Assembly of cell regulatory systems through protein interaction domains

Acyl-anchored metal-organic cages with interior cryptand-like recognition sites for selective removal of radioactive strontium(II)

Tailored design of organic linkers or metal nodes can introduce desirable functionalities into metal-organic cages (MOCs), significantly expanding their potential applications. In this study, we present a viable approach for engineering acyl-type metal nodes to create interior oxygen-rich sites within MOCs, enabling specific recognition of metal ions, including radioactive contaminants, while maintaining the structural integrity of the MOCs. A novel MOC featuring a uranyl-sealed calix[4]resorcinarene (C[4]R)-based multisite cavity, referred to as UOC, is synthesized as a prototype. In UOC, peroxide-bridged dimeric uranyl units at both ends of the coordination cage provide abundant oxygen sites for coordination, forming a cryptand-like cavity that facilitates efficient recognition and encapsulation of Sr2+ due to the size-matching effect. Additionally, hydrophobic binding cavities at both ends of UOC promote the co-inclusion of two different guest species. Inspired by the strong binding affinity of UOC for Sr2+, it is employed as a solid adsorbent for capturing low concentrations of Sr2+ in strontium-contaminated simulated groundwater. A removal efficiency of 99.7% for Sr2+ at an initial concentration as low as 0.013 mmol L-1 is achieved, demonstrating its significant potential for the selective removal of trace amounts of radioactive 90Sr2+. This work demonstrates the feasibility of incorporating acyl-type metal nodes into MOCs for the recognition and encapsulation of metal ions, offering a novel strategy for developing new MOC-based functional materials.

Structural Annotation Method for Locating sn- and C═C Positions of Lipids Using Liquid Chromatography-Electron Impact Excitation of Ions from Organics (EIEIO)-Mass Spectrometry

Definitive structural elucidation of lipids is pivotal for unraveling the functions of lipids in biological systems. Despite advancements in mass spectrometry (MS) for lipid analysis, challenges in annotation scope and efficiency remain, especially in resolving isomers. Herein, we introduce an optimized method using liquid chromatography coupled with electron impact excitation of ions from organic tandem mass spectrometry (LC-EIEIO-MS/MS) for comprehensive analysis and structural annotation of lipids. This approach integrates a six-step analytical protocol for precise lipid annotation, including (1) extracting MS information, (2) classifying lipids, (3) aligning sum composition, (4) determining sn-positions, (5) locating C═C positions, and (6) ascertaining annotation levels. In analyzing 34 lipid standards spiked into serum, our method achieved 100% and 82.4% annotation accuracy at the sn- and C═C isomer levels, respectively, compared to 26.5% and 0% in the CID mode using MS-DIAL. A total of 1312 sn-positions and 1033 C═C locations of lipids were annotated in quality control plasma pooled from healthy individuals and patients with Alzheimer's disease. The isomers of lipids revealed more pronounced differences between the healthy and diseased groups compared to the sum compositions of the lipids. Overall, the LC-EIEIO-MS/MS approach provides a comprehensive profiling and efficient annotation method for lipidomics, promising to shed new light on lipid-related biological pathways and disease mechanisms.

A quantitative intracellular peptide-binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as short linear motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called systematic intracellular motif-binding analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at noncore positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

Structural basis of promiscuous inhibition of Listeria virulence activator PrfA by oligopeptides

The facultative pathogen Listeria monocytogenes uses a master regulator, PrfA, to tightly control the fitness-costly expression of its virulence factors. We found that PrfA activity is repressed via competitive occupancy of the binding site for the PrfA-activating cofactor, glutathione, by exogenous nutritional oligopeptides. The inhibitory peptides show different sequence and physicochemical properties, but how such a wide variety of oligopeptides can bind PrfA was unclear. Using crystal structure analysis of PrfA complexed with inhibitory tri- and tetrapeptides, we show here that the binding promiscuity is due to the ability of PrfA β5 in the glutathione-binding inter-domain tunnel to establish parallel or antiparallel β sheet-like interactions with the peptide backbone. Spacious tunnel pockets provide additional flexibility for unspecific peptide accommodation while providing selectivity for hydrophobic residues. Hydrophobic contributions from two adjacent peptide residues appear to be critical for efficient PrfA inhibitory binding. In contrast to glutathione, peptide binding prevents the conformational change required for the correct positioning of the DNA-binding helix-turn-helix motifs of PrfA, effectively inhibiting virulence gene expression.

Multiplex mapping of protein-protein interaction interfaces

We describe peptide mapping through Sp lit A ntibiotic R esistance C omplementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host, and uses high throughput DNA sequencing results to infer the location of protein-protein interaction (PPI) interfaces. SpARC-map uses only routine microbiology techniques, with no reliance on specialized instrumentation or reconstituting protein complexes in vitro; it can be tuned to detect PPIs over a broad range of affinities; it can be multiplexed to probe multiple PPIs in parallel; its nonspecific background can be precisely measured, enabling the sensitive detection of weak PPIs. Using SpARC-map, we recover the known interface in the (p21-PCNA complex. We also use SpARC-map to probe the purinosome, the weakly bound complex of six purine biosynthetic enzymes, where no PPI interfaces are known. There, we identify interfaces that satisfy structural requirements for substrate channeling; we also identify protein surfaces that participate in multiple distinct interactions, which we validate using site-specific photocrosslinking in live human cells. Finally, we show that SpARC-map results can impose stringent constraints on outputs from machine learning based structure prediction.

PERspectives on circadian cell biology

Daily rhythms in the activities of PERIOD proteins are critical to the temporal regulation of mammalian physiology. While the molecular partners and genetic circuits that allow PERIOD to effect auto-repression and regulate transcriptional programmes are increasingly well understood, comprehension of the time-resolved mechanisms that allow PERIOD to conduct this daily dance is incomplete. Here, we consider the character and controversies of this central mammalian clock protein with a focus on its intrinsically disordered nature.This article is part of the Theo Murphy meeting issue 'Circadian rhythms in infection and immunity'.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks have a central role in how cells sense and respond to their environment. We engineered artificial phosphorylation networks in which reversible enzymatic phosphorylation cycles were assembled from modular protein domain parts and wired together to create synthetic phosphorylation circuits in human cells. Our design scheme enabled model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, and downstream connections can regulate gene expression. We engineered cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach that allows the design of signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Control Intracellular Protein Condensates with Light

Protein phase transitions are gaining traction among biologists for their wide-ranging roles in biological regulation. However, achieving precise control over these phenomena in vivo remains a formidable task. Optogenetic techniques present us with a potential means to control protein phase behavior with spatiotemporal precision. This review delves into the design of optogenetic tools, particularly those aimed at manipulating protein phase transitions in complex biological systems. We begin by discussing the pivotal roles of subcellular phase transitions in physiological and pathological processes. Subsequently, we offer a thorough examination of the evolution of optogenetic tools and their applications in regulating these protein phase behaviors. Furthermore, we highlight the tailored design of optogenetic tools for controlling protein phase transitions and the construction of synthetic condensates using these innovative techniques. In the long run, the development of optogenetic tools not only holds the potential to elucidate the roles of protein phase transitions in various physiological processes but also to antagonize pathological ones to reinstate cellular homeostasis, thus bringing about novel therapeutic strategies. The integration of optogenetic techniques into the study of protein phase transitions represents a significant step forward in our understanding and manipulation of biology at the subcellular level.

The conformation of the nSrc specificity-determining loop in the Src SH3 domain is modulated by a WX conserved sequence motif found in SH3 domains

Cellular signaling networks are modulated by multiple protein-protein interaction domains that coordinate extracellular inputs and processes to regulate cellular processes. Several of these domains recognize short linear motifs, or SLiMs, which are often highly conserved and are closely regulated. One such domain, the Src homology 3 (SH3) domain, typically recognizes proline-rich SLiMs and is one of the most abundant SLiM-binding domains in the human proteome. These domains are often described as quite versatile, and indeed, SH3 domains can bind ligands in opposite orientations dependent on target sequence. Furthermore, recent work has identified diverse modes of binding for SH3 domains and a wide variety of sequence motifs that are recognized by various domains. Specificity is often attributed to the RT and nSrc loops near the peptide-binding cleft in this domain family, particularly for Class I binding, which is defined as RT and nSrc loop interactions with the N-terminus of the ligand. Here, we used the Src and Abl SH3 domains as a model to further investigate the role of the RT and nSrc loops in SH3 specificity. We created chimeric domains with both the RT and nSrc loop sequences swapped between these SH3 domains, and used fluorescence anisotropy assays to test how relative binding affinities were affected for Src SH3- and Abl SH3-specific ligands. We also used Alphafold-Multimer to model our SH3:peptide complexes in combination with molecular dynamics simulations. We identified a position that contributes to the nSrc loop conformation in Src SH3, the amino acid immediately following a highly conserved Trp that creates a hydrophobic pocket critical for SH3 ligand recognition. We defined this as the WX motif, where X = Trp for Src and Cys for Abl. A broad importance of this position for modulating nSrc loop conformation in SH3 domains is suggested by analyses of previously deposited SH3 structures, multiple sequence alignment of SH3 domains in the human proteome, and our biochemical and computational data of mutant Src and Abl SH3 domains. Overall, our work uses experimental approaches and structural modeling to better understand specificity determinants in SH3 domains.

Pathway-directed recyclable chirality inversion of coordinated supramolecular polymers

It remains challenging to elucidate the fundamental mechanisms behind the dynamic chirality inversion of supramolecular assemblies with pathway complexity. Herein, metal coordination driven assembly systems based on pyridyl-conjugated cholesterol (PVPCC) and metal ions (Ag+ or Al3+) are established to demonstrate pathway-directed, recyclable chirality inversion and assembly polymorphism. In the Ag(I)/PVPCC system, a competitive pathway leads Ag-Complex to form either kinetically controlled supramolecular polymer (Ag-SP I) or thermodynamically favored Ag-SP II, accompanied by reversible chiroptical inversion. Conversely, the Al(III)/PVPCC system displays a solvent-assisted consecutive pathway: the Al-Complex initially forms ethanol-containing Al-SP II, and subsequently converts into ethanol-free Al-SP I with opposite chiroptical performance upon thermal treatment. Moreover, stable chirality inversion in the solid state enables potential dynamic circularly polarized luminescence encryption when Ag(I)/PVPCC is co-assembled with thioflavin T. These findings provide the guidance for the dynamic modulation of chirality functionality in supramolecular materials for applications in information processing, data encryption, and chiral spintronics.

A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as Short Linear Motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called Systematic Intracellular Motif Binding Analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants, and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method, and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at non-core positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

mInsc coordinates Par3 and NuMA condensates for assembly of the spindle orientation machinery in asymmetric cell division

As a fundamental process governing the self-renewal and differentiation of stem cells, asymmetric cell division is controlled by several conserved regulators, including the polarity protein Par3 and the microtubule-associated protein NuMA, which orchestrate the assembly and interplay of the Par3/Par6/mInsc/LGN complex at the apical cortex and the LGN/Gαi/NuMA/Dynein complex at the mitotic spindle to ensure asymmetric segregation of cell fate determinants. However, this model, which is well-supported by genetic studies, has been challenged by evidence of competitive interaction between NuMA and mInsc for LGN. Here, the solved crystal structure of the Par3/mInsc complex reveals that mInsc competes with Par6β for Par3, raising questions about how proteins assemble overlapping targets into functional macromolecular complexes. Unanticipatedly, we discover that Par3 can recruit both Par6β and mInsc by forming a dynamic condensate through phase separation. Similarly, the phase-separated NuMA condensate enables the coexistence of competitive NuMA and mInsc with LGN in the same compartment. Bridge by mInsc, Par3/Par6β and LGN/NuMA condensates coacervate, robustly enriching all five proteins both in vitro and within cells. These findings highlight the pivotal role of protein condensates in assembling multi-component signalosomes that incorporate competitive protein-protein interaction pairs, effectively overcoming stoichiometric constraints encountered in conventional protein complexes.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model

The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an encoder-decoder structure. This model's high precision in identifying binding sites significantly advances the field of computational biology, offering vital insights for peptide drug development and protein science.

Spatiotemporal Communication in Artificial Cell Consortia for Dynamic Control of DNA Nanostructures

The spatiotemporal orchestration of cellular processes is a ubiquitous phenomenon in pluricellular organisms and bacterial communities, where sender cells secrete chemical signals that activate specific pathways in distant receivers. Despite its importance, the engineering and investigation of spatiotemporal communication in artificial cell consortia remains underexplored. In this study, we present spatiotemporal communication between cellular-scale entities acting as both senders and receivers. The transmitted signals are leveraged to elicit conformational alterations within compartmentalized DNA structures. Specifically, sender entities control and generate diffusive chemical signals, namely, variations in pH, through the conversion of biomolecular inputs. In the receiver population, compartmentalized DNA nanostructures exhibit changes in conformation, transitioning between triplex and duplex assemblies, in response to this pH variation. We demonstrate the temporal regulation of activated DNA nanostructures through the coordinated action of two antagonistic sender populations. Furthermore, we illustrate the transient distance-dependent activation of the receivers, facilitated by sender populations situated at defined spatial locations. Collectively, our findings provide novel avenues for the design of artificial cell consortia endowed with programmable spatiotemporal dynamics through chemical communication.

Enzyme Is the Name-Adapter Is the Game

Signaling proteins in eukaryotes usually comprise a catalytic domain coupled to one or several interaction domains, such as SH2 and SH3 domains. An additional class of proteins critically involved in cellular communication are adapter or scaffold proteins, which fulfill their purely non-enzymatic functions by organizing protein-protein interactions. Intriguingly, certain signaling enzymes, e.g., kinases and phosphatases, have been demonstrated to promote particular cellular functions by means of their interaction domains only. In this review, we will refer to such a function as "the adapter function of an enzyme". Though many stories can be told, we will concentrate on several proteins executing critical adapter functions in cells of the immune system, such as Bruton´s tyrosine kinase (BTK), phosphatidylinositol 3-kinase (PI3K), and SH2-containing inositol phosphatase 1 (SHIP1), as well as in cancer cells, such as proteins of the rat sarcoma/extracellular signal-regulated kinase (RAS/ERK) mitogen-activated protein kinase (MAPK) pathway. We will also discuss how these adaptor functions of enzymes determine or even undermine the efficacy of targeted therapy compounds, such as ATP-competitive kinase inhibitors. Thereby, we are highlighting the need to develop pharmacological approaches, such as proteolysis-targeting chimeras (PROTACs), that eliminate the entire protein, and thus both enzymatic and adapter functions of the signaling protein. We also review how genetic knock-out and knock-in approaches can be leveraged to identify adaptor functions of signaling proteins.

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.

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.

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.

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.