Genome-wide prediction of SH2 domain targets using structural information and the FoldX algorithm

Discovery and Characterization of Linear Motif Mediated Protein-Protein Complexes

There are myriads of protein-protein complexes that form within the cell. In addition to classical binding events between globular domains, many protein-protein interactions involve short disordered protein regions. The latter contain so-called linear motifs binding specifically to ordered protein domain surfaces. Linear binding motifs are classified based on their consensus sequence, where only a few amino acids are conserved. In this chapter we will review experimental and in silico techniques that can be used for the discovery and characterization of linear motif mediated protein-protein complexes involved in cellular signaling, protein level and gene expression regulation.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data

Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2-pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LC_κ) and lambda (LC_λ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LC_κ and LC_λ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.

Developing synthetic biology in Argentina: the Latin American TECNOx community as an alternative way for growth of the field

Synthetic biology emerged in the USA and Europe twenty years ago and quickly developed innovative research and technology as a result of continued funding. Synthetic biology is also growing in many developing countries of Africa, Asia and Latin America, where it could have a large economic impact by helping its use of genetic biodiversity in order to boost existing industries. Starting in 2011, Argentine synthetic biology developed along an idiosyncratic path. In 2011-2012, the main focus was not exclusively research but also on community building through teaching and participation in iGEM, following the template of the early "MIT school" of synthetic biology. In 2013-2015, activities diversified and included society-centered projects, social science studies on synthetic biology and bioart. Standard research outputs such as articles and industrial applications helped consolidate several academic working groups. Since 2016, the lack of a critical mass of researchers and a funding crisis were partially compensated by establishing links with Latin American synthetic biologists and with other socially oriented open technology collectives. The TECNOx community is a central node in this growing research and technology network. The first four annual TECNOx meetings brought together synthetic biologists with other open science and engineering platforms and explored the relationship of Latin American technologies with entrepreneurship, open hardware, ethics and human rights. In sum, the socioeconomic context encouraged Latin American synthetic biology to develop in a meandering and diversifying manner. This revealed alternative ways for growth of the field that may be relevant to other developing countries.

Characterization of recombinant endo-1,4-β-xylanase of Bacillus halodurans C-125 and rational identification of hot spot amino acid residues responsible for enhancing thermostability by an in-silico approach

Increased demand of enzymes for industrial use has led the scientists towards protein engineering techniques. In different protein engineering strategies, rational approach has emerged as the most efficient method utilizing bioinformatics tools to produce enzymes with desired reaction kinetics; physiochemical (temperature, pH, half life, etc) and biological (selectivity, specificity, etc.) characteristics. Xylanase is one of the widely used enzymes in paper and food industry to degrade xylan component present in plant pulp. In this study endo 1,4-β-xylanase (Xyl-11A) from Bacillus halodurans C-125 was cloned in pET-22b (+) vector and expressed in Escherichia coli BL21 (DE3) expression strain. The enzyme had Michaelis constant K_m of 1.32 mg ml^-1 birchwoodxylan (soluble form) and maximum reaction velocity (V_max) 73.53 mmol min^-1 mg^-1 with an optimum temperature of 75 °C and pH 9.0. The thermostability analysis showed that enzyme retained more than 80% of its residual activity when incubated at 75 °C for 2 h. In addition, to increase Xyl-11A thermostability, an in-silico analysis was performedto identify the hot spot amino acid residues. Consensus-based amino acid substitution was applied to evaluate multiple sequence alignment of homologs and identified 20 amino acids positions by following Jensen-Shnnon Divergence method. 3D models of 20 selected mutants were analyzed for conformational transition in protein structures by using NMSim server. Two selected mutants T6K and I17M of Xyl-11A retained 40, 60% residual activity respectively, at 85 °C for 120 min as compared to wild type enzyme which retained 37% initial activity under same conditions, confirming the enhanced thermostability of mutants. The present study showed a good approach for the identification of promising amino acid residues responsible for enhancing the thermostability of enzymes of industrial importance.

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.

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.

AB-Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.

Mutations in the KDM5C ARID Domain and Their Plausible Association with Syndromic Claes-Jensen-Type Disease

Mutations in KDM5C gene are linked to X-linked mental retardation, the syndromic Claes-Jensen-type disease. This study focuses on non-synonymous mutations in the KDM5C ARID domain and evaluates the effects of two disease-associated missense mutations (A77T and D87G) and three not-yet-classified missense mutations (R108W, N142S, and R179H). We predict the ARID domain’s folding and binding free energy changes due to mutations, and also study the effects of mutations on protein dynamics. Our computational results indicate that A77T and D87G mutants have minimal effect on the KDM5C ARID domain stability and DNA binding. In parallel, the change in the free energy unfolding caused by the mutants A77T and D87G were experimentally measured by urea-induced unfolding experiments and were shown to be similar to the in silico predictions. The evolutionary conservation analysis shows that the disease-associated mutations are located in a highly-conserved part of the ARID structure (N-terminal domain), indicating their importance for the KDM5C function. N-terminal residues’ high conservation suggests that either the ARID domain utilizes the N-terminal to interact with other KDM5C domains or the N-terminal is involved in some yet unknown function. The analysis indicates that, among the non-classified mutations, R108W is possibly a disease-associated mutation, while N142S and R179H are probably harmless.

Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases

Mitogen‐activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less‐characterized disordered regions. We used a structurally consistent model on kinase‐docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under‐explored part of the human proteome and applied experimental tools specifically tailored to detect low‐affinity protein–protein interactions for their validation in vitro and in cell‐based assays. The combined computational and experimental approach enabled the identification of many novel MAPK‐docking motifs that were elusive for other large‐scale protein–protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase‐mediated partnerships evolved over time. The analysis suggests that most human MAPK‐binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK‐binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles.

Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking

Protein-peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein-peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein-peptide docking, available as a user's friendly web server. We present example CABS-dock results obtained in the default CABS-dock mode and using its advanced options that enable the user to increase the range of flexibility for chosen receptor fragments or to exclude user-selected binding modes from docking search. Furthermore, we demonstrate a strategy to improve CABS-dock performance by assessing the quality of models with classical molecular dynamics. Finally, we discuss the promising extensions and applications of the CABS-dock method and provide a tutorial appendix for the convenient analysis and visualization of CABS-dock results. The CABS-dock web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/.

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.

Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally.

Phosphorylation by PINK1 releases the UBL domain and initializes the conformational opening of the E3 ubiquitin ligase Parkin

Loss-of-function mutations in PINK1 or PARKIN are the most common causes of autosomal recessive Parkinson's disease. Both gene products, the Ser/Thr kinase PINK1 and the E3 Ubiquitin ligase Parkin, functionally cooperate in a mitochondrial quality control pathway. Upon stress, PINK1 activates Parkin and enables its translocation to and ubiquitination of damaged mitochondria to facilitate their clearance from the cell. Though PINK1-dependent phosphorylation of Ser65 is an important initial step, the molecular mechanisms underlying the activation of Parkin's enzymatic functions remain unclear. Using molecular modeling, we generated a complete structural model of human Parkin at all atom resolution. At steady state, the Ub ligase is maintained inactive in a closed, auto-inhibited conformation that results from intra-molecular interactions. Evidently, Parkin has to undergo major structural rearrangements in order to unleash its catalytic activity. As a spark, we have modeled PINK1-dependent Ser65 phosphorylation in silico and provide the first molecular dynamics simulation of Parkin conformations along a sequential unfolding pathway that could release its intertwined domains and enable its catalytic activity. We combined free (unbiased) molecular dynamics simulation, Monte Carlo algorithms, and minimal-biasing methods with cell-based high content imaging and biochemical assays. Phosphorylation of Ser65 results in widening of a newly defined cleft and dissociation of the regulatory N-terminal UBL domain. This motion propagates through further opening conformations that allow binding of an Ub-loaded E2 co-enzyme. Subsequent spatial reorientation of the catalytic centers of both enzymes might facilitate the transfer of the Ub moiety to charge Parkin. Our structure-function study provides the basis to elucidate regulatory mechanisms and activity of the neuroprotective Parkin. This may open up new avenues for the development of small molecule Parkin activators through targeted drug design.

Parkinson's disease (PD) is a devastating neurological condition caused by the selective and progressive degeneration of dopaminergic neurons in the brain. Loss-of-function mutations in the PINK1 or PARKIN genes are the most common causes of recessively inherited PD. Together the encoded proteins coordinate a protective cellular quality control pathway that allows elimination of impaired mitochondria in order to prevent further cellular damage and ultimately death. Although it is known that the kinase PINK1 operates upstream and activates the E3 Ubiquitin ligase Parkin, the molecular mechanisms remain elusive. Here, we combined state-of-the art computational and functional biological methods to demonstrate that Parkin is sequentially activated through PINK1-dependent phosphorylation and subsequent structural rearrangement. The induced motions result in release of Parkin's closed, auto-inhibited conformation to liberate its enzymatic functions. We provide for the first time a complete protein structure of Parkin at an all atom resolution and a comprehensive molecular dynamics simulation of its activation and opening conformations. The generated models will allow uncovering the exact mechanisms of regulation and enzymatic activity of Parkin and potentially the development of novel therapeutics through a structure-function-based drug design.

Human proteins with target sites of multiple post-translational modification types are more prone to be involved in disease

Many proteins can be modified by multiple types of post-translational modifications (Mtp-proteins). Although some post-translational modifications (PTMs) have recently been found to be associated with life-threatening diseases like cancers and neurodegenerative disorders, the underlying mechanisms remain enigmatic to date. In this study, we examined the relationship of human Mtp-proteins and disease and systematically characterized features of these proteins. Our results indicated that Mtp-proteins are significantly more inclined to participate in disease than proteins carrying no known PTM sites. Mtp-proteins were found significantly enriched in protein complexes, having more protein partners and preferred to act as hubs/superhubs in protein-protein interaction (PPI) networks. They possess a distinct functional focus, such as chromatin assembly or disassembly, and reside in biased, multiple subcellular localizations. Moreover, most Mtp-proteins harbor more intrinsically disordered regions than the others. Mtp-proteins carrying PTM types biased toward locating in the ordered regions were mainly related to protein-DNA complex assembly. Examination of the energetic effects of PTMs on the stability of PPI revealed that only a small fraction of single PTM events influence the binding energy of >2 kcal/mol, whereas the binding energy can change dramatically by combinations of multiple PTM types. Our work not only expands the understanding of Mtp-proteins but also discloses the potential ability of Mtp-proteins to act as key elements in disease development.

Enhanced prediction of Src homology 2 (SH2) domain binding potentials using a fluorescence polarization-derived c-Met, c-Kit, ErbB, and androgen receptor interactome

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains.

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

Positively selected sites in cetacean myoglobins contribute to protein stability

Since divergence ∼50 Ma ago from their terrestrial ancestors, cetaceans underwent a series of adaptations such as a ∼10-20 fold increase in myoglobin (Mb) concentration in skeletal muscle, critical for increasing oxygen storage capacity and prolonging dive time. Whereas the O2-binding affinity of Mbs is not significantly different among mammals (with typical oxygenation constants of ∼0.8-1.2 µM(-1)), folding stabilities of cetacean Mbs are ∼2-4 kcal/mol higher than for terrestrial Mbs. Using ancestral sequence reconstruction, maximum likelihood and bayesian tests to describe the evolution of cetacean Mbs, and experimentally calibrated computation of stability effects of mutations, we observe accelerated evolution in cetaceans and identify seven positively selected sites in Mb. Overall, these sites contribute to Mb stabilization with a conditional probability of 0.8. We observe a correlation between Mb folding stability and protein abundance, suggesting that a selection pressure for stability acts proportionally to higher expression. We also identify a major divergence event leading to the common ancestor of whales, during which major stabilization occurred. Most of the positively selected sites that occur later act against other destabilizing mutations to maintain stability across the clade, except for the shallow divers, where late stability relaxation occurs, probably due to the shorter aerobic dive limits of these species. The three main positively selected sites 66, 5, and 35 undergo changes that favor hydrophobic folding, structural integrity, and intra-helical hydrogen bonds.

Predicting PDZ domain mediated protein interactions from structure

Background

PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.

Results

We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.

Conclusions

We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.

Predictive Bcl-2 family binding models rooted in experiment or structure

Proteins of the Bcl-2 family either enhance or suppress programmed cell death and are centrally involved in cancer development and resistance to chemotherapy. BH3 (Bcl-2 homology 3)-only Bcl-2 proteins promote cell death by docking an α-helix into a hydrophobic groove on the surface of one or more of five pro-survival Bcl-2 receptor proteins. There is high structural homology within the pro-death and pro-survival families, yet a high degree of interaction specificity is nevertheless encoded, posing an interesting and important molecular recognition problem. Understanding protein features that dictate Bcl-2 interaction specificity is critical for designing peptide-based cancer therapeutics and diagnostics. In this study, we present peptide SPOT arrays and deep sequencing data from yeast display screening experiments that significantly expand the BH3 sequence space that has been experimentally tested for interaction with five human anti-apoptotic receptors. These data provide rich information about the determinants of Bcl-2 family specificity. To interpret and use the information, we constructed two simple data-based models that can predict affinity and specificity when evaluated on independent data sets within a limited sequence space. We also constructed a novel structure-based statistical potential, called STATIUM, which is remarkably good at predicting Bcl-2 affinity and specificity, especially considering it is not trained on experimental data. We compare the performance of our three models to each other and to alternative structure-based methods and discuss how such tools can guide prediction and design of new Bcl-2 family complexes.

Domain-mediated protein interaction prediction: From genome to network

Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.

PDZ domain-containing 1 (PDZK1) protein regulates phospholipase C-β3 (PLC-β3)-specific activation of somatostatin by forming a ternary complex with PLC-β3 and somatostatin receptors

Phospholipase C-β (PLC-β) is a key molecule in G protein-coupled receptor (GPCR)-mediated signaling. Many studies have shown that the four PLC-β subtypes have different physiological functions despite their similar structures. Because the PLC-β subtypes possess different PDZ-binding motifs, they have the potential to interact with different PDZ proteins. In this study, we identified PDZ domain-containing 1 (PDZK1) as a PDZ protein that specifically interacts with PLC-β3. To elucidate the functional roles of PDZK1, we next screened for potential interacting proteins of PDZK1 and identified the somatostatin receptors (SSTRs) as another protein that interacts with PDZK1. Through these interactions, PDZK1 assembles as a ternary complex with PLC-β3 and SSTRs. Interestingly, the expression of PDZK1 and PLC-β3, but not PLC-β1, markedly potentiated SST-induced PLC activation. However, disruption of the ternary complex inhibited SST-induced PLC activation, which suggests that PDZK1-mediated complex formation is required for the specific activation of PLC-β3 by SST. Consistent with this observation, the knockdown of PDZK1 or PLC-β3, but not that of PLC-β1, significantly inhibited SST-induced intracellular Ca(2+) mobilization, which further attenuated subsequent ERK1/2 phosphorylation. Taken together, our results strongly suggest that the formation of a complex between SSTRs, PDZK1, and PLC-β3 is essential for the specific activation of PLC-β3 and the subsequent physiologic responses by SST.

Phosphorylation at the interface

Proteomic studies have identified thousands of eukaryotic phosphorylation sites (phosphosites), but few are functionally characterized. Nishi et al., in this issue of Structure, characterize phosphosites at protein-protein interfaces and estimate the effect of their phosphorylation on interaction affinity, by combining proteomics data with protein structures.

Uncovering new aspects of protein interactions through analysis of specificity landscapes in peptide recognition domains

Protein interactions underlie all biological processes. An important class of protein interactions, often observed in signaling pathways, consists of peptide recognition domains binding short protein segments on the surface of their target proteins. Recent developments in experimental techniques have uncovered many such interactions and shed new lights on their specificity. To analyze these data, novel computational methods have been introduced that can accurately describe the specificity landscape of peptide recognition domains and predict new interactions. Combining large-scale analysis of binding specificity data with structure-based modeling can further reveal new biological insights into the molecular recognition events underlying signaling pathways.

Phosphorylation in protein-protein binding: effect on stability and function

Posttranslational modifications offer a dynamic way to regulate protein activity, subcellular localization, and stability. Here we estimate the effect of phosphorylation on protein binding and function for different types of complexes from human proteome. We find that phosphorylation sites tend to be located on binding interfaces in heterooligomeric and weak transient homooligomeric complexes. Analysis of molecular mechanisms of phosphorylation shows that phosphorylation may modulate the strength of interactions directly on interfaces and that binding hotspots tend to be phosphorylated in heterooligomers. Although the majority of complexes do not show significant estimated stability differences upon phosphorylation or dephosphorylation, for about one-third of all complexes it causes relatively large changes in binding energy. We discuss the cases where phosphorylation mediates the complex formation and regulates the function. We show that phosphorylation sites are more likely to be evolutionary conserved than other interfacial residues.

A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence

Motivation: Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders.

Results: We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain–peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction.

Availability and Implementation: The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.

Contact:gary.bader@utoronto.ca; dengnaiyang@cau.edu.cn

Supplementary information:Supplementary data are available at Bioinformatics online.

Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.

Structure-based prediction of protein-peptide specificity in Rosetta

Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.

ADAN: a database for prediction of protein-protein interaction of modular domains mediated by linear motifs

MOTIVATION

Most of the structures and functions of proteome globular domains are yet unknown. We can use high-resolution structures from different modular domains in combination with automatic protein design algorithms to predict genome-wide potential interactions of a protein. ADAN database and related web tools are online resources for the predictive analysis of ligand-domain complexes. ADAN database is a collection of different modular protein domains (SH2, SH3, PDZ, WW, etc.). It contains 3505 entries with extensive structural and functional information available, manually integrated, curated and annotated with cross-references to other databases, biochemical and thermodynamical data, simplified coordinate files, sequence files and alignments. Prediadan, a subset of ADAN database, offers position-specific scoring matrices for protein-protein interactions, calculated by FoldX, and predictions of optimum ligands and putative binding partners. Users can also scan a query sequence against selected matrices, or improve a ligand-domain interaction.

AVAILABILITY

ADAN is accessible at http://adan-embl.ibmc.umh.es/ or http://adan.crg.es/.

Using genome-wide measurements for computational prediction of SH2-peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.

Design of protein-interaction specificity gives selective bZIP-binding peptides

Interaction specificity is a required feature of biological networks and a necessary characteristic of protein or small-molecule reagents and therapeutics. The ability to alter or inhibit protein interactions selectively would advance basic and applied molecular science. Assessing or modelling interaction specificity requires treating multiple competing complexes, which presents computational and experimental challenges. Here we present a computational framework for designing protein-interaction specificity and use it to identify specific peptide partners for human basic-region leucine zipper (bZIP) transcription factors. Protein microarrays were used to characterize designed, synthetic ligands for all but one of 20 bZIP families. The bZIP proteins share strong sequence and structural similarities and thus are challenging targets to bind specifically. Nevertheless, many of the designs, including examples that bind the oncoproteins c-Jun, c-Fos and c-Maf (also called JUN, FOS and MAF, respectively), were selective for their targets over all 19 other families. Collectively, the designs exhibit a wide range of interaction profiles and demonstrate that human bZIPs have only sparsely sampled the possible interaction space accessible to them. Our computational method provides a way to systematically analyse trade-offs between stability and specificity and is suitable for use with many types of structure-scoring functions; thus, it may prove broadly useful as a tool for protein design.

Accurate prediction of peptide binding sites on protein surfaces

Many important protein-protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled) but where no structure of the protein-peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein-peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.