Assembly of cell regulatory systems through protein interaction domains

Modular binder technology by NGS-aided, high-resolution selection in yeast of designed armadillo modules

Establishing modular binders as diagnostic detection agents represents a cost- and time-efficient alternative to the commonly used binders that are generated one molecule at a time. In contrast to these conventional approaches, a modular binder can be designed in silico from individual modules to, in principle, recognize any desired linear epitope without going through a selection and hit-validation process, given a set of preexisting, amino acid-specific modules. Designed armadillo repeat proteins (dArmRP) have been developed as modular binder scaffolds, and we report here the generation of highly specific dArmRP modules by yeast surface display selection, performed on a rationally designed dArmRP library. A selection strategy was developed to distinguish the binding difference resulting from a single amino acid mutation in the target peptide. Our reverse-competitor strategy introduced here employs the designated target as a competitor to increase the sensitivity when separating specific from cross-reactive binders that show similar affinities for the target peptide. With this switch in selection focus from affinity to specificity, we found that the enrichment during this specificity sort is indicative of the desired phenotype, regardless of the binder abundance. Hence, deep sequencing of the selection pools allows retrieval of phenotypic hits with only 0.1% abundance in the selectivity sort pool from the next-generation sequencing data alone. In a proof-of-principle study, a binder was created by replacing all corresponding wild-type modules with a newly selected module, yielding a binder with very high affinity for the designated target that has been successfully validated as a detection agent in western blot analysis.

Recent advances in design and application of synthetic membraneless organelles

Membraneless organelles (MLOs) formed by liquid-liquid phase separation (LLPS) have been extensively studied due to their spatiotemporal control of biochemical and cellular processes in living cells. These findings have provided valuable insights into the physicochemical principles underlying the formation and functionalization of biomolecular condensates, which paves the way for the development of versatile phase-separating systems capable of addressing a variety of application scenarios. Here, we highlight the potential of constructing synthetic MLOs with programmable and functional properties. Notably, we organize how these synthetic membraneless compartments have been capitalized to manipulate enzymatic activities and metabolic reactions. The aim of this review is to inspire readerships to deeply comprehend the widespread roles of synthetic MLOs in the regulation enzymatic reactions and control of metabolic processes, and to encourage the rational design of controllable and functional membraneless compartments for a broad range of bioengineering applications.

Enhanced EphB2-Specific Peptide Inhibitors through Stabilization of Polyproline II Helical Structure

Ephrin (Eph) receptors are the largest family of receptor tyrosine kinases. Interactions between Eph receptors and their membrane-bound ephrin protein ligands are associated with many developmental processes as well as various cancers and neurodegenerative diseases. With significant crosstalk between different Eph receptors and ephrin ligands, there is an urgent need for high-affinity ligands that bind specifically to individual Eph receptors to interrogate and modulate their functions. Here, we describe the rational development of potent EphB2 receptor inhibitors derived from the EphB2 receptor-specific SNEW peptide. To improve inhibitory potency, we evaluated 20+ cross-linkers with the goal of spanning and stabilizing a single polyproline II helical turn observed when SNEW binds to the EphB2 receptor. Of the cross-linkers evaluated, an 11-atom cross-linker, composed of a rigid 2,7-dimethylnaphthyl moiety between two cysteine residues, was found to yield the most potent inhibitor. Analysis of the cyclized region of this peptide by NMR and molecular dynamics simulations suggests that cross-linking stabilizes the receptor-bound polyproline II helix structure observed in the receptor-peptide complex. Cross-linked SNEW variants retained binding specificity for EphB2 and showed cross-linker-dependent resistance to trypsin proteolysis. Beyond the discovery of more potent EphB2 receptor inhibitors, these studies illustrate a novel cyclization approach with potential to stabilize polyproline II helical structure in various peptides for specific targeting of the myriad protein-protein interactions (PPIs) mediated by polyproline II helices.

Folding and Binding Kinetics of the Tandem of SH2 Domains from SHP2

The SH2 domains of SHP2 play a crucial role in determining the function of the SHP2 protein. While the folding and binding properties of the isolated NSH2 and CSH2 domains have been extensively studied, there is limited information about the tandem SH2 domains. This study aims to elucidate the folding and binding kinetics of the NSH2-CSH2 tandem domains of SHP2 through rapid kinetic experiments, complementing existing data on the isolated domains. The results indicate that while the domains generally fold and unfold independently, acidic pH conditions induce complex scenarios involving the formation of a misfolded intermediate. Furthermore, a comparison of the binding kinetics of isolated NSH2 and CSH2 domains with the NSH2-CSH2 tandem domains, using peptides that mimic specific portions of Gab2, suggests a dynamic interplay between NSH2 and CSH2 in binding Gab2 that modulate the microscopic association rate constant of the binding reaction. These findings, discussed in the context of previous research on the NSH2 and CSH2 domains, enhance our understanding of the function of the SH2 domain tandem of SHP2.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Emerging and Re-emerging Warheads for Targeted Covalent Inhibitors: An Update

Covalent inhibitors and other types of covalent modalities have seen a revival in the past two decades, with a variety of new targeted covalent drugs having been approved in recent years. A key feature of such molecules is an intrinsically reactive group, typically a weak electrophile, which enables the irreversible or reversible formation of a covalent bond with a specific amino acid of the target protein. This reactive group, often called the "warhead", is a critical determinant of the ligand's activity, selectivity, and general biological properties. In 2019, we summarized emerging and re-emerging warhead chemistries to target cysteine and other amino acids (Gehringer, M.; Laufer, S. A. J. Med. Chem. 2019, 62, 5673-5724; DOI: 10.1021/acs.jmedchem.8b01153). Since then, the field has rapidly evolved. Here we discuss the progress on covalent warheads made since our last Perspective and their application in medicinal chemistry and chemical biology.

Peptide-Based Inhibitors of the Induced Signaling Protein Interactions: Current State and Prospects

Formation of the transient protein complexes in response to activation of cellular receptors is a common mechanism by which cells respond to external stimuli. This article presents the concept of blocking interactions of signaling proteins by the peptide inhibitors, and describes the progress achieved to date in the development of signaling inhibitors that act by blocking the signal-dependent protein interactions.

A K homology (KH) domain protein identified by a forward genetic screen affects bundle sheath anatomy in Arabidopsis thaliana

Because of their photosynthetic capacity, leaves function as solar panels providing the basis for the growth of the entire plant. Although the molecular mechanisms of leaf development have been well studied in model dicot and monocot species, a lot of information is still needed about the interplay of the genes that regulate cell division and differentiation and thereby affect the photosynthetic performance of the leaf. We were specifically interested in understanding the differentiation of mesophyll and bundle sheath cells in Arabidopsis thaliana and aimed to identify genes that are involved in determining bundle sheath anatomy. To this end, we established a forward genetic screen by using ethyl methanesulfonate (EMS) for mutagenizing a reporter line expressing a chloroplast-targeted green fluorescent protein (sGFP) under the control of a bundle sheath-specific promoter. Based on the GFP fluorescence phenotype, numerous mutants were produced, and by pursuing a mapping-by-sequencing approach, the genomic segments containing mutated candidate genes were identified. One of the lines with an enhanced GFP fluorescence phenotype (named ELEVATED BUNDLE SHEATH CELLS SIGNAL 1 [ebss1]) was selected for further study, and the responsible gene was verified by CRISPR/Cas9-based mutagenesis of candidate genes located in the mapped genomic segment. The verified gene, At2g25970, encodes a K homology (KH) domain-containing protein.

SMA20/PMIS2 Is a Rapidly Evolving Sperm Membrane Alloantigen with Possible Species-Divergent Function in Fertilization

Immunodominant alloantigens in pig sperm membranes include 15 known gene products and a previously undiscovered Mr 20,000 sperm membrane-specific protein (SMA20). Here we characterize SMA20 and identify it as the unannotated pig ortholog of PMIS2. A composite SMA20 cDNA encoded a 126 amino acid polypeptide comprising two predicted transmembrane segments and an N-terminal alanine- and proline (AP)-rich region with no apparent signal peptide. The Northern blots showed that the composite SMA20 cDNA was derived from a 1.1 kb testis-specific transcript. A BLASTp search retrieved no SMA20 match from the pig genome, but it did retrieve a 99% match to the Pmis2 gene product in warthog. Sequence identity to predicted PMIS2 orthologs from other placental mammals ranged from no more than 80% overall in Cetartiodactyla to less than 60% in Primates, with the AP-rich region showing the highest divergence, including, in the extreme, its absence in most rodents, including the mouse. SMA20 immunoreactivity localized to the acrosome/apical head of methanol-fixed boar spermatozoa but not live, motile cells. Ultrastructurally, the SMA20 AP-rich domain immunolocalized to the inner leaflet of the plasma membrane, the outer acrosomal membrane, and the acrosomal contents of ejaculated spermatozoa. Gene name search failed to retrieve annotated Pmis2 from most mammalian genomes. Nevertheless, individual pairwise interrogation of loci spanning Atp4a-Haus5 identified Pmis2 in all placental mammals, but not in marsupials or monotremes. We conclude that the gene encoding sperm-specific SMA20/PMIS2 arose de novo in Eutheria after divergence from Metatheria, whereupon rapid molecular evolution likely drove the acquisition of a species-divergent function unique to fertilization in placental mammals.

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.

Engineering approaches for RNA-based and cell-based osteoarthritis therapies

Osteoarthritis (OA) is a chronic, debilitating disease that substantially impairs the quality of life of affected individuals. The underlying mechanisms of OA are diverse and are becoming increasingly understood at the systemic, tissue, cellular and gene levels. However, the pharmacological therapies available remain limited, owing to drug delivery barriers, and consist mainly of broadly immunosuppressive regimens, such as corticosteroids, that provide only short-term palliative benefits and do not alter disease progression. Engineered RNA-based and cell-based therapies developed with synthetic chemistry and biology tools provide promise for future OA treatments with durable, efficacious mechanisms of action that can specifically target the underlying drivers of pathology. This Review highlights emerging classes of RNA-based technologies that hold potential for OA therapies, including small interfering RNA for gene silencing, microRNA and anti-microRNA for multi-gene regulation, mRNA for gene supplementation, and RNA-guided gene-editing platforms such as CRISPR-Cas9. Various cell-engineering strategies are also examined that potentiate disease-dependent, spatiotemporally regulated production of therapeutic molecules, and a conceptual framework is presented for their application as OA treatments. In summary, this Review highlights modern genetic medicines that have been clinically approved for other diseases, in addition to emerging genome and cellular engineering approaches, with the goal of emphasizing their potential as transformative OA treatments.

ELM-the Eukaryotic Linear Motif resource-2024 update

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.

A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model

MOTIVATION

Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.

RESULTS

In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.

AVAILABILITY AND IMPLEMENTATION

The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .

Cell Adhesion at the Tight Junctions: New Aspects and New Functions

Tight junctions (TJ) are cell-cell adhesive structures that define the permeability of barrier-forming epithelia and endothelia. In contrast to this seemingly static function, TJs display a surprisingly high molecular complexity and unexpected dynamic regulation, which allows the TJs to maintain a barrier in the presence of physiological forces and in response to perturbations. Cell-cell adhesion receptors play key roles during the dynamic regulation of TJs. They connect individual cells within cellular sheets and link sites of cell-cell contacts to the underlying actin cytoskeleton. Recent findings support the roles of adhesion receptors in transmitting mechanical forces and promoting phase separation. In this review, we discuss the newly discovered functions of cell adhesion receptors localized at the TJs and their role in the regulation of the barrier function.

Protein interaction network analysis of mTOR signaling reveals modular organization

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase that acts as a central mediator of translation and plays important roles in cell growth, synaptic plasticity, cancer, and a wide range of developmental disorders. The signaling cascade linking lipid kinases (phosphoinositide 3-kinases), protein kinases (AKT), and translation initiation complexes (EIFs) to mTOR has been extensively modeled, but does not fully describe mTOR system behavior. Here, we use quantitative multiplex coimmunoprecipitation to monitor a protein interaction network (PIN) composed of 300+ binary interactions among mTOR-related proteins. Using a simple model system of serum-deprived or fresh-media-fed mouse 3T3 fibroblasts, we observed extensive PIN remodeling involving 27+ individual protein interactions after 1 h, despite phosphorylation changes observed after only 5 min. Using small molecule inhibitors of phosphoinositide 3-kinase, AKT, mTOR, MEK and ERK, we define subsets of the PIN, termed "modules", that respond differently to each inhibitor. Using primary fibroblasts from individuals with overgrowth disorders caused by pathogenic PIK3CA or MTOR variants, we find that hyperactivation of mTOR pathway components is reflected in a hyperactive PIN. Our data define a "modular" organization of the mTOR PIN in which coordinated groups of interactions respond to the activation or inhibition of distinct nodes, and demonstrate that kinase inhibitors affect the modular network architecture in a complex manner, inconsistent with simple linear models of signal transduction.

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability

A ubiquitous feature of eukaryotic transcriptional regulation is cooperative self-assembly between transcription factors (TFs) and DNA cis-regulatory motifs. It is thought that this strategy enables specific regulatory connections to be formed in gene networks between otherwise weakly interacting, low-specificity molecular components. Here, using synthetic gene circuits constructed in yeast, we find that high regulatory specificity can emerge from cooperative, multivalent interactions among artificial zinc-finger-based TFs. We show that circuits "wired" using the strategy of cooperative TF assembly are effectively insulated from aberrant misregulation of the host cell genome. As we demonstrate in experiments and mathematical models, this mechanism is sufficient to rescue circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally inspired approach offers a simple, generalizable means for building high-fidelity, evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

The FHA domain protein ArnA functions as a global DNA damage response repressor in the hyperthermophilic archaeon Saccharolobus islandicus

Forkhead-associated (FHA) domain proteins specifically recognize phosphorylated threonine via the FHA domain and are involved in signal transduction in various processes especially DNA damage response (DDR) and cell cycle regulation in eukaryotes. Although FHA domain proteins are found in prokaryotes, archaea, and bacteria, their functions are far less clear as compared to the eukaryotic counterparts, and it has not been studied whether archaeal FHA proteins play a role in DDR. Here, we have characterized an FHA protein from the hyperthermophilic Crenarchaeon Saccharolobus islandicus (SisArnA) by genetic, biochemical, and transcriptomic approaches. We find that ΔSisarnA exhibits higher resistance to DNA damage agent 4-nitroquinoline 1-oxide (NQO). The transcription of ups genes, encoding the proteins for pili-mediated cell aggregation and cell survival after DDR, is elevated in ΔSisarnA. The interactions of SisArnA with two predicted partners, SisvWA1 (SisArnB) and SisvWA2 (designated as SisArnE), were enhanced by phosphorylation in vitro. ΔSisarnB displays higher resistance to NQO than the wild type. In addition, the interaction between SisArnA and SisArnB, which is reduced in the NQO-treated cells, is indispensable for DNA binding in vitro. These indicate that SisArnA and SisArnB work together to inhibit the expression of ups genes in vivo. Interestingly, ΔSisarnE is more sensitive to NQO than the wild type, and the interaction between SisArnA and SisArnE is strengthened after NQO treatment, suggesting a positive role of SisArnE in DDR. Finally, transcriptomic analysis reveals that SisArnA represses a number of genes, implying that archaea apply the FHA/phospho-peptide recognition module for extensive transcriptional regulation. IMPORTANCE Cellular adaption to diverse environmental stresses requires a signal sensor and transducer for cell survival. Protein phosphorylation and its recognition by forkhead-associated (FHA) domain proteins are widely used for signal transduction in eukaryotes. Although FHA proteins exist in archaea and bacteria, investigation of their functions, especially those in DNA damage response (DDR), is limited. Therefore, the evolution and functional conservation of FHA proteins in the three domains of life is still a mystery. Here, we find that an FHA protein from the hyperthermophilic Crenarchaeon Saccharolobus islandicus (SisArnA) represses the transcription of pili genes together with its phosphorylated partner SisArnB. SisArnA derepression facilitates DNA exchange and repair in the presence of DNA damage. The fact that more genes including a dozen of those involved in DDR are found to be regulated by SisArnA implies that the FHA/phosphorylation module may serve as an important signal transduction pathway for transcriptional regulation in archaeal DDR.

Peronophythora litchii RXLR effector P. litchii avirulence homolog 202 destabilizes a host ethylene biosynthesis enzyme

Oomycete pathogens can secrete hundreds of effectors into plant cells to interfere with the plant immune system during infection. Here, we identified a Arg-X-Leu-Arg (RXLR) effector protein from the most destructive pathogen of litchi (Litchi chinensis Sonn.), Peronophythora litchii, and named it P. litchii avirulence homolog 202 (PlAvh202). PlAvh202 could suppress cell death triggered by infestin 1 or avirulence protein 3a/resistance protein 3a in Nicotiana benthamiana and was essential for P. litchii virulence. In addition, PlAvh202 suppressed plant immune responses and promoted the susceptibility of N. benthamiana to Phytophthora capsici. Further research revealed that PlAvh202 could suppress ethylene (ET) production by targeting and destabilizing plant S-adenosyl-L-methionine synthetase (SAMS), a key enzyme in the ET biosynthesis pathway, in a 26S proteasome-dependent manner without affecting its expression. Transient expression of LcSAMS3 induced ET production and enhanced plant resistance, whereas inhibition of ET biosynthesis promoted P. litchii infection, supporting that litchi SAMS (LcSAMS) and ET positively regulate litchi immunity toward P. litchii. Overall, these findings highlight that SAMS can be targeted by the oomycete RXLR effector to manipulate ET-mediated plant immunity.

PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction

Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.

Protein interaction network analysis of mTOR signaling reveals modular organization

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase that acts as a central mediator of translation, and plays important roles in cell growth, synaptic plasticity, cancer, and a wide range of developmental disorders. The signaling cascade linking lipid kinases (PI3Ks), protein kinases (AKT) and translation initiation complexes (EIFs) to mTOR has been extensively modeled, but does not fully describe mTOR system behavior. Here, we use quantitative multiplex co-immunoprecipitation to monitor a protein interaction network (PIN) composed of 300+ binary interactions among mTOR-related proteins. Using a simple model system of serum deprived or fresh-media-fed mouse 3T3 fibroblasts, we observed extensive PIN remodeling involving 27+ individual protein interactions after one hour, despite phosphorylation changes observed after only five minutes. Using small molecule inhibitors of PI3K, AKT, mTOR, MEK and ERK, we define subsets of the PIN, termed 'modules', that respond differently to each inhibitor. Using primary fibroblasts from individuals with overgrowth disorders caused by pathogenic PIK3CA or MTOR variants, we find that hyperactivation of mTOR pathway components is reflected in a hyperactive PIN. Our data define a "modular" organization of the mTOR PIN in which coordinated groups of interactions respond to activation or inhibition of distinct nodes, and demonstrate that kinase inhibitors affect the modular network architecture in a complex manner, inconsistent with simple linear models of signal transduction.

Finding meaning in chaos: a selection signature for functional interactions and its use in molecular biology

A primary goal of biomedical research is to elucidate molecular mechanisms, particularly those responsible for human traits, either normal or pathological. Yet achieving this goal is difficult if not impossible when the traits of interest lack tractable models and so cannot be dissected through time-honoured approaches like forward genetics or reconstitution. Arguably, no biological problem has hindered scientific progress more than this: the inability to dissect a trait's mechanism without a tractable likeness of the trait. At root, forward genetics and reconstitution are powerful approaches because they assay for specific molecular functions. Here, we discuss an alternative way to uncover important mechanistic interactions, namely, to assay for positive natural selection. If an interaction has been selected for, then it must perform an important function, a function that significantly promotes reproductive success. Accordingly, selection is a consequence and indicator of function, and uncovering multimolecular selection will reveal important functional interactions. We propose a selection signature for interactions and review recent selection-based approaches through which to dissect traits that are not inherently tractable. The review includes proof-of-principle studies in which important interactions were uncovered by screening for selection. In sum, screens for selection appear feasible when screens for specific functions are not. Selection screens thus constitute a novel tool through which to reveal the mechanisms that shape the fates of organisms.

CMGC Kinases in Health and Cancer

CMGC kinases, encompassing cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAPKs), glycogen synthase kinases (GSKs), and CDC-like kinases (CLKs), play pivotal roles in cellular signaling pathways, including cell cycle regulation, proliferation, differentiation, apoptosis, and gene expression regulation. The dysregulation and aberrant activation of these kinases have been implicated in cancer development and progression, making them attractive therapeutic targets. In recent years, kinase inhibitors targeting CMGC kinases, such as CDK4/6 inhibitors and BRAF/MEK inhibitors, have demonstrated clinical success in treating specific cancer types. However, challenges remain, including resistance to kinase inhibitors, off-target effects, and the need for better patient stratification. This review provides a comprehensive overview of the importance of CMGC kinases in cancer biology, their involvement in cellular signaling pathways, protein-protein interactions, and the current state of kinase inhibitors targeting these kinases. Furthermore, we discuss the challenges and future perspectives in targeting CMGC kinases for cancer therapy, including potential strategies to overcome resistance, the development of more selective inhibitors, and novel therapeutic approaches, such as targeting protein-protein interactions, exploiting synthetic lethality, and the evolution of omics in the study of the human kinome. As our understanding of the molecular mechanisms and protein-protein interactions involving CMGC kinases expands, so too will the opportunities for the development of more selective and effective therapeutic strategies for cancer treatment.

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.

Bioinertization of nanoLC/MS/MS systems by depleting metal ions from the mobile phases for phosphoproteomics

We have successfully developed a bioinertized nanoflow liquid chromatography/tandem mass spectrometry (nanoLC/MS/MS) system for the highly sensitive analysis of phosphopeptides by depleting metal ions from the mobile phase. We found that not only direct contact of phosphopeptides with metal components, but also indirect contact with nanoLC pumps through the mobile phase causes significant losses during the recovery of phosphopeptides. Moreover, electrospray ionization was adversely affected by the mobile phase containing multiple metal ions as well as by the sample solvents contaminated with metal ions used in immobilized metal ion affinity chromatography for phosphopeptide enrichment. To solve these problems, metal ions were depleted by inserting an on-line metal ion removal device containing metal-chelating membranes between the gradient mixer and the autosampler. As a result, the peak areas of the identified phosphopeptides increased an average of 9.9-fold overall and 77-fold for multiply phosphorylated peptides with the insertion of the on-line metal ion removal system. This strategy would be applicable to highly sensitive analysis of other phosphorylated biomolecules by microscale-LC/MS/MS.

Targeting lipid-protein interaction to treat Syk-mediated acute myeloid leukemia

Membrane lipids control the cellular activity of kinases containing the Src homology 2 (SH2) domain through direct lipid-SH2 domain interactions. Here we report development of new nonlipidic small molecule inhibitors of the lipid-SH2 domain interaction that block the cellular activity of their host proteins. As a pilot study, we evaluated the efficacy of lipid-SH2 domain interaction inhibitors for spleen tyrosine kinase (Syk), which is implicated in hematopoietic malignancies, including acute myeloid leukemia (AML). An optimized inhibitor (WC36) specifically and potently suppressed oncogenic activities of Syk in AML cell lines and patient-derived AML cells. Unlike ATP-competitive Syk inhibitors, WC36 was refractory to de novo and acquired drug resistance due to its ability to block not only the Syk kinase activity, but also its noncatalytic scaffolding function that is linked to drug resistance. Collectively, our study shows that targeting lipid-protein interaction is a powerful approach to developing new small molecule drugs.

Regulation of eukaryotic protein kinases by Pin1, a peptidyl-prolyl isomerase

The peptidyl-prolyl isomerase Pin1 cooperates with proline-directed kinases and phosphatases to regulate multiple oncogenic pathways. Pin1 specifically recognizes phosphorylated Ser/Thr-Pro motifs in proteins and catalyzes their cis-trans isomerization. The Pin1-catalyzed conformational changes determine the stability, activity, and subcellular localization of numerous protein substrates. We conducted a survey of eukaryotic protein kinases that are regulated by Pin1 and whose Pin1 binding sites have been identified. Our analyses reveal that Pin1 target sites in kinases do not fall exclusively within the intrinsically disordered regions of these enzymes. Rather, they fall into three groups based on their location: (i) within the catalytic kinase domain, (ii) in the C-terminal kinase region, and (iii) in regulatory domains. Some of the kinases downregulated by Pin1 activity are tumor-suppressing, and all kinases upregulated by Pin1 activity are functionally pro-oncogenic. These findings further reinforce the rationale for developing Pin1-specific inhibitors as attractive pharmaceuticals for cancer therapy.

Lipid Binding of SH2 Domains

The Src homology 2 (SH2) domain is a modular protein interaction domain that specifically recognizes the phosphotyrosine (pY) motif of a target molecule. We recently reported that a large majority of human SH2 domains tightly bind membrane lipids, and many show high lipid specificity. Most of them can bind a lipid and the pY motif coincidently because their lipid-binding sites are topologically distinct from pY-binding pockets. Lipid binding of SH2 domain-containing kinases and phosphatases is functionally important because it exerts exquisite spatiotemporal control on protein-protein interaction and cell signaling activities mediated by these proteins. Here, we describe two assays, surface plasmon resonance analysis and fluorescence quenching analysis, which allow quantitative determination of the affinity and specificity of SH2-lipid interaction and high-throughput screening for SH2 domain-lipid-binding inhibitors.

Immunoprecipitation: Variations, Considerations, and Applications

Immunoprecipitation (IP) refers to methods of affinity chromatography that enrich and/or purify a specific protein from a complex mixture using a specific antibody immobilized on a solid support. Several operations and processes that are dependent on the isolation, concentration, and modification of proteins have seen improvement in their selectivity and separation based on the integration of IP-specific reactions into their workflows. This relatively simple principle has contributed significantly to our understanding of proteins and their behaviors and has become increasingly fundamental to most protein characterization studies today. In this chapter, we review the basic principles of IP and the several factors that influence each stage, and subsequently the success, of an IP experiment. Moreover, variations in application of the IP principle are discussed, and the adaptability of the techniques based on such is highlighted in the provision of two IP workflows to purify a particular protein from an entire cellular proteosome. These workflows cover the preparation and fractionation of crude cellular lysate into individual subcellular fractions, through to both "batch" and "column"-based extractions of the target protein of interest. Protocols for determining the validity of the workflows, and the presence/abundance of the protein of interest, are also briefly described.

SH2 Domains: Folding, Binding and Therapeutical Approaches

SH2 (Src Homology 2) domains are among the best characterized and most studied protein-protein interaction (PPIs) modules able to bind and recognize sequences presenting a phosphorylated tyrosine. This post-translational modification is a key regulator of a plethora of physiological and molecular pathways in the eukaryotic cell, so SH2 domains possess a fundamental role in cell signaling. Consequently, several pathologies arise from the dysregulation of such SH2-domains mediated PPIs. In this review, we recapitulate the current knowledge about the structural, folding stability, and binding properties of SH2 domains and their roles in molecular pathways and pathogenesis. Moreover, we focus attention on the different strategies employed to modulate/inhibit SH2 domains binding. Altogether, the information gathered points to evidence that pharmacological interest in SH2 domains is highly strategic to developing new therapeutics. Moreover, a deeper understanding of the molecular determinants of the thermodynamic stability as well as of the binding properties of SH2 domains appears to be fundamental in order to improve the possibility of preventing their dysregulated interactions.

Not your Mother's MAPKs: Apicomplexan MAPK function in daughter cell budding

Reversible phosphorylation by protein kinases is one of the core mechanisms by which biological signals are propagated and processed. Mitogen-activated protein kinases, or MAPKs, are conserved throughout eukaryotes where they regulate cell cycle, development, and stress response. Here, we review advances in our understanding of the function and biochemistry of MAPK signaling in apicomplexan parasites. As expected for well-conserved signaling modules, MAPKs have been found to have multiple essential roles regulating both Toxoplasma tachyzoite replication and sexual differentiation in Plasmodium. However, apicomplexan MAPK signaling is notable for the lack of the canonical kinase cascade that normally regulates the networks, and therefore must be regulated by a distinct mechanism. We highlight what few regulatory relationships have been established to date, and discuss the challenges to the field in elucidating the complete MAPK signaling networks in these parasites.

Multivalent binding kinetics resolved by fluorescence proximity sensing

Multivalent protein interactors are an attractive modality for probing protein function and exploring novel pharmaceutical strategies. The throughput and precision of state-of-the-art methodologies and workflows for the effective development of multivalent binders is currently limited by surface immobilization, fluorescent labelling and sample consumption. Using the gephyrin protein, the master regulator of the inhibitory synapse, as benchmark, we exemplify the application of Fluorescence proximity sensing (FPS) for the systematic kinetic and thermodynamic optimization of multivalent peptide architectures. High throughput synthesis of +100 peptides with varying combinatorial dimeric, tetrameric, and octameric architectures combined with direct FPS measurements resolved on-rates, off-rates, and dissociation constants with high accuracy and low sample consumption compared to three complementary technologies. The dataset and its machine learning-based analysis deciphered the relationship of specific architectural features and binding kinetics and thereby identified binders with unprecedented protein inhibition capacity; thus, highlighting the value of FPS for the rational engineering of multivalent inhibitors.

Fluorescence proximity sensing enables high throughput determination of binding affinities and kinetics of peptide inhibitors with varying valency and multivalent architecture.

Boronic Acid: A Novel Pharmacophore Targeting Src Homology 2 (SH2) Domain of STAT3

SH2 domains have been recognized as promising targets for various human diseases. However, targeting SH2 domains with phosphopeptides or small-molecule inhibitors derived from bioisosteres of the phosphate group is still challenging. Identifying novel bioisosteres of the phosphate group to achieve favorable in vivo potency is urgently needed. Here, we report the feasibility of targeting the STAT3-SH2 domain with a boronic acid group and the identification of a highly potent inhibitor compound 7 by replacing the carboxylic acid of compound 4 with a boronic acid. Compound 7 shows higher binding affinity, better cellular potency, more favorable PK profiles, and higher in vivo antitumor activity than 4. The stronger anticancer effect of 7 partially stems from its covalent binding mode with the SH2 domain, verified by the washout experiments. The relatively high level of sequence conservation among SH2 domains makes the results presented here of general significance.

Quantitative fragmentomics allow affinity mapping of interactomes

Human protein networks have been widely explored but most binding affinities remain unknown, hindering quantitative interactome-function studies. Yet interactomes rely on minimal interacting fragments displaying quantifiable affinities. Here, we measure the affinities of 65,000 interactions involving PDZ domains and their target PDZ-binding motifs (PBM) within a human interactome region particularly relevant for viral infection and cancer. We calculate interactomic distances, identify hot spots for viral interference, generate binding profiles and specificity logos, and explain selected cases by crystallographic studies. Mass spectrometry experiments on cell extracts and literature surveys show that quantitative fragmentomics effectively complements protein interactomics by providing affinities and completeness of coverage, putting a full human interactome affinity survey within reach. Finally, we show that interactome hijacking by the viral PBM of human papillomavirus E6 oncoprotein substantially impacts the host cell proteome beyond immediate E6 binders, illustrating the complex system-wide relationship between interactome and function.

Protein networks have been widely explored but most binding affinities remain unknown, limiting the quantitative interpretation of interactomes. Here the authors measure affinities of 65,000 interactions involving human PDZ domains and target sequence motifs relevant for viral infection and cancer.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Genetic variation associated with condensate dysregulation in disease

A multitude of cellular processes involve biomolecular condensates, which has led to the suggestion that diverse pathogenic mutations may dysregulate condensates. Although proof-of-concept studies have identified specific mutations that cause condensate dysregulation, the full scope of the pathological genetic variation that affects condensates is not yet known. Here, we comprehensively map pathogenic mutations to condensate-promoting protein features in putative condensate-forming proteins and find over 36,000 pathogenic mutations that plausibly contribute to condensate dysregulation in over 1,200 Mendelian diseases and 550 cancers. This resource captures mutations presently known to dysregulate condensates, and experimental tests confirm that additional pathological mutations do indeed affect condensate properties in cells. These findings suggest that condensate dysregulation may be a pervasive pathogenic mechanism underlying a broad spectrum of human diseases, provide a strategy to identify proteins and mutations involved in pathologically altered condensates, and serve as a foundation for mechanistic insights into disease and therapeutic hypotheses.

Signal-processing and adaptive prototissue formation in metabolic DNA protocells

The fundamental life-defining processes in living cells, such as replication, division, adaptation, and tissue formation, occur via intertwined metabolic reaction networks that process signals for downstream effects with high precision in a confined, crowded environment. Hence, it is crucial to understand and reenact some of these functions in wholly synthetic cell-like entities (protocells) to envision designing soft materials with life-like traits. Herein, we report on all-DNA protocells composed of a liquid DNA interior and a hydrogel-like shell, harboring a catalytically active DNAzyme, that converts DNA signals into functional metabolites that lead to downstream adaptation processes via site-selective strand displacement reactions. The downstream processes include intra-protocellular phenotype-like changes, prototissue formation via multivalent interactions, and chemical messenger communication between active sender and dormant receiver cell populations for sorted heteroprototissue formation. The approach integrates several tools of DNA-nanoscience in a synchronized way to mimic life-like behavior in artificial systems for future interactive materials.

The Molten Globule State of a Globular Protein in a Cell Is More or Less Frequent Case Rather than an Exception

Quite a long time ago, Oleg B. Ptitsyn put forward a hypothesis about the possible functional significance of the molten globule (MG) state for the functioning of proteins. MG is an intermediate between the unfolded and the native state of a protein. Its experimental detection and investigation in a cell are extremely difficult. In the last decades, intensive studies have demonstrated that the MG-like state of some globular proteins arises from either their modifications or interactions with protein partners or other cell components. This review summarizes such reports. In many cases, MG was evidenced to be functionally important. Thus, the MG state is quite common for functional cellular proteins. This supports Ptitsyn’s hypothesis that some globular proteins may switch between two active states, rigid (N) and soft (MG), to work in solution or interact with partners.

Distinct Transcriptional Profile of PDZ Genes after Activation of Human Macrophages and Dendritic Cells

The PDZ (PSD95, Dlg and ZO-1) genes encode proteins that primarily function as scaffolds of diverse signaling pathways. To date, 153 PDZ genes have been identified in the human genome, most of which have multiple protein isoforms widely studied in epithelial and neural cells. However, their expression and function in immune cells have been poorly studied. Herein, we aimed to assess the transcriptional profiles of 83 PDZ genes in human macrophages (Mɸ) and dendritic cells (DCs) and changes in their relative expression during cell PRR stimulation. Significantly distinct PDZ gene transcriptional profiles were identified under different stimulation conditions. Furthermore, a distinct PDZ gene transcriptional signature was found in Mɸ and DCs under the same phagocytic stimuli. Notably, more than 40 PDZ genes had significant changes in expression, with potentially relevant functions in antigen-presenting cells (APCs). Given that several PDZ proteins are targeted by viral products, our results support that many of these proteins might be viral targets in APCs as part of evasion mechanisms. Our results suggest a distinct requirement for PDZ scaffolds in Mɸ and DCs signaling pathways activation. More assessments on the functions of PDZ proteins in APCs and their role in immune evasion mechanisms are needed.

Predicting protein-peptide binding residues via interpretable deep learning

 

Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, they highly rely on third-party tools or information for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers)-based Contrastive Learning framework to predict the protein-Peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of designed features. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structure and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Our results highlight the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Interestingly, we demonstrate that peptide-binding residues in local sequential regions have more specific sequential patterns as compared with other protein-ligand binding residues, which potentially provides functional difference. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/.

AVAILABILITY

https://github.com/Ruheng-W/PepBCL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Anion-coordination-driven single-double helix switching and chiroptical molecular switching based on oligoureas

Synthetic foldamers with helical conformation are widely seen, but controllable interconversion amongst different geometries (helical structure and sense) is challenging. Here, a family of oligourea (tetra-, penta-, and hexa-) ligands bearing stereocenters at both ends are designed and shown to switch between single and double helices with concomitant inversion of helical senses upon anion coordination. The tetraurea ligand forms a right-handed single helix upon chloride anion (Cl-) binding and is converted into a left-handed double helix when phosphate anion (PO4 3-) is coordinated. The helical senses of the single and double helices are opposite, and the conversion is further found to be dependent on the stoichiometry of the ligand and phosphate anion. In contrast, only a single helix is formed for the hexaurea ligand with the phosphate anion. This distinction is attributed to the fact that the characteristic phosphate anion coordination geometry is satisfied by six urea moieties with twelve H-bonds. Our study revealed unusual single-double helix interconversion accompanied by unexpected chiroptical switching of helical senses.

Dephosphorylation accelerates the dissociation of ZAP70 from the T cell receptor

Src homology 2 (SH2) domains are phosphotyrosine binding motifs that play key roles in cellular signaling. There are 110 proteins in the human genome containing SH2 binding domains, of which 10 contain tandem SH2 domains. Tandem domains have been shown to improve avidity and specificity and contribute to allostery. Here, we show that tandem SH2 domains can also exhibit binding lifetimes that are accelerated by the activity of phosphatases. This accelerated unbinding requires tandem SH2 domains to engage their substrates in dynamic binding modes that cycle between single SH2-bound states. We experimentally confirm that this is the case for the well-studied kinase ZAP70 binding the T cell receptor. We suggest that accelerated unbinding is a general feature of signaling networks.

Protein–protein binding domains are critical in signaling networks. Src homology 2 (SH2) domains are binding domains that interact with sequences containing phosphorylated tyrosines. A subset of SH2 domain–containing proteins has tandem domains, which are thought to enhance binding affinity and specificity. However, a trade-off exists between long-lived binding and the ability to rapidly reverse signaling, which is a critical requirement of noise-filtering mechanisms such as kinetic proofreading. Here, we use modeling to show that the unbinding rate of tandem, but not single, SH2 domains can be accelerated by phosphatases. Using surface plasmon resonance, we show that the phosphatase CD45 can accelerate the unbinding rate of zeta chain–associated protein kinase 70 (ZAP70), a tandem SH2 domain–containing kinase, from biphosphorylated peptides from the T cell receptor (TCR). An important functional prediction of accelerated unbinding is that the intracellular ZAP70–TCR half-life in T cells will not be fixed but rather, dependent on the extracellular TCR–antigen half-life, and we show that this is the case in both cell lines and primary T cells. The work highlights that tandem SH2 domains can break the trade-off between signal fidelity (requiring long half-life) and signal reversibility (requiring short half-life), which is a key requirement for T cell antigen discrimination mediated by kinetic proofreading.

Recent Advances of Self-Healing Polymer Materials via Supramolecular Forces for Biomedical Applications

Noncovalent interactions can maintain the three-dimensional structures of biomacromolecules (e.g., polysaccharides and proteins) and control specific recognition in biological systems. Supramolecular chemistry was gradually developed as a result, and this led to design and application of self-healing materials. Self-healing materials have attracted attention in many fields, such as coatings, bionic materials, elastomers, and flexible electronic devices. Nevertheless, self-healing materials for biomedical applications have not been comprehensively summarized, even though many reports have been focused on specific areas. In this Review, we first introduce the different categories of supramolecular forces used in preparing self-healing materials and then describe biological applications developed in the last 5 years, including antibiofouling, smart drug/protein delivery, wound healing, electronic skin, cartilage lubrication protection, and tissue engineering scaffolds. Finally, the limitations of current biomedical applications are indicated, key design points are offered for new biological self-healing materials, and potential directions for biological applications are highlighted.

Protein-PAINT: Superresolution microscopy with signaling proteins

Superresolution techniques have advanced our understanding of complex cellular structures and processes but require the attachment of fluorophores to targets through tags or antibodies, which can be bulky and result in underlabeling. To overcome these limitations, we developed a technique to visualize the nanoscale binding locations of signaling proteins by taking advantage of their native interaction domains. Here, we demonstrated that pPAINT (protein point accumulation in nanoscale topography) is a new, single-molecule localization microscopy (SMLM) technique and used it to investigate T cell signaling by visualizing the Src homology 2 (SH2) domain, which is common in signaling molecules. When SH2 domain-containing proteins relocate to the plasma membrane, the domains selectively, transiently, and reversibly bind to preferred phosphorylated tyrosine residues on receptors. This transient binding yields the stochastic blinking events necessary for SMLM when observed with total internal reflection microscopy and enables quantification of binding coefficients in intact cells. We used pPAINT to reveal the binding sites of several T cell receptor-proximal signaling molecules, including Zap70, PI3K, Grb2, Syk, Eat2, and SHP2, and showed that the probes could be multiplexed. We showed that the binding half-life of the tandem SH2 domain of PI3K correlated with binding site cluster size at the immunological synapses of T cells, but that longer binding lifetimes were associated with smaller clusters for the monovalent SH2 domain of Eat2. These results demonstrate the potential of pPAINT for investigating phosphotyrosine-mediated signaling processes at the plasma membrane.