithms used to perform their predictions and the corrections they implement to improve accuracy. In this work, we used the Pepspec application of the Rosetta molecular modeling suite14. While primarily oriented to peptide design, this application implements a sequence diversification algorithm that can be used to predict amino acid preferences. To the best of our knowledge, this tool is the only one currently available that provides a built-in script to compute a PWM directly from the sequence diversification results. It is important to remark that the protocol is focused on PPIs, therefore, the initial structure is expected to be a complex of two protein subunits. Before the actual sequence diversification step, the protein considered as the ligand is cropped to the segment expected to interact with the receptor protein, and it is further treated as a peptide. However, the protocol can also be applied to protein-peptide complexes, a scenario in which steps 1.1-1.3 may not be required. During the preparation step (section 1) it is also essential to correct wrongly formatted residues and heteroatoms, as well as to model segments of the complex structure relevant for the target binding site that could not be properly resolved. These corrections depend on the specific structure under study and were not required for the structure used as an example herein.
The most critical steps of this protocol are those performed with the Rosetta applications, which include an initial repack of side chains with FixBB (step 2.1) and the actual sequence diversification with Pepspec (step 2.2). This initial repack step, called pre-packing, is explicitly mentioned to be required by Pepspec authors14. Although it can be performed by Pepspec, the authors of this application highly recommend using the FixBB application, which was particularly designed to optimize side chain rotamers in fixed protein backbones. In the sequence diversification step, it is important to consider that the Pepspec application is oriented to peptide design. Consequently, it reports a few best-scoring peptide candidates by default. Since the goal of the protocol presented here is to generate a large number of putative peptide ligands rather than a few best-scoring candidates, we changed the &#34;-pepspec:n_peptides&#34; option from 8 (its default) to 200 (step 2.2.1). Using this setting, Pepspec predicted more than 20,000 peptides as potential ligands. This set of putative peptides provided a very broad view of the binding landscape of the receptor, which was then sampled for the top 20% of best-scoring peptides for actual estimation of amino acid preferences. If a lower number of peptides is passed to &#34;-pepspec:n_peptides&#34;, significantly fewer candidates will be accepted by Pepspec. Under this scenario, the sampling proposed in the protocol may capture many putative peptide ligands with suboptimal energy scores, potentially resulting in less robust estimations.
One of the main limitations of the protocol presented in this work is that it relies on the previous knowledge of the structure of the protein containing the binding surface. However, this structure does not necessarily have to be determined experimentally but can be modeled ab initio or by homology modeling14. In addition, it is also necessary to know the binding mode of at least one amino acid residue (anchor) of the peptide ligand. This anchor will be extended to a particular number of residues via specific anchor extension options from Pepspec to perform the sequence diversification. If the orientation of the entire ligand in the binding site is known, as is the case of the representative example of this study, options related to anchor extension should be left as default (no extension), although a residue from the peptide must still be specified as an anchor to guide the sequence diversification algorithm. The Pepspec application does not support de novo docking of a possible anchor residue, but it can use as input the output of other docking applications or a model of a homologous protein-peptide complex to perform anchor docking14; though these scenarios are beyond the scope of this article.
An important disadvantage of the suggested protocol is its inherent predictive nature, which is directly affected by the resolution and accuracy of the initial structure or model of the protein-protein complex. However, Pepspec authors have stated that the accuracy of this application&#160;was significantly improved by treating the input backbone coordinates as an ensemble of structures rather than using a single protein structure and applying background normalization when computing the PWM14. Furthermore, the protocol is an alternative to the cumbersome and costly experimental methods for the estimation of amino acid preferences. All of these experimental methods rely on evaluating large sequence libraries obtained by introducing mutations to the sequence of protein ligands, followed by experimental evaluation of the impact of such mutations (see Bratulic and Badran12 for a review). Computational protocols such as the one proposed in this work allow the screening of thousands of putative peptide ligands in a very efficient manner, potentially providing a more robust set for the estimation of amino acid preferences13,14,25. Our proposed protocol can be applied to any PPI that could be reduced to a protein-peptide interface. Additionally, this protocol may serve as an initial strategy to identify mediators of PPIs, such as potential activators or inhibitors. The identified mediators can be further used to study these PPIs in the laboratory, or they can be evaluated as potential therapeutic agents.

Interaction between two proteins often involves the binding of short segments of amino acids to peptide-binding domains, resembling protein-peptide interfaces. Receptor proteins involved in such protein-protein interactions (PPI) often have the ability to recognize a certain set of overlapping but divergent ligand sequences, a property known as multispecificity1,2. Multispecific recognition is a feature of many cellular proteins, but it is particularly remarkable in enzymes and cell signaling proteins3. Proteins interacting with multispecific binding sites often have a combination of more and less conserved regions in their sequence4,5,6. In this scenario, the more conserved sequence motifs are involved in stringent molecular interactions. Conversely, the more variable sequences interact with somehow permissive surfaces in the receptor binding site. Usually, these less conserved but still functionally relevant segments are loops lacking defined secondary structure patterns or have even more dynamic conformations, such as those typical of intrinsically disordered proteins7.
Identification of potential peptide ligands of binding sites is usually the first step in the design of mediators able to interfere with the corresponding PPIs8. However, it is often unlikely to find a single most frequent amino acid residue at most sequence positions in ligands of multispecific binding sites. Instead, these sites may have particular preferences for a specific class of amino acids according to their chemical properties, e.g., acidic and negatively charged amino acids such as aspartate or glutamate, bulky aromatic amino acids such as phenylalanine or more hydrophobic residues such as aliphatic amino acids alanine, valine, leucine or isoleucine3. Several experimental methods can provide insights about amino acid preferences of protein binding sites, including directed evolution9, multi-codon scanning mutagenesis10, and deep mutational scanning11. All of these methods follow the approach of sequence diversification, which is based on introducing mutations to original ligands and further analyzing their effect on the function of the receptor protein (see Bratulic and Badran12 for a comprehensive review). However, these methods often require the survey of large sequence libraries, which makes them more cumbersome, costly, and time-consuming.
Computational methods to infer the amino acid preferences of multispecific binding sites have the potential to circumvent the limitations of wet lab methods. Among these, the in silico sequence diversification approach evaluates the energetic impact of a wide range of amino acid replacements in the ligand sequence as a way to characterize the structural plasticity of the PPI13. This method begins with the structure or model of the peptide ligand bound to the receptor binding site and subsequently introduces mutations to the ligand sequence. Statistical and energy-scoring functions are then used to evaluate the impact of these mutations on stability and binding affinity. The set of best-scoring ligand sequences resulting from the evaluation phase can then be used to compute the amino acid preferences. This strategy has the potential to process a very high number of ligand sequences in an efficient manner. Therefore, it can provide a more complete and consistent inference of amino acid preferences compared to those computed from the more limited number of sequences that can usually be processed in wet lab approaches.
The Pepspec application of the Rosetta molecular modeling suite14 is a tool that performs sequence diversification as a key step of its peptide design mode. This application requires a structure or model of the receptor protein with a bound peptide down to a single amino acid residue in length, which is used as an anchor for the next steps. The sequence of the bound peptide is then extended (if necessary) and diversified to generate a large number of putative peptide ligands. The binding affinity of these peptides is then evaluated by flexible-backbone peptide docking in order to select those with the best predicted binding scores. Although the main output of this application is the best peptide candidates selected at the end of the design phase, the much larger set of peptides accepted during this phase can also be used to compute the amino acid preferences of the target binding site. Amino acid preferences are computed as the frequency of each amino acid residue per position of the ligand sequence represented either as a position weight matrix (PWM) or as a more visual sequence logo.
In this article, we describe a protocol to estimate the amino acid preferences of the binding surface of a receptor protein involved in a PPI. The protocol is focused on PPIs in which a linear segment of the protein-ligand is known to bind to the receptor protein, so the scenario can be modeled as a protein-peptide interface. In this scenario, conserved motifs from the ligand typically interact with defined pockets in the receptor binding site, although the entire ligand segment involved in the PPI may contain less conserved regions. A flowchart summarizing the major steps of the protocol is shown in Figure 1. The protocol starts with the 3D structure of the protein-protein complex and further reduces the ligand protein to the potential best-interacting segment, leaving the receptor protein intact. The best-interacting segment is inferred by using the BUDE Alanine Scan server15, which conducts computational alanine scanning mutagenesis to identify hot-spot residues between the two interacting proteins. In this approach, residues from the ligand are individually replaced by alanine, and the estimated change in free energy or stability of the complex (&#916;&#916;G) is then used to infer the relevance of the corresponding residue for the target PPI. Once the best-interacting segment is inferred, its complex with the receptor protein is used as the base structure submitted to Pepspec to perform sequence diversification.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66314/66314fig01.jpg" /> 
Figure 1: Overview of the main steps of the protocol proposed in this work. Numbers match step numbers in the protocol section. Figures were made with the protein-protein complex used as the example described in the text. In this complex, the protein chain considered as the receptor is shown in pink, while the chain considered as the ligand is shown in light blue with its predicted best-interacting segment highlighted in red. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66314/66314fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
One of the limitations of the suggested protocol is the requirement for a resolved structure of the protein-peptide interface. The protocol may alternatively begin with a model of the target protein-peptide interface, although the specific modeling steps are not described herein. Moreover, although the protocol can be conducted on a personal computer running any operating system, a Linux environment is required for the steps involving the Rosetta applications. A computer cluster is also highly recommended for the sequence diversification step due to the large number of iterations typically performed by Pepspec.
Application of the suggested protocol is illustrated with the estimation of amino acid preferences of the biding surface of IRF5, a member of the human interferon regulatory factor (IRF) family. We chose this protein as an example because, during its activation, two subunits bind to form a dimer whose structure is well characterized16. In IRF dimers, binding can be modeled as a protein-peptide interface in which one subunit provides the binding surface and the other one interacts through a region containing a short conserved motif called pLxIS17,18. In addition, binding to IRF subunits is multispecific; therefore, they can form homodimers, heterodimers, and complexes with other cellular proteins known as coactivators18.

<table><tbody><tr><td>BUDE Alanine Scan Server</td><td>University of Edinburgh</td><td>https://pragmaticproteindesign.bio.ed.ac.uk/balas/</td><td>doi: 10.1021/acschembio.9b00560</td></tr><tr><td>Rosetta Modeling Software</td><td>Rosetta Commons</td><td>https://www.rosettacommons.org/software</td><td>doi: 10.1002/prot.22851</td></tr><tr><td>UCSF Chimera</td><td>University of California San Francisco</td><td>https://www.cgl.ucsf.edu/chimera/</td><td>doi: 10.1002/jcc.20084</td></tr></tbody></table>

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.

In this article, we described a protocol to predict the amino acid preferences of the binding surface of IRF5, a member of a family of transcription factors known as human interferon regulatory factors. These proteins are regulators of innate and adaptive immune responses and participate in the differentiation and activation of several immune cells. IRF subunits have highly plastic and multispecific binding surfaces, being capable of forming homodimers, heterodimers, and complexes with other cellular proteins17,18. Dimerization is thought to be the first step in the activation of these factors, and in most family members, it is triggered by the phosphorylation of multiple serine/threonine residues18. During dimerization, each monomer interacts with the biding surface of the other monomer via a highly conserved motif called pLxIS, located towards the C-terminal region of their sequence. The pLxIS abbreviation partially represents the amino acid preferences of the binding surface, which sequentially recognizes a polar amino acid (&#39;p&#39;), followed by two positions with a high frequency of leucine (&#39;L&#39;) and isoleucine (&#39;I&#39;), separated by a position occupied by any amino acid (&#39;x&#39;) and followed by a phosphorylatable serine residue (Ser436 in this example). Phosphorylation of several serine residues, including that of the pLxIS motif, promotes the bending of the C-terminal segment of one monomer and its interaction with the binding surface of the other monomer19,22.
The protocol described here started with a 3D structure of the IRF5 dimer19, in which one of the monomers was arbitrarily considered as the receptor in the PPI, while the other one was considered as the ligand containing the pLxIS motif. To better define the segment of the ligand interacting with the receptor binding site, we conducted computational alanine scanning mutagenesis (step 1.2). The predicted segment was comprised of 13 amino acid residues from positions 424 to 436, with the pLxIS motif starting at Arg432. The structure of the original dimer was then reduced to a peptide-protein complex in which the sequence of the monomer considered as ligand was cropped to the predicted best-interacting segment, whereas the other monomer was left intact (step 1.3). This structure was then used as input for the sequence diversification strategy (section 2), designating the leucine residue of the pLxIS motif (Leu433) as the anchor required by Pepspec. This process resulted in over 26,000 potential peptide ligands. The top 20% potential ligands with the best energy scores (5,280) were used to estimate the amino acid preferences of the binding surface in the form of a PWM (Figure 5A) and a sequence logo (Figure 5B) (section 3).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66314/66314fig05.jpg" /> 
Figure 5: Amino acid preferences of the binding surface of IRF3. (A)&#160;PWM indicating the frequency of each amino acid residue (rows) per position in the peptide ligand sequence (columns). (B) Sequence logo visually representing the corresponding amino acid frequencies. Positions of the original IRF5 sequence are shown in parenthesis below each column of the sequence logo. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66314/66314fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In the PWM, each row corresponds to a specific amino acid residue, while each column represents a position in the sequence. Each cell of the matrix contains the relative frequency of each amino acid at that position, weighted by the overall background frequencies. Sequence logos are constructed by stacking the letters of amino acids so that the total height of the stack at each position indicates the conservation of the overall sequence at that position. Conversely, the height of the individual letters within the stack indicates the frequency of the corresponding amino acid. In this example, both the PWM and the sequence logo are consistent with the previous knowledge regarding the binding surface of IRF5, with a higher preference for a polar amino acid (glutamate) at position 432 (&#39;p&#39;) and a very high preference for leucine and isoleucine at positions 433 and 435, respectively. Remarkably, positions 427, 429, and 436 were all predicted to have higher conservation for aspartate&#160;despite being occupied by serine in the original IRF5 sequence. This finding evidences the importance of phosphorylation of these positions for the formation of the IRF5 dimer since the negative charge in the side chains of&#160;aspartate and glutamate resembles that of phosphoserine. In fact, a previous study reported that a decoy peptide called IRF5D, in which these serine residues were replaced by aspartate, was able to inhibit IRF5 activity23. Conversely, position 425 was predicted to have a very high preference for serine, suggesting that the serine residue in this position may participate in the PPI in its unphosphorylated form. Indeed, it has been previously reported for other IRFs that phosphorylation of the equivalent serine residue negatively affects dimerization and binding to other coactivators16,24.

Watch this Scientific Journal Video about Author Spotlight: A computational approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions at JoVE.com

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.

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 ...

computational-prediction-amino-acid-preferences-potentially

Nanyang Technological University

Research

JoVE Journal

Biochemistry

1.7K Views.  Universidad Tecnológica de Panamá (UTP). We stellen een protocol voor voor de computationele voorspelling van aminozuurvoorkeuren in meer specifieke eiwit-eiwit interacties. Dit protocol kan worden beschouwd als een eerste stap in het ontwerpen van bemiddelaars van deze interacties. We zijn geïnteresseerd in het gebruik van deze mediatoren als potentiële remmers van specifieke interacties, in immunologisch technoloog.Onze implementator die wordt gebruikt voor het karakteriseren van aminozuu...

Video: Auteur in de schijnwerpers: een computationele benadering om aminozuurvoorkeuren te ontcijferen in multispecifieke eiwit-eiwitinteracties

Fluorescence Anisotropy as a Tool to Study Protein-protein Interactions

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is necessary to characterize it at the structural and mechanistic level. Several biochemical and biophysical methods exist for such purpose. Among them, fluorescence anisotropy is a powerful technique particularly used when the fluorescence intensity of a fluorophore-labeled protein remains constant upon protein-protein interaction. In this technique, a fluorophore-labeled protein is excited with vertically polarized light of an appropriate wavelength that selectively excites a subset of the fluorophores according to their relative orientation with the incoming beam. The resulting emission also has a directionality whose relationship in the vertical and horizontal planes defines anisotropy (r) as follows: r=(IVV-IVH)/(IVV+2IVH), where IVV and IVH are the fluorescence intensities of the vertical and horizontal components, respectively. Fluorescence anisotropy is sensitive to the rotational diffusion of a fluorophore, namely the apparent molecular size of a fluorophore attached to a protein, which is altered upon protein-protein interaction. In the present text, the use of fluorescence anisotropy as a tool to study protein-protein interactions was exemplified to address the binding between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor like-1 GTPase (EFL1). Conventionally, labeling of a protein with a fluorophore is carried out on the thiol groups (cysteine) or in the amino groups (the N-terminal amine or lysine) of the protein. However, SBDS possesses several cysteines and lysines that did not allow site directed labeling of it. As an alternative technique, the dye 4&#39;,5&#39;-bis(1,3,2 dithioarsolan-2-yl) fluorescein was used to specifically label a tetracysteine motif, Cys-Cys-Pro-Gly-Cys-Cys, genetically engineered in the C-terminus of the recombinant SBDS protein. Fitting of the experimental data provided quantitative and mechanistic information on the binding mode between these proteins.

Protein interactions are at the heart of a cell&#39;s function. Calorimetric and spectroscopic techniques are commonly used to characterize them. Here we describe fluorescence anisotropy as a tool to study the interaction between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor-like 1 GTPase (EFL1).

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is ...

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating these studies, reincorporation of detergent-solubilized membrane proteins into liposomes is a stochastic process where protein topology is impossible to enforce. This paper offers an alternative method to these challenging techniques that utilizes a liposome-based scaffold. Protein solubility is enhanced by deletion of the transmembrane domain, and these amino acids are replaced with a tethering moiety, such as a His-tag. This tether interacts with an anchoring group (Ni2+ coordinated by nitrilotriacetic acid (NTA(Ni2+)) for His-tagged proteins), which enforces a uniform protein topology at the surface of the liposome. An example is presented wherein the interaction between Dynamin-related protein 1 (Drp1) with an integral membrane protein, Mitochondrial Fission Factor (Mff), was investigated using this scaffold liposome method. In this work, we have demonstrated the ability of Mff to efficiently recruit soluble Drp1 to the surface of liposomes, which stimulated its GTPase activity. Moreover, Drp1 was able to tubulate the Mff-decorated lipid template in the presence of specific lipids. This example demonstrates the effectiveness of scaffold liposomes using structural and functional assays and highlights the role of Mff in regulating Drp1 activity.

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

This paper describes a method for assessing the interactions and assemblies of integral membrane proteins in vitro with various partner factors in a lipid-proximal environment.

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating ...

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.

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 ...

Chemistry

Peptide-based Identification of Functional Motifs and their Binding Partners

Specific short peptides derived from motifs found in full-length proteins, in our case HIV-1 Nef, not only retain their biological function, but can also competitively inhibit the function of the full-length protein. A set of 20 Nef scanning peptides, 20 amino acids in length with each overlapping 10 amino acids of its neighbor, were used to identify motifs in Nef responsible for its induction of apoptosis. Peptides containing these apoptotic motifs induced apoptosis at levels comparable to the full-length Nef protein. A second peptide, derived from the Secretion Modification Region (SMR) of Nef, retained the ability to interact with cellular proteins involved in Nef's secretion in exosomes (exNef). This SMRwt peptide was used as the "bait" protein in co-immunoprecipitation experiments to isolate cellular proteins that bind specifically to Nef's SMR motif. Protein transfection and antibody inhibition was used to physically disrupt the interaction between Nef and mortalin, one of the isolated SMR-binding proteins, and the effect was measured with a fluorescent-based exNef secretion assay. The SMRwt peptide's ability to outcompete full-length Nef for cellular proteins that bind the SMR motif, make it the first inhibitor of exNef secretion. Thus, by employing the techniques described here, which utilize the unique properties of specific short peptides derived from motifs found in full-length proteins, one may accelerate the identification of functional motifs in proteins and the development of peptide-based inhibitors of pathogenic functions.

Techniques to dissect the mechanisms underlying the secretion of HIV-1 Nef in exosomes are described. Specific short peptides derived from Nef and protein transfection were exploited to determine the structure, function, and binding partners of Nef&#x2019;s Secretion Modification Region. These procedures have general relevance in many mechanistic studies.

Specific short  peptides derived from motifs found in full-length proteins, in our case HIV-1  Nef, not only retain their biological function, but can ...

Immunology and Infection

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

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 ...

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may result in disease, making protein interactions important drug targets. It is thus highly important to understand these interactions at the molecular level. Protein interactions are studied using a variety of techniques ranging from cellular and biochemical assays to quantitative biophysical assays, and these may be performed either with full-length proteins, with protein domains or with peptides. Peptides serve as excellent tools to study protein interactions since peptides can be easily synthesized and allow the focusing on specific interaction sites. Peptide arrays enable the identification of the interaction sites between two proteins as well as screening for peptides that bind the target protein for therapeutic purposes. They also allow high throughput SAR studies. For identification of binding sites, a typical peptide array usually contains partly overlapping 10-20 residues peptides derived from the full sequences of one or more partner proteins of the desired target protein. Screening the array for binding the target protein reveals the binding peptides, corresponding to the binding sites in the partner proteins, in an easy and fast method using only small amount of protein.
In this article we describe a protocol for screening peptide arrays for mapping the interaction sites between a target protein and its partners. The peptide array is designed based on the sequences of the partner proteins taking into account their secondary structures. The arrays used in this protocol were Celluspots arrays prepared by INTAVIS Bioanalytical Instruments. The array is blocked to prevent unspecific binding and then incubated with the studied protein. Detection using an antibody reveals the binding peptides corresponding to the specific interaction sites between the proteins.

Peptide array screening is a high throughput assay for identifying protein-protein interaction sites. This allows mapping multiple interactions of a target protein and can serve as a method for identifying sites for inhibitors that target a protein. Here we describe a protocol for screening and analyzing peptide arrays.

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may ...

Validating Xanthatin-Keap1 Interaction with Docking and CETSA

Protein Target Prediction and Validation of Small Molecule Compound

Proteins are fundamental to human physiology, with their targets being crucial in research and drug development. The identification and validation of crucial protein targets have become integral to drug development. Molecular docking is a computational tool widely utilized to investigate protein-ligand binding, especially in the context of drug and protein target interactions. For the experimental verification of the binding and to access the binding of the drug and its target directly, the cellular thermal shift assay (CETSA) method is used. This study aimed to integrate molecular docking with CETSA to predict and validate interactions between drugs and vital protein targets. Specifically, we predicted the interaction between xanthatin and Keap1 protein as well as its binding mode through molecular docking analysis, followed by verification of the interaction using the CETSA assay. Our results demonstrated that xanthatin could establish hydrogen bonds with specific amino acid residues of Keap1 protein and reduce the thermostability of Keap1 protein, indicating that xanthatin could directly interact with Keap1 protein.

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

The experiment used here shows a method of molecular docking combined with cellular thermal shift assay to predict and validate the interaction between small molecules and protein targets.

Proteins are fundamental to human physiology, with their targets being crucial in research and drug development. The identification and validation of ...

Medicine

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

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

Education

JoVE Core

Molecular Biology

Unit 1

Protein Function

KEY TERMS AND CONCEPTS

Conserved Binding Sites

Many proteins&#8217; biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein&#8217;s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the interaction energy of different amino acid residues with the ligand and predicts the ones where binding energy is at a minimum to be potential binding sites. However, examining conserved sequences is often used in conjunction with other methodologies to enhance this prediction further. Structurally conserved residues can be used to distinguish between binding sites and exposed protein surfaces. The amino acids, Trp, Phe, and Met, are highly conserved in binding sites, and no such conservation is observed in the case of exposed protein surfaces.
Various computational tools can predict binding sites using a mix of structural, energetic, and conserved binding site methodologies.&#160; ConCavity is a tool that can be used to predict 3D ligand-binding pockets and individual ligand-binding residues. The algorithm used directly integrates evolutionary sequence conservation estimates with structure-based prediction. Another tool, MONKEY, is used to identify conserved transcription-factor binding sites in multispecies alignments. It employs factor specificity and binding-site evolution models to compute the likelihood that putative sites are conserved and assign statistical significance to each prediction.

Conserved Binding Sites: Indentification using ConCavity and MONKEY

Many proteins&#8217; biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein ...

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

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