Search Results (124)

Multi-omics technologies enable parallel quantification of proteomic and metabolomic layers, yet enzyme abundance often shows weak or nonlinear correspondence under diverse biological conditions. This apparent discordance has been attributed to both technical limitations—such as dynamic range compression in LC-MS/MS, metabolite derivatization artifacts, and missing values in proteomic measurements—as well as intrinsic biological properties of metabolic network architecture. While technical factors contribute to cross-omic mismatch, accumulating evidence suggests that constraint-driven network behavior plays a major role in shaping this decoupling. Enzyme abundance constrains catalytic capacity; however, realized flux is selected within this capacity under distributed flux control, as formalized by flux control coefficients in metabolic control analysis, and is further modulated by enzyme kinetics (e.g., km and Vmax), post-translational modifications, substrate availability, and thermodynamic constraints. Metabolite pools, in turn, reflect the physicochemical state of the system, while specific metabolites can also act as regulatory effectors that modulate enzymatic activity and cellular signaling. Because metabolic networks are underdetermined, multiple flux configurations can satisfy identical protein abundance and metabolite concentration data. Static cross-layer correlation is therefore insufficient for mechanistic inference. We synthesize biological mechanisms—including post-translational regulation, allostery, thermodynamic buffering, spatial compartmentalization, feedback amplification, and redox gating—that weaken linear abundance–metabolite expectations. We further outline a constraint-based interpretation framework in which proteomics imposes capacity bounds, metabolomics informs reaction directionality and metabolite pool constraints, and flux-informed approaches reduce solution degeneracy by providing additional information on pathway activity. Moving beyond correlation requires integrating perturbation, temporal resolution, and constraint-aware modeling. Proteome–metabolome discordance should therefore be interpreted not as inconsistency, but as indicative of constraint-driven state selection within high-dimensional biochemical systems. Full article

The X-ray structural analysis of the N-terminal domain cavity from eleven transcription regulators (TFs) of the Fumarate Nitrate Reduction regulator/cAMP Regulator Protein family shows several significant trends. The conservancy of effector-binding phosphate binding cassette features in three TFs suggests a closer connection among them than the one obtained through the comparison of overall amino acid sequences. Conversely, there are also three clearly different allosteric activation mechanisms, which most likely evolved independently. Interestingly, several TFs of this family adopt the DNA-binding conformation without binding any ligand; instead, the buried region corresponding to the “allosteric” cavity is partially filled with salt bridges (which is also the case for two allosteric apo TFs). One plausible conclusion from these observations is that the non-allosteric TFs evolved from an allosteric counterpart and used salt bridges to fill and stabilize the formally polar ligand-binding cavity. O₂-sensing TFs share some residues in the relevant N-terminal domain cavity and might have had an already non-allosteric common ancestor. Full article

The catalytic mechanism of the Zn(II)-dependent chlorothalonil dehalogenase from Pseudomonas sp. CTN-3 (Chd) was examined using molecular dynamics (MD) simulations, Bayesian network analysis, and Markov state model analysis to quantify its motions. Chd selectively substitutes an aromatic chlorine-carbon bond in chlorothalonil (TPN; 2,4,5,6-tetrachloroisophtalonitrile) with an aromatic alcohol (4-hydroxytrichloro-isophthalonitrile; 4-OH-TPN). It is a homodimer with two solvent-accessible channels in each monomer, which are proposed to provide different routes for substrate and products to access/leave the catalytic Zn(II) site. Based on MD simulations, Chd exhibits allosteric behavior wherein a “Y”-shaped substrate channel exhibits a “flip flop” mechanism, where the “right” substrate channel opens to allow TPN to enter, after which it closes, followed by the “left” channel opening. The “right” channel then reopens, likely to allow the product, 4-OH-TPN, to leave the active site, but this reopening of the right channel drives the “left” channel to close. Coupled with the substrate channels alternately opening and closing, a corresponding possible Cl⁻ channel opens and closes. Although the dynamics of this process are fast, Chd needs to overcome a 5 kT free-energy barrier for this transition and to relax after opening. Additionally, exposed “wing” residues, hydrophilic residues at the ends of protruding α-helices, act as allosteric indicators, signaling the complex allosteric motions required to open the substrate channel. We propose, for the first time, a dynamic mechanism that drives substrate binding and product release, providing new insight into Chd’s catalytic mechanism. Full article

Antibiotic resistance is an increasing concern in modern healthcare. Therefore, it is important to identify novel antimicrobial agents and new molecular targets for such compounds. Here, we describe the identification of an N-pyridylpyrazolone derivative, 4-(2-aminoethyl)-2-(pyridin-2-yl)-1,2-dihydro-3H-pyrazol-3-one dihydrochloride (compound 1), which is effective against Gram-positive and Gram-negative bacteria and inhibits the enzymatic activity of Escherichia coli L-threonine dehydrogenase (TDH). To characterize its interaction with compound 1, TDH was overexpressed in E. coli. The recombinant enzyme was shown to exist in dilute solution in equilibrium between dimeric and tetrameric forms, with a K_d value for the dimer/tetramer transition of 3 ± 1 nM, and to bind L-threonine cooperatively with a Hill coefficient of 1.4. Compound 1 acted as a partial mixed inhibitor of TDH with an EC₅₀ value of 47 ± 16 µM and did not affect the equilibrium between oligomeric states. Altogether, these findings identify compound 1 as a promising starting point for the development of novel antibiotics and as a tool compound for studying the functional properties of TDH. Full article

From Galileo’s conviction that the Universe is written in the language of mathematics to the genomic and computational revolutions of the twentieth century, science has long sought to describe nature through external symbolic systems. However, mounting evidence across biology, physics, and cognitive science suggests that this reduction of semantics to syntax is insufficient. Life cannot be deterministically read only from DNA sequences, cognition cannot be reduced to just rule-based logic, and complex systems exhibit emergent behaviors that transcend symbolic description. We argue that these phenomena point toward an underappreciated principle, “the intelligence of matter”, whereby organized material systems inherently process information, adapt, and remember. Examples span from protein allostery and epigenetic memory to epitranscriptomic regulation, intelligent soft matter, and ecological reservoir computing. In these cases, computation emerges not from imposed codes but from the intrinsic dynamics of matter that is far from equilibrium. Recognizing intelligence as a general property of organized matter may inaugurate a new scientific style: one that deciphers the semantics of nature rather than superimposing ours and thus reshaping the epistemology of modern science. Full article

Understanding the atomistic basis of multi-layer mechanisms employed by broadly reactive neutralizing antibodies of the SARS-CoV-2 spike protein without directly blocking receptor engagement remains an important challenge in coronavirus immunology. Class 4 antibodies represent an intriguing case: they target a deeply conserved, cryptic epitope on the receptor-binding domain yet exhibit variable neutralization potency across subgroups F1 (CR3022, EY6A, COVA1-16), F2 (DH1047), and F3 (S2X259). The molecular basis for this variability is not fully understood. Here, we employed a multi-modal computational approach integrating atomistic and coarse-grained molecular dynamics simulations, binding free energy calculations, mutational scanning, and dynamic network analysis to elucidate how these antibodies engage the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and influence its function. Our results reveal that neutralization efficacy arises from the interplay of direct interfacial interactions and allosteric effects. Group F1 antibodies (CR3022, EY6A, COVA1-16) primarily operate via classic allostery, modulating flexibility in RBD loop regions to indirectly interfere with the ACE2 receptor binding through long-range effects. Group F2 antibody DH1047 represents an intermediate mechanism, combining partial steric hindrance—through engagement of ACE2-critical residues T376, R408, V503, and Y508—with significant allosteric influence, facilitated by localized communication pathways linking the epitope to the receptor interface. Group F3 antibody S2X259 achieves potent neutralization through a synergistic mechanism involving direct competition with ACE2 and localized allosteric stabilization, albeit with potentially increased escape vulnerability. Dynamic network analysis identified a conserved “allosteric ring” within the RBD core that serves as a structural scaffold for long-range signal propagation, with antibody-specific extensions modulating communication to the ACE2 interface. These findings support a model where Class 4 neutralization strategies evolve through the refinement of peripheral allosteric connections rather than epitope redesign. This study establishes a robust computational framework for understanding the atomistic basis of neutralization activity and immune escape for Class 4 antibodies, highlighting how the interplay of binding energetics, conformational dynamics, and allosteric modulation governs their effectiveness against SARS-CoV-2. Full article

Network theory analysis has emerged as a powerful approach for investigating the complex behavior of dynamic and interactive systems, including proteomic systems. One key application of these methods is the study of long-range signaling dynamics in proteins, a phenomenon known as allostery. In this study, we applied computational models using network theory analysis to explore long-range electrostatic interactions and allosteric drug rescue mechanisms in the DNA-binding domain (DBD) of the p53 protein, a critical tumor suppressor whose dysfunction, often caused by missense mutations, is implicated in over 50% of human cancers. Using heat kernel and Wasserstein distance-based analyses, we explored the allosteric behavior of p53-DBD constructs with the Y220C mutation in the presence or absence of allosteric effector drugs. Our results demonstrated that these network theory-based protocols effectively detected the differential efficacies of small molecule allosteric effector drug compounds in restoring long-range electrostatic dynamics in the Y220C mutant. Furthermore, our approach identified key long-range electrostatic interactions critical to both the nominal and drug-rescued functionality of the p53-DBD, providing valuable insights into allosteric modulation and its therapeutic potential. Full article

When globular proteins fold into their characteristic three-dimensional structures, some amino acids are located on the surface, while others are situated in the protein core, where they cannot interact with molecules in the environment. Predicting the degree of solubility of amino acids provides insight into the function and relevance of residues. Residue accessibility is crucial for several protein functions, including enzymatic activity, allostery, multimer formation, binding to other molecules, and immunogenicity. We developed a novel sequence-based predictor for amino acid accessibility with features derived from three-dimensional protein structures. Several machine learning algorithms were tested, and the long short-term memory (LSTM) deep learning method demonstrated the best performance; thus, it was utilized to develop the freely available SolAcc tool. It showed superior performance compared to state-of-the-art predictors in a blind test. Full article

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues that function as universal hotspots in KRAS recognition. KRAS residues I36, Y40, M67, and H95 consistently emerge as critical contributors to binding stability. Binding free energy computations show that, despite similar binding modes, K27 relies heavily on electrostatic contributions from major binding hotspots while K55 exploits a dense hydrophobic cluster enhancing its effector-mimetic signature. The allosteric binders K13 and K19, by contrast, stabilize a KRAS-specific pocket in the α3–loop–α4 motif, introducing new hinges and bottlenecks that rewire the communication architecture of KRAS without full immobilization. Network-based analysis reveals a strikingly consistent theme: despite their distinct mechanisms of recognition, all systems engage a unifying allosteric architecture that spans multiple functional motifs. This architecture is not only preserved across complexes but also mirrors the intrinsic communication framework of KRAS itself, where specific residues function as central hubs transmitting conformational changes across the protein. By integrating dynamic profiling, energetic mapping, and network modeling, our study provides a multi-scale mechanistic roadmap for targeting KRAS, revealing how engineered proteins can exploit both conserved motifs and isoform-specific features to enable precision modulation of KRAS signaling in oncogenic contexts. Full article

In our previous study, a taste sensor modified with 3-bromo-2,6-dihydroxybenzoic acid (3-Br-2,6-DHBA) exhibited significant responses to xanthine-based substances, suggesting an allosteric detection mechanism. This study investigates the potential of the 3-Br-2,6-DHBA-modified sensor membrane for detecting other drug classes. Eleven structurally diverse drugs—including caffeine, antibiotics, antivirals, analgesic-antipyretics from the WHO Model List of Essential Medicines for Children—were tested, as they were previously undetectable by a conventional bitterness sensor. Among them, amoxicillin, an oral broad-spectrum penicillin, and cefalexin, an oral cephalosporin, elicited significantly higher sensor responses when 3-Br-2,6-DHBA-modified membrane was used. To further examine this response, experiments were conducted using membranes modified with 3-Br-2,6-DHBA, 2,6-dihydroxybenzoic acid (2,6-DHBA), and benzoic acid. These tests confirmed that only 3-Br-2,6-DHBA-modified membrane produced significant responses to amoxicillin and cefalexin, suggesting that hydroxyl groups in 3-Br-2,6-DHBA contribute to allosteric effects via hydrogen bonding. Additional tests demonstrated higher responses for cefaclor and cefdinir, both oral cephalosporins. The interaction between 3-Br-2,6-DHBA and the beta-lactam ring, as well as adjacent five- or six-membered rings in amoxicillin and several oral cephalosporins, likely enables allosteric detection by stacking via π electron, hydrophobilc interaction, and hydrogen bonding. In conclusion, the 3-Br-2,6-DHBA-modified sensor membrane effectively detects amoxicillin and oral cephalosporins via allosteric mechanism. Full article

Background: Resveratrol has been shown to modulate stress-related anxiety by reducing brain monoamine oxidase A (MAO-A) activity. However, the molecular mechanism underlying this neurochemical effect remains unknown. In this study, we employed in silico approaches to investigate the binding affinity of resveratrol and its predominant blood metabolite, resveratrol glucuronide, to specific sites on MAO-A. Methods: For the in silico analysis, we employed molecular docking and molecular dynamics simulations. Within the liver–brain axis, we investigated the role of hepatic MAO-A in the development of anxiety. The activity of whole-brain MAO-A was compared with its activity in specific brain regions, including the amygdala, hippocampus, and prefrontal cortex. Results: Our findings suggest the presence of an allosteric site on the enzyme that accommodates these compounds. Furthermore, in vivo experiments demonstrated that high-dose resveratrol suppresses MAO activity not only in the brain but also in the liver of stress-exposed rats. The in vivo results are interpreted in the context of an allosteric site on MAO-A in both the brain and liver, which may mediate the interaction with resveratrol and its metabolite. Conclusions: The primary outcomes of the study include the identification of the role of hepatic MAO-A in the development of anxiety-like behavior, as well as the determination of resveratrol dose ranges at which it functions as an allosteric modulator of MAO-A activity. Full article

Allostery is a fundamental biological phenomenon that occurs when a molecule binds to a protein’s allosteric site, triggering conformational changes that regulate the protein’s activity. However, allostery in antibodies remains largely unexplored, and only a few reports have focused on allostery from antigen-binding fragments (Fab) to crystallizable fragments (Fc). But this study, using anti-phenobarbital antibodies—which are widely applied for detecting the potential health food adulterant phenobarbital—as a model and employing multiple computational methods, is the first to identify a cyclopeptide (cyclo[Link-M-WFRHY-K]) that induces allostery from Fc to Fab in antibody and elucidates the underlying antibody allostery mechanism. The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. Meanwhile, molecular dynamics simulations combined with other analytical methods demonstrated that cyclopeptide induces global conformational shifts in the antibody, which ultimately alter the Fab domain and enhance its antigen-binding activity from Fc to Fab. This result will enable cyclopeptides as a potential Fab-targeted allosteric modulator to provide a new strategy for the regulation of antigen-binding activity and contribute to the construction of novel immunoassays for food safety and other applications using allosteric antibodies as the core technology. Furthermore, graph theory analysis further revealed a common allosteric signaling pathway within the antibody, involving residues Q123, S207, S326, C455, A558, Q778, D838, R975, R1102, P1146, V1200, and K1286, which will be very important for the engineering design of the anti-phenobarbital antibodies and other highly homologous antibodies. Finally, the non-covalent interaction analysis showed that allostery from Fc to Fab primarily involves residue signal transduction driven by hydrogen bonds and hydrophobic interactions. Full article

RAS mutations occur in about 30% of human cancers, leading to enhanced RAS signaling and tumor growth. KRAS is the most commonly mutated oncogene in human tumors, especially lung, pancreatic, and colorectal cancers. Direct targeting of KRAS is difficult due to its highly conserved sequence; but, its complex with the guanine nucleotide exchange factor Son of Sevenless (SOS) 1 promises an attractive target for inhibiting RAS-mediated signaling. Here, we first revealed putative allosteric binding sites of the SOS1, KRASG12C-SOS1 complex, and the ternary KRASG13D-SOS1 complex structures using two network-based models, the essential site scanning analysis and the residue interaction network model. The results enabled us to identify two new putative allosteric pockets for the ternary KRASG13D-SOS1 complex. These were then screened together with the known ligand binding site against the natural compounds in the InterBioScreen (IBS) database using the Glide software package developed by Schrödinger, Inc. The docking poses of seven hit compounds were assessed using 400 ns long molecular dynamics (MD) simulations with two independent replicas using Desmond, coupled with thermal MM-GBSA calculations for the estimation of the binding free energy values. The structural skeleton of the seven proposed compounds consists of different functional groups and heterocyclic rings that possess anti-cancer activity and exhibit persistent interactions with key residues in binding pockets throughout the MD simulations. STOCK1N-09823 was determined as the most promising hit that promoted the disruption of the interactions R73 (chain A)/N879 and R73 (chain A)/Y884, which are key for SOS1-mediated KRAS activation. Full article

Perfringolysin O (PFO) is a prototypical member of a large family of pore-forming toxins (PFTs) that are potent virulence factors for many pathogenic bacteria. One of the most enigmatic properties of these PFTs is how structural changes are coordinated between different subunits within a single complex. Moreover, there are conflicting data in the literature, with gel electrophoresis results apparently showing that pores are only complete rings, whereas microscopy images clearly also show incomplete-ring pores. Here, we developed a novel multi-stack gel electrophoretic assay to finely separate PFO pore complexes and found that this assay indeed resolves both complete- and incomplete-ring pores. However, unexpectedly, we found that the stoichiometries of these complexes are predominantly integral multiples of six subunits. High-resolution atomic force microscopy images of PFO pore complexes also reveal a predominant hexameric-based stoichiometry. We also observed this hexameric-based stoichiometry at the prepore stage and identified a mutant that is kinetically trapped at a hexameric state. Thus, overall, these results reveal a previously unknown hexameric-based structural hierarchy in the PFO complexes. We suggest that the structural coordination within the hexamers is different than between the hexamers and is thus a critical feature of the structural coordination of the complex as a whole. Full article

A water-soluble arene ruthenium metalla-rectangle (MR1) functionalized with boronic acid groups was used to host various fluorescent dyes (fluorescein, eosin Y, and erythrosin B). These simple host–guest systems partially quench the natural fluorescence of the dyes, which can be regained in the presence of saccharides, phosphorylated molecules, and other analytes. The intensity of the regained fluorescence is directly linked to the nature of the analyte, and it shows some dose–response relationships with saccharides and phosphorylated molecules that are not compatible with a displacement assay, thus suggesting an allosteric mechanism. Interestingly, when fluorescein is trapped by the metalla-rectangle in the presence of D-fructose, half of the maximum fluorescence intensity is recovered at a fructose concentration of 17.2 ± 4.7 μM, while, for D-glucose, a concentration of 50.6 ± 2.5 μM is required for the same effect. Indeed, all combinations of analyte–host–dye (seven analytes, one host, three dyes) show a unique dose–response relationship in water at pH 8.0. However, in the presence of naphthalene and pyrene, fluorescein⸦MR1 shows a different behavior, acting as an indicator displacement assay with the full recovery of fluorescence. All data were analyzed by unsupervised machine learning technologies (PCA and cluster analysis), suggesting that such systems with multiple analyte–response behaviors are offering new perspectives for the development of highly sensitive, easily tunable, water-soluble, fluorescent-based sensing arrays for biomolecules and other analytes. Full article