Molecular insights into kaempferol derivatives as potential inhibitors for CDK2 in colon cancer: pharmacophore modeling, docking, and dynamic analysis.

Cyclin-dependent kinase 2 (CDK2) has been recognized as one of the crucial factors in cell cycle regulation and has been proposed as a potential target for cancer therapies, particularly for colorectal cancer (CRC). Due to the increased incidence rate of CRC and challenges associated with existing treatment options, there is a need for efficient and selective anti-cancer compounds. The current work aims to explore the ability of novel kaempferol derivatives as CDK2 inhibitors by performing conceptual pharmacophore modeling, molecular docking, and molecular dynamic analysis. Kaempferol and its derivatives were obtained from PubChem, and the optimized 3D structures of the compounds were generated using Maestro Ligprep. Subsequently, a pharmacophore model was developed to identify compounds with high fitness values, resulting in the selection of several kaempferol derivatives for further study. We evaluated the ADMET properties of these compounds to assess their therapeutic potential. Molecular docking was conducted using Maestro and BIOVIA Discovery Studio version 4.0 to predict the binding affinities of the compounds to CDK2. The top candidates were subjected to MM-GBSA analysis to predict their binding free energies. Molecular dynamics simulations using GROMACS were performed to assess the thermodynamic stability of the ligand-protein complexes. The results revealed several kaempferol derivatives with high predicted binding affinities to CDK2 and favorable ADMET properties. Specifically, compounds 5281642, 5318980, and 14427423 demonstrated binding free energies of -30.26, -38.66, and -34.2 kcal/mol, respectively. Molecular dynamics simulations indicated that these ligand-protein complexes remained stable throughout the simulation period, with RMSD values remaining below 2 Å. In conclusion, the identified kaempferol derivatives show potential as CDK2 inhibitors based on computational predictions and demonstrate stability in molecular dynamics simulations, suggesting their future application in CRC treatment by targeting CDK2. These computational findings encourage further experimental validation and development of kaempferol derivatives as anti-cancer agents.

cPEPmatch Webserver: A comprehensive tool and database to aid rational design of cyclic peptides for drug discovery.

Cyclic peptides have emerged as versatile scaffolds in drug discovery due to their stability and specificity. Here, we present the cPEPmatch webserver (accessible at https://t38webservices.nat.tum.de/cpepmatch/), an easy-to-use interface for the rational design of cyclic peptides targeting protein-protein interactions combined with a semi-quantitative evaluation of binding stability. This platform also offers access to a comprehensive database of cyclic peptide crystal structures. We demonstrate the webserver's utility through a series of case studies involving medically relevant protein systems, highlighting its potential to significantly advance drug discovery efforts.

Metal ions in biomedically relevant macromolecular structures.

Understanding the functions of metal ions in biological systems is crucial for many aspects of research, including deciphering their roles in diseases and potential therapeutic use. Structural information about the molecular or atomic details of these interactions, generated by methods like X-ray crystallography, cryo-electron microscopy, or nucleic magnetic resonance, frequently provides details that no other method can. As with any experimental method, they have inherent limitations that sometimes lead to an erroneous interpretation. This manuscript highlights different aspects of structural data available for metal-protein complexes. We examine the quality of modeling metal ion binding sites across different structure determination methods, where different kinds of errors stem from, and how they can impact correct interpretations and conclusions.

Protein assemblies in the Arabidopsis thaliana chloroplast compartment.

Introduction: Equipped with a photosynthetic apparatus that uses the energy of solar radiation to fuel biosynthesis of organic compounds, chloroplasts are the metabolic factories of mature leaf cells. The first steps of energy conversion are catalyzed by a collection of protein complexes, which can dynamically interact with each other for optimizing metabolic efficiency under changing environmental conditions. Materials and methods: For a deeper insight into the organization of protein assemblies and their roles in chloroplast adaption to changing environmental conditions, an improved complexome profiling protocol employing a MS-cleavable cross-linker is used to stabilize labile protein assemblies during the organelle isolation procedure. Results and discussion: Changes in protein:protein interaction patterns of chloroplast proteins in response to four different light intensities are reported. High molecular mass assemblies of central chloroplast electron transfer chain components as well as the PSII repair machinery react to different light intensities. In addition, the chloroplast encoded RNA-polymerase complex was found to migrate at a molecular mass of ~8 MDa, well above its previously reported molecular mass. Complexome profiling data produced during the course of this study can be interrogated by interested readers via a web-based online resource (https://complexomemap.de/projectsinteraction-chloroplasts).

6'-Sialyllactose Alleviates Muscle Fatigue through Reduced Blood Lactate Level after Treadmill Exercise in Mice.

6'-Sialyllactose (6'-SL), found in human breast milk, exhibits anti-inflammatory, immune function-enhancing, brain development-promoting, and gut health-improving effects. However, its effects on muscle fatigue remain unknown. Here, we aimed to investigate the effects of 6'-SL on blood lactate level, muscle fiber type, and oxidative phosphorylation protein complexes (OXPHOS) in muscle after exercise using C57BL/6J male mice. C57BL/6J mice were randomly assigned to control or 100 mg/kg 6'-SL. After 12 weeks of 6'-SL administration, the mice were made to perform treadmill exercise; their blood lactate and glucose levels were measured at the basal level (rest) and 0, 5, and 10 min after treadmill exercise. Results showed that 6'-SL treatment in C57BL/6J mice significantly reduced blood lactate level and improved blood glucose level. Moreover, 6'-SL increased the expression of slow-myosin heavy chain (MHC) and OXPHOS in gastrocnemius muscle. In addition, 6'-SL treatment for 12 weeks did not affect food intake, serum biomarkers of tissue injury, and lipid profiles compared with those of the controls. These findings indicate that non-toxic 6'-SL suppressed muscle fatigue during exercise by promoting protein expression of muscle fibers, especially slow-twitch muscle fibers characterized by abundant OXPHOS complexes and decreased blood lactate level. This study suggests that 6'-SL holds promise as a nutritional supplement in exercise and clinical settings, subject to further validation.

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.

Key structural factors that determine the in vitro enzymatic digestibility of amylose-complexes.

The effects of complexing conditions on the formation of amylose-lipid-protein complexes and relationships between structure and digestion of amylose-lipid and amylose-lipid-protein complexes were poorly understood. The objective of this study was to investigate the effects of complexing time (0, 0.5, 2, 4 and 6 h) and temperature (60, 70, 80, 90 and 100 °C) on the structure and in vitro amylolysis of amylose-lauric acid (AM-LA) and amylose-lauric acid-ß-lactoglobulin (AM-LA-ßLG) complexes, and to understand the relationships between structure and in vitro digestiblity of these complexes. Longer complexing time and higher complexing temperature promoted the formation of greater amounts of the more stable type II crystallites than type I crystallites in both AM-LA and AM-LA-ßLG complexes, which in turn decreased the rate and extent of the complexes digestion to a greater extent. Correlation analyses between parameters for structure and digestion kinetics showed that both the quantity of AM-LA and AM-LA-ßLG complexes and the quality of their arrangement into V-type crystallites influenced their rate and extent of digestion. This study demonstrates that AM-LA and AM-LA-ßLG complexes can be prepared with designed structural and functional properties tailored for various applications.

Role of adaptor protein complexes in generating functionally distinct synaptic vesicle pools.

The synaptic vesicle (SV) cycle ensures the release of neurotransmitters and the replenishment of SVs to sustain neuronal activity. Multiple endocytosis and sorting pathways contribute to the recapture of the SV membrane and proteins after fusion. Adaptor protein (AP) complexes are among the critical components of the SV retrieval machinery. The canonical clathrin adaptor AP2 ensures the replenishment of most SVs across many neuronal populations. An alternative AP1/AP3-dependent process mediates the formation of a subset of SVs that differ from AP2 vesicles in molecular composition and respond preferentially during higher frequency firing. Furthermore, recent studies show that vesicular transporters for different neurotransmitters depend to a different extent on the AP3 pathway and this affects the release properties of the respective neurotransmitters. This review focuses on the current understanding of the AP-dependent molecular and functional diversity among SVs. We also discuss the contribution of these pathways to the regulation of neurotransmitter release across neuronal populations.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations.

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Nucleic acid-binding KH domain proteins influence a spectrum of biological pathways including as part of membrane-localized complexes.

K-Homology domain (KH domain) proteins bind single-stranded nucleic acids, influence protein-protein interactions of proteins that harbor them, and are found in all kingdoms of life. In concert with other functional protein domains KH domains contribute to a variety of critical biological activities, often within higher order machineries including membrane-localized protein complexes. Eukaryotic KH domain proteins are linked to developmental processes, morphogenesis, and growth regulation, and their aberrant expression is often associated with cancer. Prokaryotic KH domain proteins are involved in integral cellular activities including cell division and protein translocation. Eukaryotic and prokaryotic KH domains share structural features, but are differentiated based on their structural organizations. In this review, we explore the structure/function relationships of known examples of KH domain proteins, and highlight cases in which they function within or at membrane surfaces. We also summarize examples of KH domain proteins that influence bacterial virulence and pathogenesis. We conclude the article by discussing prospective research avenues that could be pursued to better investigate this largely understudied protein category.

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. RESULTS: Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

Evaluation of a Commercial TIMS-Q-TOF Platform for Native Mass Spectrometry.

Mass-spectrometry based assays in structural biology studies measure either intact or digested proteins. Typically, different mass spectrometers are dedicated for such measurements: those optimized for rapid analysis of peptides or those designed for high molecular weight analysis. A commercial trapped ion mobility-quadrupole-time-of-flight (TIMS-Q-TOF) platform is widely utilized for proteomics and metabolomics, with ion mobility providing a separation dimension in addition to liquid chromatography. The ability to perform high-quality native mass spectrometry of protein complexes, however, remains largely uninvestigated. Here, we evaluate a commercial TIMS-Q-TOF platform for analyzing noncovalent protein complexes by utilizing the instrument's full range of ion mobility, MS, and MS/MS (both in-source activation and collision cell CID) capabilities. The TIMS analyzer is able to be tuned gently to yield collision cross sections of native-like complexes comparable to those previously reported on various instrument platforms. In-source activation and collision cell CID were robust for both small and large complexes. TIMS-CID was performed on protein complexes streptavidin (53 kDa), avidin (68 kDa), and cholera toxin B (CTB, 58 kDa). Complexes pyruvate kinase (237 kDa) and GroEL (801 kDa) were beyond the trapping capabilities of the commercial TIMS analyzer, but TOF mass spectra could be acquired. The presented results indicate that the commercial TIMS-Q-TOF platform can be used for both omics and native mass spectrometry applications; however, modifications to the commercial RF drivers for both the TIMS analyzer and quadrupole (currently limited to m/z 3000) are necessary to mobility analyze protein complexes greater than about 60 kDa.

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures.

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

Transient Structural Properties of the Rho GDP-Dissociation Inhibitor.

Rho GTPases, master spatial regulators of a wide range of cellular processes, are orchestrated by complex formation with guanine nucleotide dissociation inhibitors (RhoGDIs). These have been thought to possess an unstructured N-terminus that inhibits nucleotide exchange of their client upon binding/folding. Via NMR analyses, molecular dynamics simulations, and biochemical assays, we reveal instead pertinent structural properties transiently maintained both, in the presence and absence of the client, imposed onto the terminus context-specifically by modulating interactions with the surface of the folded C-terminal domain. These observations revise the long-standing textbook picture of the GTPases' mechanism of membrane extraction. Rather than by a disorder-to-order transition upon binding of an inhibitory peptide, the intricate and highly selective extraction process of RhoGTPases is orchestrated via a dynamic ensemble bearing preformed transient structural properties, suitably modulated by the specific surrounding along the multi-step process.

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC.

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

A prediction method of interaction based on Bilinear Attention Networks for designing polyphenol-protein complexes delivery systems.

Polyphenol-protein complexes delivery systems are gaining attention for their potential health benefits and food industry development. However, creating an ideal delivery system requires extensive wet-lab experimentation. To address this, we collected 525 ligand-protein interaction data pairs and established an interaction prediction model using Bilinear Attention Networks. We utilized 10-fold cross validation to address potential overfitting issues in the model, resulting in showed higher average AUROC (0.8443), AUPRC (0.7872), and F1 (0.8164). The optimal threshold (0.3739) was selected for the model to be used for subsequent analysis. Based on the model prediction results and optimal threshold, by verifying experimental analysis, the interaction of paeonol with the following proteins was obtained, including bovine serum albumin (lgKa = 6.2759), bovine ß-lactoglobulin (lgKa = 6.7479), egg ovalbumin (lgKa = 5.1806), zein (lgKa = 6.0122), bovine α-lactalbumin (lgKa = 3.9170), bovine lactoferrin (lgKa = 4.5380), the first four proteins are consistent with the predicted results of the model, with lgKa >5. The established model can accurately and rapidly predict the interaction of polyphenol-protein complexes. This study is the first to combine open ligand-protein interaction experiments with Deep Learning algorithms in the food industry, greatly improving research efficiency and providing a novel perspective for future complex delivery system construction.

Gastrointestinal digestion behavior and bioavailability of greenly prepared highly loaded myofibrillar-luteolin vehicle.

In this study, the highly loaded myofibrillar protein (MP)-luteolin (Lut) complexes were noncovalently constructed by using green high-pressure homogenization technology (HPH) and high-pressure micro-fluidization technology (HPM), aiming to optimize the encapsulation efficiency of flavonoids in the protein-based vehicle without relying on the organic solvent (i.e. DMSO, ethanol, etc.). The loading efficiency of Lut into MPs could reach 100 % with a concentration of 120 µmol/g protein by using HPH (103 MPa, 2 passes) without ethanol adoption. The in vitro gastrointestinal digestion behavior and antioxidant activity of the complexes were then compared with those of ethanol-assisted groups. During gastrointestinal digestion, the MP digestibility of complexes, reaching more than 70.56 % after thermal treatment, was higher than that of sole protein. The release profile of Lut encapsulated in ethanol-containing and ethanol-free samples both well fitted with the Hixson-Crowell release kinetic model (R2 = 0.92 and 0.94, respectively), and the total phenol content decreased by ≥ 40.02 % and ≥ 62.62 %, respectively. The in vitro antioxidant activity (DPPH, ABTS, and Fe2+) of the digestive products was significantly improved by 23.89 %, 159.69 %, 351.12 % (ethanol groups) and 13.43 %, 125.48 %, 213.95 % (non-ethanol groups). The 3 mg/mL freeze-dried digesta significantly alleviated lipid accumulation and oxidative stress in HepG2 cells. The triglycerides and malondialdehyde contents decreased by at least 57.62 % and 67.74 % after digesta treatment. This study provides an easily approached and environment-friendly strategy to construct a highly loaded protein-flavonoid conjugate, which showed great potential in the formulation of healthier meat products.

Purification of In Vivo or In Vitro-Assembled RNA-Protein Complexes by RNA Centric Methods.

Throughout their entire life cycle, RNAs are associated with RNA-binding proteins (RBPs), forming ribonucleoprotein (RNP) complexes with highly dynamic compositions and very diverse functions in RNA metabolism, including splicing, translational regulation, ribosome assembly. Many RNPs remain poorly characterized due to the challenges inherent in their purification and subsequent biochemical characterization. Therefore, developing methods to isolate specific RNA-protein complexes is an important initial step toward understanding their function. Many elegant methodologies have been developed to isolate RNPs. This chapter describes different approaches and methods devised for RNA-specific purification of a target RNP. We focused on general methods for selecting RNPs that target a given RNA under conditions favourable for the copurification of associated factors including RNAs and protein components of the RNP.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

The ABC toxin complex from Yersinia entomophaga can package three different cytotoxic components expressed from distinct genetic loci in an unfolded state: the structures of both shell and cargo.

Bacterial ABC toxin complexes (Tcs) comprise three core proteins: TcA, TcB and TcC. The TcA protein forms a pentameric assembly that attaches to the surface of target cells and penetrates the cell membrane. The TcB and TcC proteins assemble as a heterodimeric TcB-TcC subcomplex that makes a hollow shell. This TcB-TcC subcomplex self-cleaves and encapsulates within the shell a cytotoxic `cargo' encoded by the C-terminal region of the TcC protein. Here, we describe the structure of a previously uncharacterized TcC protein from Yersinia entomophaga, encoded by a gene at a distant genomic location from the genes encoding the rest of the toxin complex, in complex with the TcB protein. When encapsulated within the TcB-TcC shell, the C-terminal toxin adopts an unfolded and disordered state, with limited areas of local order stabilized by the chaperone-like inner surface of the shell. We also determined the structure of the toxin cargo alone and show that when not encapsulated within the shell, it adopts an ADP-ribosyltransferase fold most similar to the catalytic domain of the SpvB toxin from Salmonella typhimurium. Our structural analysis points to a likely mechanism whereby the toxin acts directly on actin, modifying it in a way that prevents normal polymerization.