Predicting peptide-mediated interactions on a genome-wide scale.

We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI's coverage of the human protein interactome.

Protein-protein interaction detection in vitro and in cells by proximity biotinylation.

We report a new method for detection of protein-protein interactions in vitro and in cells. One protein partner is fused to Escherichia coli biotin ligase (BirA), while the other protein partner is fused to BirA's "acceptor peptide" (AP) substrate. If the two proteins interact, BirA will catalyze site-specific biotinylation of AP, which can be detected by streptavidin staining. To minimize nonspecific signals, we engineered the AP sequence to reduce its intrinsic affinity for BirA. The rapamycin-controlled interaction between FKBP and FRB proteins could be detected in vitro and in cells with a signal to background ratio as high as 28. We also extended the method to imaging of the phosphorylation-dependent interaction between Cdc25C phosphatase and 14-3-3epsilon phosphoserine/threonine binding protein. Protein-protein interaction detection by proximity biotinylation has the advantages of low background, high sensitivity, small AP tag size, and good spatial resolution in cells.

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Potent and specific peptide inhibitors of human pro-survival protein Bcl-xL.

The Bcl-2 family of proteins plays a critical role regulating apoptosis, and pro-survival Bcl-2 family members are important therapeutic targets due to their overexpression in different cancers. Pro-apoptotic Bcl-2 homology 3 (BH3)-only proteins antagonize pro-survival Bcl-2 protein functions by binding directly to them, and a sub-class of BH3-only proteins termed sensitizers can initiate apoptosis via this mechanism in response to diverse signals. The five pro-survival proteins Bcl-xL, Mcl-1, Bcl-2, Bcl-w and Bfl-1 differ in their binding preferences, with Bcl-xL, Bcl-2 and Bcl-w sharing similar interaction profiles for many natural sensitizers and small molecules. Peptides that bind selectively to just one or a subset of family members have shown utility in assays that diagnose apoptotic blockades in cancer cells and as reagents for dissecting apoptotic mechanism. Combining computational design, combinatorial library screening and rational mutagenesis, we designed a series of BH3 sensitizer peptides that bind Bcl-xL with sub-nanomolar affinity and selectivity up to 1000-fold over each of the four competing pro-survival proteins. We demonstrate the efficacy of our designed BH3 peptides in assays that differentiate between cancer cells that are dependent on different pro-survival proteins.

Template-based prediction of protein function.

We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.

Peptide ligands for pro-survival protein Bfl-1 from computationally guided library screening.

Pro-survival members of the Bcl-2 protein family inhibit cell death by binding short helical BH3 motifs in pro-apoptotic proteins. Mammalian pro-survival proteins Bcl-x(L), Bcl-2, Bcl-w, Mcl-1, and Bfl-1 bind with varying affinities and specificities to native BH3 motifs, engineered peptides, and small molecules. Biophysical studies have determined interaction patterns for these proteins, particularly for the most-studied family members Bcl-x(L) and Mcl-1. Bfl-1 is a pro-survival protein implicated in preventing apoptosis in leukemia, lymphoma, and melanoma. Although Bfl-1 is a promising therapeutic target, relatively little is known about its binding preferences. We explored the binding of Bfl-1 to BH3-like peptides by screening a peptide library that was designed to sample a high degree of relevant sequence diversity. Screening using yeast-surface display led to several novel high-affinity Bfl-1 binders and to thousands of putative binders identified through deep sequencing. Further screening for specificity led to identification of a peptide that bound to Bfl-1 with K(d) < 1 nM and very slow dissociation from Bfl-1 compared to other pro-survival Bcl-2 family members. A point mutation in this sequence gave a peptide with ~50 nM affinity for Bfl-1 that was selective for Bfl-1 in equilibrium binding assays. Analysis of engineered Bfl-1 binders deepens our understanding of how the binding profiles of pro-survival proteins differ and may guide the development of targeted Bfl-1 inhibitors.

Structure-based redesign of the binding specificity of anti-apoptotic Bcl-x(L).

Many native proteins are multi-specific and interact with numerous partners, which can confound analysis of their functions. Protein design provides a potential route to generating synthetic variants of native proteins with more selective binding profiles. Redesigned proteins could be used as research tools, diagnostics or therapeutics. In this work, we used a library screening approach to reengineer the multi-specific anti-apoptotic protein Bcl-x(L) to remove its interactions with many of its binding partners, making it a high-affinity and selective binder of the BH3 region of pro-apoptotic protein Bad. To overcome the enormity of the potential Bcl-x(L) sequence space, we developed and applied a computational/experimental framework that used protein structure information to generate focused combinatorial libraries. Sequence features were identified using structure-based modeling, and an optimization algorithm based on integer programming was used to select degenerate codons that maximally covered these features. A constraint on library size was used to ensure thorough sampling. Using yeast surface display to screen a designed library of Bcl-x(L) variants, we successfully identified a protein with ~1000-fold improvement in binding specificity for the BH3 region of Bad over the BH3 region of Bim. Although negative design was targeted only against the BH3 region of Bim, the best redesigned protein was globally specific against binding to 10 other peptides corresponding to native BH3 motifs. Our design framework demonstrates an efficient route to highly specific protein binders and may readily be adapted for application to other design problems.

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods.

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.

Design of peptide inhibitors that bind the bZIP domain of Epstein-Barr virus protein BZLF1.

Designing proteins or peptides that bind native protein targets can aid the development of novel reagents and/or therapeutics. Rational design also tests our understanding of the principles underlying protein recognition. This article describes several strategies used to design peptides that bind to the basic region leucine zipper (bZIP) domain of the viral transcription factor BZLF1, which is encoded by the Epstein-Barr virus. BZLF1 regulates the transition of the Epstein-Barr virus from a latent state to a lytic state. It shares some properties in common with the more studied human bZIP transcription factors, but also includes novel structural elements that pose interesting challenges to inhibitor design. In designing peptides that bind to BZLF1 by forming a coiled-coil structure, we considered both affinity for BZLF1 and undesired self-association, which can weaken the effectiveness of an inhibitor. Several designed peptides exhibited different degrees of target-binding affinity and self-association. Rationally engineered molecules were more potent inhibitors of DNA binding than a control peptide corresponding to the native BZLF1 dimerization region itself. The most potent inhibitors included both positive and negative design elements and exploited interaction with the coiled-coil and basic DNA-binding regions of BZLF1.

Determinants of BH3 binding specificity for Mcl-1 versus Bcl-xL.

Interactions among Bcl-2 family proteins are important for regulating apoptosis. Prosurvival members of the family interact with proapoptotic BH3 (Bcl-2-homology-3)-only members, inhibiting execution of cell death through the mitochondrial pathway. Structurally, this interaction is mediated by binding of the alpha-helical BH3 region of the proapoptotic proteins to a conserved hydrophobic groove on the prosurvival proteins. Native BH3-only proteins exhibit selectivity in binding prosurvival members, as do small molecules that block these interactions. Understanding the sequence and structural basis of interaction specificity in this family is important, as it may allow the prediction of new Bcl-2 family associations and/or the design of new classes of selective inhibitors to serve as reagents or therapeutics. In this work, we used two complementary techniques--yeast surface display screening from combinatorial peptide libraries and SPOT peptide array analysis--to elucidate specificity determinants for binding to Bcl-x(L)versus Mcl-1, two prominent prosurvival proteins. We screened a randomized library and identified BH3 peptides that bound to either Mcl-1 or Bcl-x(L) selectively or to both with high affinity. The peptides competed with native ligands for binding into the conserved hydrophobic groove, as illustrated in detail by a crystal structure of a specific peptide bound to Mcl-1. Mcl-1-selective peptides from the screen were highly specific for binding Mcl-1 in preference to Bcl-x(L), Bcl-2, Bcl-w, and Bfl-1, whereas Bcl-x(L)-selective peptides showed some cross-interaction with related proteins Bcl-2 and Bcl-w. Mutational analyses using SPOT arrays revealed the effects of 170 point mutations made in the background of a peptide derived from the BH3 region of Bim, and a simple predictive model constructed using these data explained much of the specificity observed in our Mcl-1 versus Bcl-x(L) binders.