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Title: Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm}
Article:
Abstract:
Current experiments likely cover only a fraction of all protein-protein interactions. Here, we developed a method to predict SH2-mediated protein-protein interactions using the structure of SH2-phosphopeptide complexes and the FoldX algorithm. We show that our approach performs similarly to experimentally derived consensus sequences and substitution matrices at predicting known in vitro and in vivo targets of SH2 domains. We use our method to provide a set of high-confidence interactions for human SH2 domains with known structure filtered on secondary structure and phosphorylation state. We validated the predictions using literature-derived SH2 interactions and a probabilistic score obtained from a naive Bayes integration of information on coexpression, conservation of the interaction in other species, shared interaction partners, and functions. We show how our predictions lead to a new hypothesis for the role of SH2 domains in signaling.
Introduction:
The cell's ability to respond to internal and external cues depends largely on reversible post-translational modifications of proteins, such as phosphorylation, ubiquitylation, methylation or acetylation. These modifications often occur on short unstructured stretches of proteins and are read by domains that recognize the modified form[1]. Signal transduction often involves phosphorylation of tyrosine residues by tyrosine kinases. This turns on the recognition of the phosphorylated site by SH2-domain containing proteins, leading to regulation of cellular localization, enzymatic activity and formation of multiprotein complexes[2],[3]. Experiments using peptide libraries indicate that each SH2 domain binds a different spectrum of phosphopeptides[4]–[8]. Although the differences in the binding constants for different phosphopeptides are often modest[9], they are known to play an important role in regulating signal transduction in vivo[3]. For example, exchanging an SH2 domain for another with a different specificity can impair activation of the Ras pathway in Caenorhabditis elegans[10], alter the transformation ability of the Abelson murine leukemia[11] and the Rous sarcoma viruses[12] and trigger mesoderm formation in Xenopus laevis[13]. Moreover, point mutations that induce changes in specificity are associated with diseases such as the X-linked alpha-gammaglobulinemia[14], the X-linked lymphoproliferative syndrome[15] and the Noonan syndrome[16]. The in vitro binding specificity of SH2 domains is commonly determined using peptide libraries[4],[17]. The results of peptide library experiments are often summarized in the form of consensus sequences[4] or as position-specific scoring matrices[18] and then used to predict and characterize novel in vivo SH2-mediated protein-protein interactions. However, the genome-wide determination of the binding specificity of SH2 domains using peptide libraries seems impractical given the more than one hundred human SH2 domains[19] and the limited complexity of the peptide libraries available. The computational modeling of SH2 domain specificity is in a developing stage[20]–[22]. On one hand, fast methods with energy functions based on solvent-accessible surface area reached only limited success[20]. On the other hand, algorithms using molecular dynamics[21] and comparative molecular field analysis[22] showed a good predictive power but are computationally expensive and can only be used to study a limited number of complexes for a given SH2 domain. Recently, McLaughlin and coworkers predicted the binding specificity of two SH2 domains by combining information on known binding peptides with structure-based calculations[23]. The resulting hidden Markov models could be used in a genomic scale to predict SH2-mediated interactions[23]. However, a main drawback of their method is that it relies partially on experimental information. The limitations of the current computational methods encouraged us to develop a new structure-based algorithm to predict the specificity of SH2 domains. Our group has developed FoldX, an empirical force field for the prediction of protein energetics[24]. The energy of a protein or protein complex is calculated in FoldX using a structure-based energy function. This energy function is a linear combination of empirical terms such as solvation of polar and hydrophobic atoms, water binding, Van der Waals energy, steric clashes, hydrogen bonds, electrostatic interactions and side chain and main chain entropy. These energy terms are scaled with atom or residue burial and have empirical weights derived by fitting to a database with more than one thousand mutations[24]. FoldX can give accurate predictions for changes in protein stability upon mutation[24], water and metal binding[25] and interactions between globular domains[26]–[29]. The algorithm is fast enough to be used in genome-wide predictions and the modularity of its energy function makes the implementation of new capabilities straightforward. FoldX is available online at http://foldx. crg. es. We have implemented the force field contributions of phosphorylated amino acids( pTyr, pSer and pThr) into FoldX and used it to predict the binding specificity of nine human SH2 domains with known structure. Our calculations can reproduce experimental consensus target sequences. FoldX performs as well as experimentally derived consensus sequences or position-specific substitution matrices in the prediction of in vitro SH2-phosphopeptide binding and in vivo SH2-mediated protein-protein interactions. Together with information on phosphorylation and secondary structure, FoldX can give accurate predictions of novel protein-protein interactions. We used the developed method to predict a high confidence SH2 interaction network and validated it using information on co-expression, conservation of the interaction in other species, shared interaction partners and shared GO functions, integrated using a naive Bayes network. The predicted interactions can be use to derive biologically relevant testable hypothesis.
Discussion:
We have implemented phosphorylation into FoldX in order to predict the binding specificity of SH2 domains. There are only nine available X-ray structures of human SH2 domains in complex with a target phosphopeptide( Table 1). We tried to overcome the modest size of this dataset by testing whether the upgraded version of FoldX can predict six different kinds of experimental results: the effect of dephosphorylation( Table S1) and of mutations in the environment of the peptide group on the stability of the complex( Figure 1), the consensus target sequence for the domain( Table 1), the in vitro binding specificity( Figure 2) and how it changes upon mutation of the SH2 domain( Table 2) and in vivo SH2-mediated protein-protein interactions( Figure 3). The performance of FoldX is comparable in all cases to consensus target sequences and substitution matrices derived from experiment. Based on this combination of results, we propose that the FoldX algorithm is a useful alternative to peptide library experiments for the prediction of SH2-mediated protein-protein interactions. Several groups have developed other structure-based methods to predict the in vitro binding specificity of SH2 domains[20]–[22]. Henriques and coworkers tested several solvent-accessible surface area-based energy functions for binding of 6 phosphopeptides to the src SH2 domain, finding a poor correlation between their calculations and experiment[20]. Later algorithms using molecular dynamics[21] and comparative molecular field analysis[22] showed a good predictive power. Both studies focused on a small number of complexes( 9 and 30, respectively) and on a single SH2 domain. The portability of these methods to other SH2 domains remains to be shown. Here, we have shown that FoldX can predict the in vitro binding thermodynamics of protein-phosphopeptide complexes including nine different SH2 domains, showing its applicability for genome-wide predictions. The structure-based prediction of SH2-mediated in vivo protein-protein interactions has been addressed only once. McLaughlin and coworkers predicted the binding specificity of the Grb2 and SAP SH2 domains by combining information on known binding peptides plus structure-based predictions[23]. The resulting hidden Markov models gave predictions enriched in known in vivo interactions and binding sites[23]. The main advantage of our approach is that we derived the binding specificity using only structure-based calculations. Thus, our method is applicable to domains for which no binding experiments are available. We also benefit from the use of extra information on phosphorylation and secondary structure, which is available in databases or readily calculated from sequence. Our approach is limited by the number of known structures of protein-phosphopeptide complexes. Nevertheless, given the structure of an SH2 domain in isolation, the location of the phosphopeptide binding site and the structure of a given SH2-phosphopeptide complex can be predicted computationally[58]-[60]. This, together with available methods for homology modelling of globular domains will widen the applicability of FoldX considerably in the near future. FoldX can predict in vivo interactions as well as methods based on experimentally determined in vitro specificity. The fact that both methods based on binding specificity can make useful predictions confirms that specificity plays an important role in determining SH2-mediated protein interactions in vivo. On the other hand, the observed limitations of these methods strongly suggests that high affinity between an SH2 domain and its binding site is necessary but not sufficient to mediate binding in vivo due to other factors like co-expression, co-localization, phosphorylation and binding site availability requirements. FoldX may miss an SH2-mediated interaction in which specificity plays only a minor role, stressing the importance of integrating biological information into our method. We believe that future prediction methods should account for both the biophysics and the biology of SH2 domains. We have used FoldX to predict in vivo protein-protein interactions for nine SH2 domain-containing proteins and annotated the predicted interactions with supporting information, providing a resource for further experimental testing. The predicted interactions are more informative than typical high-throughput or bioinformatics experiments in the sense that they provide binding site information and a structural template for the putative complex. Together with the prediction of binding specificity for other peptide binding domains and enzymes, we propose that FoldX can be used for the large-scale prediction and study of protein-protein interaction networks and signaling cascades and the impact of genetic variation in binding.
Understanding the functional role of every protein in the cell is a long-standing goal of cellular biology . An important step in this direction is to discover how and when proteins interact inside the cell to accomplish their tasks . Many of the cellular functions depend on reversible protein modifications like phosphorylation . To sense these modifications , cells have protein domains capable of binding phosphorylated proteins such as the SH2 domain . In this work , we show that it is possible to use the three-dimensional structure of protein domains to predict its binding preferences . Using a computational tool called FoldX , we have predicted the binding specificity of several human SH2 domains . These predictions , based on the computational analysis of the 3-D structure , were shown to be of similar accuracy as those obtained from experimental binding assays . We show here that it is also possible to understand how a mutation changes the binding preference of protein binding domains , opening the way for better understanding of some disease causing mutations . The combination of this novel computational approach with other sources of information allowed us to provide a set of high-confidence novel interactions for the proteins here studied .