computational alanine scanning mutagenesis of protein-peptide interface 

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

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/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.

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

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

Computational Alanine Scanning Mutagenesis of Protein-Peptide Interface 

Begin by downloading the structure of the protein-protein complex. For this, navigate to the protein data bank homepage and enter the PDB ID in the main search box. On the main page for the structure, click on download files, and then on biological assembly 1 to download the files in PDB-gz format.Open the downloaded structure in UCSF Chimera. Navigate to tools, then structure/editing, and click on change chain IDs. Rename the second chain, initially labeled as A to B.Then, click on favorites, followed by model panel.Select the model with the two chains, and click on the group/ungroup button to separate each chain into a different model. Next, 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 okay.Click on select, then chain, and confirm that the chains in the dimer are now identified as A and B.Click on file, and save PDB to save the edited structure as a new PDB file. For identifying the target segment in the ligand protein, navigate to the BUDE Alanine Scan server. Click on the choose file button under structure, upload, and upload the saved PDB file.On the next page, check that the structure was correctly loaded and enter a name for the job in the server. Set chains A as a receptor, and B as the ligand, and click on the start scan button to submit the job. Once the job is finished, click on show results to open the results page.From the residue list, select the stretch of residues predicted to better interact with the target binding surface. Using this protocol, an amino acid segment interacting with IRF5 binding surface was predicted. Using computational alanine scanning mutagenesis, a 13 amino acid segment was predicted from positions 424 to 436, with the PLXIS motif starting at arginine 432.

Research

JoVE Journal

Biochemistry

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

Sequence Diversification of Protein-Peptide Interface with Pepspec