Predicting kinase substrates using conservation of local motif density

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions

MOTIVATION

The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level.

RESULTS

We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/esbgkannan/phosformer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis

With the development of biotechnology, a large number of phosphorylation sites have been experimentally confirmed and collected, but only a few of them have kinase annotations. Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https://github.com/guoxinyunncu/PKSPS.

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions

In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al., 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as 'evolutionary signatures' of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

Recent Advances in Substrate Identification of Protein Kinases in Plants and Their Role in Stress Management

Protein phosphorylation-dephosphorylation is a well-known regulatory mechanism in biological systems and has become one of the significant means of protein function regulation, modulating most of the biological processes. Protein kinases play vital role in numerous cellular processes. Kinases transduce external signal into responses such as growth, immunity and stress tolerance through phosphorylation of their target proteins. In order to understand these cellular processes at the molecular level, one needs to be aware of the different substrates targeted by protein kinases. Advancement in tools and techniques has bestowed practice of multiple approaches that enable target identification of kinases. However, so far none of the methodologies has been proved to be as good as a panacea for the substrate identification. In this review, the recent advances that have been made in the identifications of putative substrates and the implications of these kinases and their substrates in stress management are discussed.

The Structure of an NDR/LATS Kinase-Mob Complex Reveals a Novel Kinase-Coactivator System and Substrate Docking Mechanism

Eukaryotic cells commonly use protein kinases in signaling systems that relay information and control a wide range of processes. These enzymes have a fundamentally similar structure, but achieve functional diversity through variable regions that determine how the catalytic core is activated and recruited to phosphorylation targets. “Hippo” pathways are ancient protein kinase signaling systems that control cell proliferation and morphogenesis; the NDR/LATS family protein kinases, which associate with “Mob” coactivator proteins, are central but incompletely understood components of these pathways. Here we describe the crystal structure of budding yeast Cbk1–Mob2, to our knowledge the first of an NDR/LATS kinase–Mob complex. It shows a novel coactivator-organized activation region that may be unique to NDR/LATS kinases, in which a key regulatory motif apparently shifts from an inactive binding mode to an active one upon phosphorylation. We also provide a structural basis for a substrate docking mechanism previously unknown in AGC family kinases, and show that docking interaction provides robustness to Cbk1’s regulation of its two known in vivo substrates. Co-evolution of docking motifs and phosphorylation consensus sites strongly indicates that a protein is an in vivo regulatory target of this hippo pathway, and predicts a new group of high-confidence Cbk1 substrates that function at sites of cytokinesis and cell growth. Moreover, docking peptides arise in unstructured regions of proteins that are probably already kinase substrates, suggesting a broad sequential model for adaptive acquisition of kinase docking in rapidly evolving intrinsically disordered polypeptides.

The structure of an ancient signaling complex in the Hippo pathway (an NDR/LATS family kinase) reveals a distinctive activation switch, and shows how this regulator recognizes short and rapidly evolving protein motifs.

The core organization of systems that relay information inside cells is preserved across vast evolutionary distances. Thus, detailed characterization of these systems’ crucial modules can provide insight into the emergence and adaptation of signaling pathways, and illuminate broadly relevant mechanisms that control cells’ diverse processes. In this paper we describe the first three-dimensional structure of a protein kinase–coactivator complex from budding yeast that is a key component of “hippo” signaling pathways, which direct cell proliferation, fate, and architecture in a wide range of eukaryotes. We show that this kinase–coactivator complex is a dynamic switch controlled by binding events distant from its active site, and that the kinase recognizes specific short motifs in disordered regions of target proteins by a previously unknown mechanism. This substrate docking interaction provides in vivo robustness to the kinase’s regulation of its known targets, and identifies likely new substrates that expand our view of this hippo pathway’s role in cell division. Moreover, during the course of evolution, the short motif that interacts with the kinase’s docking surface appears in rapidly changing intrinsically disordered regions of a number of proteins that are probably already in vivo substrates. Thus, our findings support the idea that proteins evolve more robust functional links to the signaling networks that control them by acquiring short peptide motifs that interface with key conserved signaling modules.

Detecting functional divergence after gene duplication through evolutionary changes in posttranslational regulatory sequences

Gene duplication is an important evolutionary mechanism that can result in functional divergence in paralogs due to neo-functionalization or sub-functionalization. Consistent with functional divergence after gene duplication, recent studies have shown accelerated evolution in retained paralogs. However, little is known in general about the impact of this accelerated evolution on the molecular functions of retained paralogs. For example, do new functions typically involve changes in enzymatic activities, or changes in protein regulation? Here we study the evolution of posttranslational regulation by examining the evolution of important regulatory sequences (short linear motifs) in retained duplicates created by the whole-genome duplication in budding yeast. To do so, we identified short linear motifs whose evolutionary constraint has relaxed after gene duplication with a likelihood-ratio test that can account for heterogeneity in the evolutionary process by using a non-central chi-squared null distribution. We find that short linear motifs are more likely to show changes in evolutionary constraints in retained duplicates compared to single-copy genes. We examine changes in constraints on known regulatory sequences and show that for the Rck1/Rck2, Fkh1/Fkh2, Ace2/Swi5 paralogs, they are associated with previously characterized differences in posttranslational regulation. Finally, we experimentally confirm our prediction that for the Ace2/Swi5 paralogs, Cbk1 regulated localization was lost along the lineage leading to SWI5 after gene duplication. Our analysis suggests that changes in posttranslational regulation mediated by short regulatory motifs systematically contribute to functional divergence after gene duplication.

How a protein is controlled is intimately linked to its function. Therefore, evolution can drive the functional divergence of proteins by tweaking their regulation, even if enzymatic capacities are preserved. Changes in posttranslational regulation (protein phosphorylation, degradation, subcellular localization, etc.) could therefore represent key mechanisms in functional divergence and lead to different phenotypic outcomes. Since disordered protein regions contain sites of protein modification and interaction (known as short linear motifs) and evolve rapidly relative to domains encoding enzymatic functions, these regions are good candidates to harbour sequence changes that underlie changes in function. In this study, we develop a statistical framework to identify changes in rate of evolution specific to protein regulatory sequences and identify hundreds of short linear motifs in disordered regions that are likely to have diverged after the whole-genome duplication in budding yeast. We show that these divergent motifs are much more frequent in paralogs than in single-copy proteins, and that they are more frequent in duplicate pairs that have functionally diverged. Our analysis suggests that changes in short linear motifs in disordered protein regions could be important molecular mechanisms of functional divergence after gene duplication.

Exploiting holistic approaches to model specificity in protein phosphorylation

Phosphate plays a chemically unique role in shaping cellular signaling of all current living systems, especially eukaryotes. Protein phosphorylation has been studied at several levels, from the near-site context, both in sequence and structure, to the crowded cellular environment, and ultimately to the systems-level perspective. Despite the tremendous advances in mass spectrometry and efforts dedicated to the development of ad hoc highly sophisticated methods, phosphorylation site inference and associated kinase identification are still unresolved problems in kinome biology. The sequence and structure of the substrate near-site context are not sufficient alone to model the in vivo phosphorylation rules, and they should be integrated with orthogonal information in all possible applications. Here we provide an overview of the different contexts that contribute to protein phosphorylation, discussing their potential impact in phosphorylation site annotation and in predicting kinase-substrate specificity.

Turnover of protein phosphorylation evolving under stabilizing selection

Most proteins are regulated by posttranslational modifications and changes in these modifications contribute to evolutionary changes as well as to human diseases. Phosphorylation of serines, threonines, and tyrosines are the most common modifications identified to date in eukaryotic proteomes. While the mode of action and the function of most phosphorylation sites remain unknown, functional studies have shown that phosphorylation affects protein stability, localization and ability to interact. Two broad modes of action have been described for protein phosphorylation. The first mode corresponds to the canonical and qualitative view whereby single phosphorylation sites act as molecular switches that either turn on or off specific protein functions through direct or allosteric effects. The second mode is more akin to a rheostat than a switch. In this case, a group of phosphorylation sites in a given protein region contributes collectively to the modification of the protein, irrespective of the precise position of individual sites, through an aggregate property. Here we discuss these two types of regulation and examine how they affect the rate and patterns of protein phosphorylation evolution. We describe how the evolution of clusters of phosphorylation sites can be studied under the framework of complex traits evolution and stabilizing selection.

CPhos: a program to calculate and visualize evolutionarily conserved functional phosphorylation sites

Profiling using high-throughput MS has discovered an overwhelming number of novel protein phosphorylation sites ("phosphosites"). However, the functional relevance of these sites is not always clear. In light of recent studies on the evolutionary mechanism of phosphorylation, we have developed CPhos, a Java program that can assess the conservation of phosphosites among species using an information theory-based approach. The degree of conservation established using CPhos can be used to assess the functional significance of phosphosites. CPhos has a user friendly graphical user interface and is available both as a web service and as a standalone Java application to assist phosphoproteomic researchers in analyzing and prioritizing lists of phosphosites for further experimental validation. CPhos can be accessed or downloaded at http://helixweb.nih.gov/CPhos/.