Peptide Recognition Domains Research Articles

PairK: Pairwise k-mer alignment for quantifying protein motif conservation in disordered regions.

Protein-protein interactions are often mediated by a modular peptide recognition domain binding to a short linear motif (SLiM) in the disordered region of another protein. The ability to predict domain-SLiM interactions would allow researchers to map protein interaction networks, predict the effects of perturbations to those networks, and develop biologically meaningful hypotheses. Unfortunately, sequence database searches for SLiMs generally yield mostly biologically irrelevant motif matches or false positives. To improve the prediction of novel SLiM interactions, researchers employ filters to discriminate between biologically relevant and improbable motif matches. One promising criterion for identifying biologically relevant SLiMs is the sequence conservation of the motif, exploiting the fact that functional motifs are more likely to be conserved than spurious motif matches. However, the difficulty of aligning disordered regions has significantly hampered the utility of this approach. We present PairK (pairwise k-mer alignment), an MSA-free method to quantify motif conservation in disordered regions. PairK outperforms both standard MSA-based conservation scores and a modern LLM-based conservation score predictor on the task of identifying biologically important motif instances. PairK can quantify conservation over wider phylogenetic distances than MSAs, indicating that SLiMs may be more conserved than is implied by MSA-based metrics. PairK is available as open-source code at https://github.com/jacksonh1/pairk.

Systematic Modeling, Prediction, and Comparison of Domain-Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The R prd 2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

Interactome Mapping Uncovers a General Role for Numb in Protein Kinase Regulation

Cellular functions are frequently regulated by protein-protein interactions involving the binding of a modular domain in one protein to a specific peptide sequence in another. This mechanism may be explored to identify binding partners for proteins harboring a peptide-recognition domain. Here we report a proteomic strategy combining peptide and protein microarray screening with biochemical and cellular assays to identify modular domain-mediated protein-protein interactions in a systematic manner. We applied this strategy to Numb, a multi-functional protein containing a phosphotyrosine-binding (PTB) domain. Through the screening of a protein microarray, we identified >100 protein kinases, including both Tyr and Ser/Thr kinases, that could potentially interact with the Numb PTB domain, suggesting a general role for Numb in regulating kinase function. The putative interactions between Numb and several tyrosine kinases were subsequently validated by GST pull-down and/or co-immunoprecipitation assays. Furthermore, using the Oriented Peptide Array Library approach, we defined the specificity of the Numb PTB domain which, in turn, allowed us to predict binding partners for Numb at the genome level. The combination of the protein microarray screening with computer-aided prediction produced the most expansive interactome for Numb to date, implicating Numb in regulating phosphorylation signaling through protein kinases and phosphatases. Not only does the data generated from this study provide an important resource for hypothesis-driven research to further define the function of Numb, the proteomic strategy described herein may be employed to uncover the interactome for other peptide-recognition domains whose consensus motifs are known or can be determined.

PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence.

Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study. In this paper, we propose a machine-learning-based tool, named PBRpredict, to predict residues in peptide-binding domains from protein sequence alone. To develop a generic predictor, we train the models on peptide-binding residues of diverse types of domains. As inputs to the models, we use a high-dimensional feature set of chemical, structural and evolutionary information extracted from protein sequence. We carefully investigate six different state-of-the-art classification algorithms for this application. Finally, we use the stacked generalization approach to non-linearly combine a set of complementary base-level learners using a meta-level learner which outperformed the winner-takes-all approach. The proposed predictor is found competitive based on statistical evaluation. PBRpredict-Suite software: http://cs.uno.edu/~tamjid/Software/PBRpredict/pbrpredict-suite.zip. Supplementary data are available at Bioinformatics online.

Transcriptome-wide identification and competitive disruption of sacum-binding partners in human colorectal cancer

Human sacum is regulatory adaptor protein involved in cellular signaling network of colorectal cancer. Molecular evidences suggest that the protein is integrated into oncogenic signaling network by binding to SH3-containing proteins through its proline-rich motifs. In this study, we have performed a transcriptome-wide analysis and identification of sacum-binding partners in the genome profile of human colorectal cancer. The sacum-binding potency of SH3-containing proteins found in colorectal cancer was investigated by using bioinformatics modeling and intermolecular binding analysis. With the protocol we were able to predict those high-affinity domain binders of the proline-rich peptides of human sacum in a high-throughput manner, and to analyze sequence-specific interaction in the domain–peptide recognition at molecular level. Consequently, a number of putative domain binders with both high affinity and specificity were identified, from which the Src SH3 domain was selected as a case study and tested for its binding activity towards the sacum peptides. We also designed two peptide variants that may have potent capability to competitively disrupt sacum interaction with its partners.

Rational redesign of a cation···π···π stacking at cardiovascular Fbw7-Skp1 complex interface and its application for deriving self-inhibitory peptides to disrupt the complex interaction.

The Fbw7-Skp1 complex is an essential component in the formation and development of the mammalian cardiovascular system; the complex interaction is mediated through binding of Skp1 C-terminal peptide (qGlu-peptide) to the F-box domain of Fbw7. By visually examining the crystal structure, we identified a typical cation ···π···π stacking system at the complex interface, which is formed by the Trp1159 residue of qGlu-peptide with the Lys2299 and His2359 residues of Fbw7 F-box domain. Both hybrid quantum mechanics/molecular mechanics (QM/MM) analysis of the real domain-peptide complex and electron-correlation ab initio calculation of the stacking system model suggested that the cation···π···π plays an important role in stabilizing the complex; substitution of peptide Trp1159 residue with aromatic Phe and Tyr would not cause a considerable effect on the configuration and energetics of cation···π···π stacking system, whereas His substitution seems to largely destabilize the system. Subsequently, the qGlu-peptide was stripped from the full-length Skp1 protein to define a so-called self-inhibitory peptide, which may rebind to the domain-peptide complex interface and thus disrupt the complex interaction. Fluorescence polarization (FP) assays revealed that the Trp1159Phe and Trp1159Tyr variants have a comparable or higher affinity (K d=41 and 62μM) than the wild-type qGlu-peptide (K d=56μM), while the Trp1159His mutation would largely impair the binding potency of qGlu-peptide to Fbw7 F-box domain (K d=280μM), confirming that the cation···π···π confers both affinity and specificity to the domain-peptide recognition, which can be reshaped by rational molecular design of the nonbonded interaction system. Graphical abstract Stereoview of the complex structure of Fbw7 with Skp1 (PDB: 2ovp), where the Trp1159 residue of Skp1 qGlu-peptide can form a cation···π···π stacking system with the Lys2299 and His2359 residues of Fbw7 F-box domain.

Publisher Note

Human sacum is regulatory adaptor protein involved in cellular signaling network of colorectal cancer. Molecular evidence suggests that the protein is integrated into oncogenic signaling network by binding to SH3-containing proteins through its proline-rich motifs. In this study, we have performed a transcriptome-wide analysis and identification of sacum-binding partners in the genome profile of human colorectal cancer. The sacum-binding potency of SH3-containing proteins found in colorectal cancer was investigated by using bioinformatics modeling and intermolecular binding analysis. With the protocol we were able to predict those high-affinity domain binders of the proline-rich peptides of human sacum in a high-throughput manner, and to analyze sequence-specific interaction in the domain-peptide recognition at molecular level. Consequently, a number of putative domain binders with both high affinity and specificity were identified, from which the Src SH3 domain was selected as a case study and tested for its binding activity towards the sacum peptides. We also designed two peptide variants that may have potent capability to competitively disrupt sacum interaction with its partners.

Systematic Identification and Competitive Disruption of Securin-Binding Partners in the Gene Diversity Space of Human Colorectal Cancer.

Human securin is regulatory protein involved in control of the metaphase-anaphase transition and anaphase onset of colorectal cancer. Molecular evidences suggest that the protein is integrated into oncogenic signaling network by binding to SH3-containing proteins through its proline-rich peptides. In this study, we have performed a genome-wide analysis and identification of securin-binding partners in the gene diversity space of human colorectal cancer. The securin-binding potency of SH3-containing proteins found in colorectal cancer was investigated by using bioinformatics modeling and intermolecular assay. With the protocol we were able to predict those high-affinity domain binders of the proline-rich peptides of human securin in a high-throughput manner, and to analyze sequence-specific interaction in the domain-peptide recognition at molecular level. Consequently, a number of putative domain binders with both high affinity and specificity were identified, from which the Src SH3 domain was selected as a case study and tested for its binding activity towards the securin peptides using fluorescence-based analysis. We also designed two peptide mutants that may have potent capability to competitively disrupt securin interaction with its partners. This article is protected by copyright. All rights reserved.

NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

Peptides are extensively used to characterize functional or (linear) structural aspects of receptor–ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data. Many features of the training sequences can be encoded as input, and the network architecture is highly customizable. The results returned by the server include a graphical representation of the motif identified by the method, performance values and a downloadable model that can be applied to scan protein sequences for occurrence of the motif. While its performance for the characterization of peptide–MHC interactions is widely documented, we extended NNAlign to be applicable to other receptor–ligand systems as well. Version 2.0 supports alignments with insertions and deletions, encoding of receptor pseudo-sequences, and custom alphabets for the training sequences. The server is available at http://www.cbs.dtu.dk/services/NNAlign-2.0.

Peptide Scaffold-Based Discovery of Nonpeptide Natural Medicines to Target PI3K p85 SH2 Domain

Targeting phosphoinositide 3-kinase (PI3K) has been recognized as an attractive strategy for anticancer therapy. The PI3K is a heterodimer composed of a catalytic subunit p110 and a regulatory subunit p85. Here, instead of targeting the catalytic p110 that has been considered previously, we purposed targeting the peptide-recognition domain SH2 of regulatory p85 with natural medicines obtained by using a peptide scaffold-based screening scheme. In the procedure, a core binding motif was extracted from the cocrystallized complex of a cognate phosphopeptide with the domain, which was considered as basic scaffold to perform high-through virtual screening against a structurally diverse, nonredundant library of natural products. A number of hit compounds with high binding potency to the domain and significant conformational similarity with the peptide scaffold were identified; in vitro affinity assay confirmed that five hits have moderate or high affinity for the domain with measured dissociation constants Kd range between 25 and 360 μM, which are comparable to or even better than that of the cognate phosphopeptide SDpYMNMTP and its core motif peptide pYMNM (Kd = 15 and 32 μM, respectively). Structural analysis and nonbonded comparison of SH2 interactions with phosphopeptides and potent hit compounds revealed that only negatively charged phosphate and, sometime, sulfate can confer domain-binding capability to small-molecule compounds, but carboxylate cannot. A similar binding mode of compounds with phosphopeptide is important for the compounds to have high affinity and specificity.

Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression.

Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains. The cross validated results of the domain-peptide binding affinity data sets show that predictive performance of the support vector based models are efficient.

The Peptide-Mediated Interactions Between Human Osteoclast-Stimulating Factor and Its Partner Proteins in Osteoporosis: Which Binds to Which?

Human osteoclast-stimulating factor (hOSF) is an intracellular protein produced by osteoclasts that induces osteoclast formation and bone resorption in osteoporosis by recruiting multiple signaling complexes with its diverse biological partners through peptide-mediated interactions (PMIs). The protein contains a modular peptide-recognition domain of Src homology 3 (SH3), which can recognize and bind to the polyproline regions of its partner proteins, as well as two N-terminal polyproline segments, which can be recognized and bound by the SH3 domains of its partner proteins. Here, we attempted to elucidate the complicated PMIs between the different SH3 domains and different polyprolines of hOSF and its three known interacting partners, i.e. proto-oncogene tyrosine-protein kinase (c-Src), survival motor neuron (SMN) and Src-associated in mitosis, 68 kD (Sam68). A total of 29 peptide segments containing the SH3-binding motif PXXP were extracted from these partner proteins, which are potential binding sites of hOSF SH3 domain, while the c-Src kinase also possesses a SH3 domain that may recognize and bind the two polyproline peptides at hOSF N-terminus. Structural bioinformatics analysis identified a number of biologically functional PMI candidates between these SH3 domains and these polyproline peptides, which were then tested in vitro using fluorescence spectroscopy assays. Consequently, it is found that (i) hOSF SH3 domain exhibits strong binding potency to two Sam68 peptides 36RQPPLPHR43 (K d = 13.7 μM) and 425APPARPVK432 (K d = 3.2 μM) as well as moderate affinity to three SMN peptides 193FLPPPPPM200 (K d = 56.2 μM), 235PFPSGPPI242 (K d = 28.4 μM) and 246PPPICPDS253 (K d = 74.5 μM), but has only weak or no binding to c-Src peptides. Instead, a proline-rich region at hOSF N-terminal that contains two overlapping peptides (3KPPPKPVK10 and 6PKPVKPGQ13) can be bound tightly by c-Src SH3 domain with high and moderate affinity (K d = 5.8 and 39.6 μM, respectively).

Targeting Yes-associated Protein with Evolved Peptide Aptamers to Disrupt TGF-β Signaling Pathway: Therapeutic Implication for Bone Tumor.

The binding of transcription coactivator Yes-associated protein (YAP) to Smad transcription factors is an important event in activating transforming growth factor-β (TGF-β) signaling pathway, which is involved in the tumorigenicity and metastasis of bone tumor. Design of peptide aptamers to disrupt YAPSmad interaction has been established as a promising approach for bone tumor therapy. Here, an evolution strategy was used to optimize Smad-derived peptides for high potency binding to YAP WW2 domain, resulting in an improved peptide population, from which those high-scoring candidates were characterized rigorously using molecular dynamics (MD) simulations and interaction free energy calculations. With the computational protocol we were able to generate a number of potential domain binders, which were then substantiated by using fluorescence spectroscopy assay. Subsequently, the complex structure of YAP WW2 domain with a high-affinity peptide was modeled and examined in detail, which was then used to guide structure-based peptide optimization to obtain several strong domain binders. Structural and energetic analysis revealed that electrostatic complementarity is primarily responsible for domainpeptide recognition, while other nonbonded interactions such as hydrogen bonding and salt bridges can contribute significantly to the recognition specificity.

Evolution of the SH3 Domain Specificity Landscape in Yeasts.

To explore the conservation of Src homology 3 (SH3) domain-mediated networks in evolution, we compared the specificity landscape of these domains among four yeast species, Saccharomyces cerevisiae, Ashbya gossypii, Candida albicans, and Schizosaccharomyces pombe, encompassing 400 million years of evolution. We first aligned and catalogued the families of SH3-containing proteins in these four species to determine the relationships between homologous domains. Then, we tagged and purified all soluble SH3 domains (82 in total) to perform a quantitative peptide assay (SPOT) for each SH3 domain. All SPOT readouts were hierarchically clustered and we observed that the organization of the SH3 specificity landscape in three distinct profile classes remains conserved across these four yeast species. We also produced a specificity profile for each SH3 domain from manually aligned top SPOT hits and compared the within-family binding motif consensus. This analysis revealed a striking example of binding motif divergence in a C. albicans Rvs167 paralog, which cannot be explained by overall SH3 sequence or interface residue divergence, and we validated this specificity change with a yeast two-hybrid (Y2H) assay. In addition, we show that position-weighted matrices (PWM) compiled from SPOT assays can be used for binding motif screening in potential binding partners and present cases where motifs are either conserved or lost among homologous SH3 interacting proteins. Finally, by comparing pairwise SH3 sequence identity to binding profile correlation we show that for ~75% of all analyzed families the SH3 specificity profile was remarkably conserved over a large evolutionary distance. Thus, a high sequence identity within an SH3 domain family predicts conserved binding specificity, whereas divergence in sequence identity often coincided with a change in binding specificity within this family. As such, our results are important for future studies aimed at unraveling complex specificity networks of peptide recognition domains in higher eukaryotes, including mammals.

Bioinformatics Approaches for Predicting Disordered Protein Motifs.

Short, linear motifs (SLiMs) in proteins are functional microdomains consisting of contiguous residue segments along the protein sequence, typically not more than 10 consecutive amino acids in length with less than 5 defined positions. Many positions are 'degenerate' thus offering flexibility in terms of the amino acid types allowed at those positions. Their short length and degenerate nature confers evolutionary plasticity meaning that SLiMs often evolve convergently. Further, SLiMs have a propensity to occur within intrinsically unstructured protein segments and this confers versatile functionality to unstructured regions of the proteome. SLiMs mediate multiple types of protein interactions based on domain-peptide recognition and guide functions including posttranslational modifications, subcellular localization of proteins, and ligand binding. SLiMs thus behave as modular interaction units that confer versatility to protein function and SLiM-mediated interactions are increasingly being recognized as therapeutic targets. In this chapter we start with a brief description about the properties of SLiMs and their interactions and then move on to discuss algorithms and tools including several web-based methods that enable the discovery of novel SLiMs (de novo motif discovery) as well as the prediction of novel occurrences of known SLiMs. Both individual amino acid sequences as well as sets of protein sequences can be scanned using these methods to obtain statistically overrepresented sequence patterns. Lists of putatively functional SLiMs are then assembled based on parameters such as evolutionary sequence conservation, disorder scores, structural data, gene ontology terms and other contextual information that helps to assess the functional credibility or significance of these motifs. These bioinformatics methods should certainly guide experiments aimed at motif discovery.

Prediction and experimental characterization of nsSNPs altering human PDZ-binding motifs.

Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations.

Why ligand cross-reactivity is high within peptide recognition domain families? A case study on human c-Src SH3 domain

Many important protein–protein interactions in eukaryotic signaling networks are mediated by peptide recognition domains (PRDs), which bind short linear sequence motifs in other proteins. However, high ligand cross-reactivity is observed within most PRD families, rendering a broad specificity for the family members. In the present study, we attempt to explore the molecular mechanism and physicochemical origin of PRD cross-reactivity. In the procedure, a structure-based method called atomic cross-nonbonded interaction analysis (ACNIA) is described to extract atomic-level nonbonded interaction information at domain–peptide interface and to correlate the information with peptide affinity based on a set of structure-solved, affinity-known protein–peptide complex samples compiled from numerous literatures and databases. The ACNIA-derived affinity predictor is tested rigorously with statistical validation approach, which is also demonstrated to be capable of perceiving slight structural change in the interface using three distinct panels of SH3-binding peptide data. Subsequently, with help of the affinity predictor we adopt the human c-Src SH3 domain, one of the most sophisticated PRDs, as a paradigm to investigate the ligand cross-reactivity within SH3 family. It is found that most of the family members have only few non-essential residue differences in their peptide-binding pockets, and thus exhibit a similar peptide recognition profile and high cross-reactivity. The cross-reactivity is even shared by different subclasses of SH3 domains. The findings suggest that inherent binding specificity is not the only factor to select appropriate binders for specific SH3 domains, and other aspects such as cellular context and the rest of the SH3-containing proteins may play important roles in reducing their ligand cross-reactivity.

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

Domain–peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain–peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain–peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain–peptide binding at three-dimensional structure level.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

The number of protein-peptide interactions in a cell isso large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method forthe discovery of domain interaction sites and domain-domain docking.

Recognition of Methylated Peptides by Drosophila melanogaster Polycomb Chromodomain

Lysine methylation is one of the important post-translational modifications (PTMs) that regulate protein functions. Up to now, proteomic identification of this PTM remains a challenge due to the lack of effective enrichment methods in mass spectrometry experiments. To address this challenge, we present here a systematic approach to predicting peptides in which lysine residues may be methylated to mediate protein-protein interactions. We used the chromodomain of the polycomb protein in Drosophila melanogaster as a model system to illustrate the success of this approach. We started with molecular dynamics simulations and free energy analyses on the histone peptides complexed with the polycomb chromodomain to understand how the binding specificity is achieved. We next conducted virtual mutagenesis to quantify each domain and peptide residue's contribution to the domain-peptide recognition, based on which scoring scheme was developed to evaluate the possibility of any lysine-containing peptides to be methylated and recognized by the chromodomain. A peptide microarray experiment on a panel of conserved histone peptides showed a satisfactory prediction accuracy of the scoring scheme. Next, we implemented a bioinformatics pipeline that integrates multiple lines of evidence including conservation, subcellular localization, and mass spectrometry data to scan the fly proteome for a systematic identification of possible methyllysine-containing peptides. These putative chromodomain-binding peptides suggest unknown functions of the important regulator protein polycomb and provide a list of candidate methylation events for follow-up investigations.