MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories

Biomolecular conformational changes and transient druggable binding sites through full-length AMPK molecular dynamics simulations

AMPK (AMP-activated protein kinase) is a crucial signaling protein found in essentially all eukaryotic organisms and acts as an energy sensor. When activated by metabolic stress, AMPK phosphorylates a variety of molecular targets, altering enzyme activity and gene expression to regulate cellular responses. In general, in response to low intracellular ATP levels (high ADP:ATP ratio), AMPK triggers the activation of energy-producing pathways while simultaneously inhibiting energy-consuming processes. Recent studies have established a connection between molecular pathways involved in sensing energy and potential for extending longevity. AMPK indirect activator compounds have shown a potential strategy to obtain an anti-aging biological activity. This study explores the conformational changes and transient druggable binding pockets over the 1 μs trajectory of molecular dynamics simulations to comprehend the behavior of main domains and allosteric drug and metabolite (ADaM) site. The described conformations of the apo-ADaM site suggest an important influence of specific residues on the cavity volume variations. A clustering set of representative AMPK conformations allowed to identify the more favorable binding site volume and shape at the protein apo form, including the carbohydrate-binding module (CBM) region which exhibited a stable movement near the ADaM site of the alpha-subunit. The identification of gamma-subunit transient druggable binding pocket CBS3 during the microscale time trajectory simulations also offers valuable insights into structure-based AMP-mimetic drug design for AMPK activation.

PocketSCP: A Method for Spatiotemporal Topological Visualization and Analysis of Protein Pocket Dynamics

The identification and analysis of pockets are crucial for understanding the functional mechanisms and therapeutic potential of proteins. However, it is challenging to track the dynamic characteristics of the pockets. In this paper, we present a method for the visualization and analysis of protein pocket dynamics called PocketSCP. Initially, the representation of lining amino acid atoms is proposed to characterize the spatiotemporal and topological properties of pockets. Subsequently, 3D mapping based on a reference molecular conformation is designed to generate 3D distribution maps of pockets. To facilitate observation and analysis, 3D to 2D plane mapping based on equidistant azimuthal projection is designed, leveraging the near-spherical shape properties of protein molecules. Finally, the efficacy of our method in identifying potential patterns within protein pockets is demonstrated through experimental validation.

Effects of Point Mutations on the Thermal Stability of the NBD1 Domain of hCFTR

Cystic fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. The first nucleotide-binding domain (NBD1) of the CFTR is considered to be a hotspot for CF-causing mutations, and some of these mutations compromise the domain's thermal stability as well as its interactions with other domains. The mechanisms by which such mutations exert their deleterious effects are important in the basic research of this complex disease as well as for the development of mutation-specific therapies. With this in mind, we studied two class-II, severe, CF-causing mutations, L467P and A559T, known to destabilize the domain by 19.3 and 10.7 °C, respectively, and to lead to a misfolded, nonfunctioning CFTR, by conducting microsecond-long molecular dynamics (MD) simulations at an elevated temperature of 410 K on L467P-NBD1 and A559T-NBD1 constructs. For comparison, similar simulations were also performed on the wild-type (WT) construct and on the 6SS-NBD1 and 2PT/M470V-NBD1 constructs, both bearing sets of stabilizing mutations that stabilize the domain by 17.5 and 8.2 °C, respectively. The resulting trajectories were analyzed using multiple metrics, leading to a good correlation between the experimental ΔTm values and the results of the simulations, as well as multiple experimental observations and results of previous modeling efforts. Specifically, our analyses point to specific regions within NBD1 that are substantially affected by the L467P and A559T mutations and, therefore, may play some role in their pathogenesis. Many of these regions are also known to be important for the proper folding and function of the full-length CFTR. Using time-dependent assignment of DSSP elements, we also found that the two mutants follow different disintegration pathways, that of L467P-NBD1 starting in region 464-471 which resides within the F1-like ATP-binding core subdomain and continues in regions 550-562 and 514-523 within the ABCα subdomain whereas that of A559T-NBD1 simultaneously starting at the 550-562 and 514-523 regions. We propose that the analyses presented in this work may pave the way toward the development of L467P and A559T-specific CF therapies and by extension to other mutation-specific therapies for CF and for other diseases involving mutations in NBDs of other proteins.

Identifying a non-conserved site for achieving allosteric covalent inhibition of CECR2

The bromodomain (BRD) represents a highly conserved structural module that provides BRD proteins with fundamental functionality in modulating protein-protein interactions involved in diverse biological processes such as chromatin-mediated gene transcription, DNA recombination, replication and repair. Consequently, dysregulation of BRD proteins has been implicated in the pathogenesis of numerous human diseases. In recent years, considerable scientific endeavors have focused on unraveling the molecular mechanisms underlying BRDs and developing inhibitors that target these domains. While these inhibitors compete for binding with the acetylated lysine binding site of BRDs, achieving inhibition of BRD proteins via competitive pocket binding has proven challenging due to the conserved nature of these pockets. To address this limitation, the present study employed dynamic simulations for a comprehensive analysis, leading to the identification of a non-conserved pocket in CECR2 for achieving BRD family inhibition through allosteric modulation. Subsequently, the compound BAY 11-7085 was proven capable of covalently binding to C494 of this pocket after covalent docking and biological verification in vitro. The allosteric inhibition strategy of CECR2 was further verified by the structurally optimized compound LC-CE-7, which is an allosteric covalent CECR2 inhibitor with anti-cancer effects in MDA-MB-231 cells.

Decoding Allosteric Effects of Missense Variations in Drug Metabolism: Afrocentric CYP3A4 Alleles Explored

There is growing research on the allosteric behaviour of proteins, including studies on allosteric mutations that contribute to human diseases and the development of allosteric drugs. Allostery also plays a key role in drug metabolism, an essential factor in drug development. However, population specific variations, particularly in 3D protein structures, remain understudied. This study focuses on CYP3A4, a key enzyme responsible for metabolizing over 50% of FDA-approved drugs and often linked to adverse drug reactions. Given the vast genetic diversity of Africa, we investigated 13 CYP3A4 alleles from African populations using post-molecular dynamics analyses, with 12 being single variations and one containing a double variation. Except for one, all allele variations were located away from the active site, suggesting allosteric effects. Our comparative analyses of reference and variant structures, through hydrogen bond interactions, dynamic residue network analysis and substrate channel dynamics, revealed notable differences at both global and residue levels. The *32-I335T variant showed the largest changes compared to the reference structure, while *3-M445T (near normal metabolizer) exhibited the least change, with other variants falling in between. The *32-I335T variant showed a distorted conformation in the radius of gyration, a distinct kink in the I helix with specific hydrogen bonds and altered channel patterns. The *12-L373F variant, associated with reduced metabolism of midazolam and quinine, showed increased rigidity in its vicinity, potentially interfering with catalytic activity. Our findings align with clinical and wet lab data, suggesting that our approaches could be applied to analyse variants without clinical evidence.

APH Inhibitors that Reverse Aminoglycoside Resistance in Enterococcus casseliflavus

Aminoglycoside-phosphotransferases (APHs) are a class of bacterial enzymes that mediate acquired resistance to aminoglycoside antibiotics. Here we report the identification of small molecules counteracting aminoglycoside resistance in Enterococcus casseliflavus. Molecular dynamics simulations were performed to identify an allosteric pocket in three APH enzymes belonging to 3' and 2'' subfamilies in which we then screened, in silico, 12,000 small molecules. From a subset of only 14 high-scored molecules tested in vitro, we identified a compound, named here EK3, able to non-competitively inhibit the APH(2'')-IVa, an enzyme mediating clinical gentamicin resistance. Structure-activity relationship (SAR) exploration of this hit compound allowed us to identify a molecule with improved enzymatic inhibition. By measuring bacterial sensitivity, we found that the three best compounds in this series restored bactericidal activity of various aminoglycosides, including gentamicin, without exhibiting toxicity to HeLa cells. This work not only provides a basis to fight aminoglycoside resistance but also highlights a proof-of-concept for the search of allosteric modulators by using in silico methods.

Molecular Dynamics Investigation into the Stability of KRas and CRaf Multimeric Complexes

In the Ras/Raf/MAPK signaling pathway, Ras and Raf proteins interact synergistically to form a tetrameric complex. NMR experiments have demonstrated that Ras dimerizes in solution and binds stably to Raf, forming Ras·Raf complexes. In this study, we constructed the ternary and quaternary complexes of KRas and CRaf based on crystal structures, denoted as (KRas)2·CRaf and (KRas)2·(CRaf)2, respectively. Molecular dynamics (MD) simulations were performed to investigate the stability of these complexes, while hydrogen bonds as well as salt bridges formed at the protein-protein interaction interfaces were analyzed based on simulation trajectories. The results revealed that the KRas·CRaf complex is more stable in explicit solvent compared with the KRas dimer. Formation of the stable quaternary complex (KRas)2·(CRaf)2 might be attributed to the association of two binary KRas·CRaf complexes. Additionally, MD simulations of the KRasG12D·CRaf complex revealed a stable and extended binding site at the KRas-CRaf interaction interface. This binding site was identified as a potential therapeutic target to block abnormal signal transmission in the pathway.

Functionally important residues from graph analysis of coevolved dynamic couplings

The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins' performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue-residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings-residue pairs with critical dynamical interactions that have been preserved during evolution-DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.

Mapping the distribution and affinities of ligand interaction sites on human serum albumin

Ligands in many instances interact with a protein at multiple sites with a range of affinities. In this study, ligand-protein interaction sites on human serum albumin (HSA) are mapped using the site-identification by ligand competitive saturation (SILCS)-Biologics approach in conjunction with hydrogen-deuterium exchange (HDX)-mass spectrometry (MS) experiments. Ligands studied include known HSA binders, ibuprofen and ketoprofen, and compounds arginine, alanine, sucrose, and trehalose, excipients used in therapeutic formulations of protein-based drugs. In addition, the impact of excipient binding to HSA on its stability is investigated through temperature-ramp stability studies monitoring solution viscosity. For the studied ligands, interactions that correspond to known drug-binding sites (DSs) are identified. These include previously identified ibuprofen and ketoprofen interaction sites as well as additional sites and, in the case of the excipients, the ligands are shown to also bind at previously unidentified interaction sites, termed excipient sites (ESs) with 20 or more sites identified for the studied compounds. HDX-MS titrations were used to determine dissociation constants for a subset of the interaction sites for ibuprofen, ketoprofen, arginine, and sucrose, which exhibited Kd values in the low micromolar to millimolar range in satisfactory agreement with SILCS-Biologics predicted affinities, validating the computational approach to identify both high- and low-affinity interaction sites. The stability studies indicate the excipients offer protection at low excipient/protein ratios up to 66 with destabilization occurring at ratios above 132 with the exception of sucrose at the t0 time point, indicating that the more favorable affinities of sucrose seen in the SILCS-Biologics and HDX-MS analyses contribute to protein stabilization. These results indicate that ligands can bind to large numbers of interaction sites on proteins, with those interactions having implications for the development of formulations for therapeutic proteins.

Innovative computational approaches in drug discovery and design

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

Pyriofenone Interacts with the Major Facilitator Superfamily Transporter of Phytopathogenic Fungi to Potentially Control Tea Leaf Spot Caused by Lasiodiplodia theobromae

Tea leaf spot caused by Lasiodiplodia theobromae is a newly discovered fungal disease in southwest China. Due to a lack of knowledge of its epidemiology and control strategies, the disease has a marked impact on tea yield and quality. Pyriofenone is a new fungicide belonging to the aryl phenyl ketone fungicide group, which has shown marked efficacy in controlling various fungal diseases. However, its mechanism of action is not yet understood. This study found that pyriofenone exhibits strong in vitro inhibitory activity against various phytopathogenic fungi. Specifically, it showed strong inhibitory activity against L. theobromae, with a half-maximal effective concentration (EC50) value of 0.428 μg/ml determined by measuring the mycelial growth rate. Morphological observations, using optical, scanning electron, and transmission electron microscopy, revealed that pyriofenone induces morphological abnormalities in L. theobromae hyphae. At lower doses, the hyphae became swollen, the distance between septa decreased, and the hyphal growth rate slowed. At higher doses and longer exposures, the hyphae collapsed. Transcriptomic and bioinformatic analyses indicated that pyriofenone can affect the expression of genes related to membrane transporters. Homology modeling suggested that pyriofenone may bind to a candidate target protein of the major facilitator superfamily transporter, with a free binding energy of -7.1 kcal/mol. This study suggests that pyriofenone may potentially regulate the transport of metabolites in L. theobromae, thus affecting hyphal metabolism and interfering with hyphal growth. Pyriofenone exhibits in vitro inhibitory activity against various tea foliar pathogens and holds promise for future applications to the control of tea foliar diseases.

Cross-disease drug discovery based on bioinformatics and virtual screening: Study of key genes in Alzheimer's disease and ovarian cancer

BACKGROUND

Alzheimer's disease (AD) and cancer, both age-related diseases, are characterized by abnormal cellular behavior. Epidemiological data indicate an inverse relationship between AD and various cancers. Accordingly, this study seeks to analyze the negatively correlated genes between AD and ovarian cancer and identify closely related compounds through virtual screening technology to explore potential therapeutic drugs.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus database, and negatively correlated genes between AD and ovarian cancer were identified using bioinformatics analysis. Clinical prognostic and survival analyses were performed to identify genes most negatively associated with these diseases. The top ten compounds with the strongest binding to the target genes were screened from the ChemDiv database using virtual screening technology, considering the blood-brain barrier. Molecular dynamics simulations were used to identify potential sites for the binding of these compounds to the target protein MX1. Additionally, point mutation analysis of the target protein was performed. Finally, the binding site was verified in vitro.

RESULTS

The MX1 gene was most significantly negatively associated with AD and ovarian cancer. Molecular dynamics simulations revealed intersection sites at Glu-227 and Gly-188, where MX1 binds tightly to the head compound.

CONCLUSION

This study successfully identified MX1 as being negatively associated with AD and ovarian cancer and assessed the potential drug compounds that bind most closely to it. Our findings provide important rationale and candidate targets for the development of novel therapeutic strategies for AD and ovarian cancer.

Dehydrogenase versus oxidase function: the interplay between substrate binding and flavin microenvironment

Redox enzymes, mostly equipped with metal or organic cofactors, can vary their reactivity with oxygen by orders of magnitudes. Understanding how oxygen reactivity is controlled by the protein milieu remains an open issue with broad implications for mechanistic enzymology and enzyme design. Here, we address this problem by focusing on a widespread group of flavoenzymes that oxidize phenolic compounds derived from microbial lignin degradation, using either oxygen or a cytochrome c as electron acceptors. A comprehensive phylogenetic analysis revealed conserved amino acid motifs in their flavin-binding site. Using a combination of kinetics, mutagenesis, structural, and computational methods, we examined the role of these residues. Our results demonstrate that subtle and localized changes in the flavin environment can drastically impact on oxygen reactivity. These effects are afforded through the creation or blockade of pathways for oxygen diffusion. Substrate binding plays a crucial role by potentially obstructing oxygen access to the flavin, thus influencing the enzyme's reactivity. The switch between oxidase and dehydrogenase functionalities is thereby achieved through targeted, site-specific amino acid replacements that finely tune the microenvironment around the flavin. Our findings explain how very similar enzymes can exhibit distinct functional properties, operating as oxidases or dehydrogenases. They further provide valuable insights for the rational design and engineering of enzymes with tailored functions.

Duvelisib is a novel NFAT inhibitor that mitigates adalimumab-induced immunogenicity

Introduction

TNFα inhibitor (TNFi) immunogenicity in rheumatoid arthritis (RA) is a major obstacle to its therapeutic effectiveness. Although methotrexate (MTX) can mitigate TNFi immunogenicity, its adverse effects necessitate alternative strategies. Targeting nuclear factor of activated T cells (NFAT) transcription factors may protect against biologic immunogenicity. Therefore, developing a potent NFAT inhibitor to suppress this immunogenicity may offer an alternative to MTX.

Methods

We performed a structure-based virtual screen of the NFATC2 crystal structure to identify potential small molecules that could interact with NFATC2. For validation, we investigated the effect of the identified compound on NFAT transcriptional activity, nuclear localization, and binding to the NFAT consensus sequence. In vivo studies assessed the ability of the compound to protect against TNFi immunogenicity, while ex vivo studies evaluated its effect on CD4+ T cell proliferation and B cell antibody secretion.

Results

We identified duvelisib (DV) as a novel NFATC2 and NFATC1 inhibitor that attenuates NFAT transcriptional activity without inhibiting calcineurin or NFAT nuclear localization. Our results suggest that DV inhibits NFAT independently of PI3K by interfering with nuclear NFAT binding to the NFAT consensus promoter sequence. DV significantly protected mice from adalimumab immunogenicity and attenuated ex vivo CD4+ T cell proliferation and B cell antibody secretion.

Discussion

DV is a promising NFAT inhibitor that can protect against TNFi immunogenicity without inhibiting calcineurin phosphatase activity. Our results suggest that the future development of DV analogs may be of interest as agents to attenuate unwanted immune responses.

In Silico Strategies for Characterizing Inner Cavities of Lipid-Binding Proteins

Cavities in proteins perform diverse functions such as substrate binding, enzyme catalysis, passage for transportation of small molecules, and protein oligomerization. Often, the physical properties of these cavities are closely linked to the protein function; such as the hydrophobic lipid-binding cavities in lipid-binding proteins (LBPs) that protect lipid substrates from the larger aqueous milieu. Therefore, the characterization of protein cavities can provide valuable insights into protein structure-function relationships, hinting toward their mechanism of action while aiding in the identification of ligand binding sites that are essential for drug discovery approaches. Several algorithms have historically been designed to identify and characterize the different types of cavities in protein structures. We summarize these algorithms and provide a step-by-step guide for locating and characterizing internal cavities in proteins using CICLOP by using ATP-binding cassette transporter A1 (ABCA1) as an example.

Predicting fragment binding modes using customized Lennard-Jones potentials in short molecular dynamics simulations

Reliable in silico prediction of fragment binding modes remains a challenge in current drug design research. Due to their small size and generally low binding affinity, fragments can potentially interact with their target proteins in different ways. In the current study, we propose a workflow aimed at predicting favorable fragment binding sites and binding poses through multiple short molecular dynamics simulations. Tailored Lennard-Jones potentials enable the simulation of systems with high concentrations of identical fragment molecules surrounding their respective target proteins. In the present study, descriptors and binding free energy calculations were implemented to filter out the desired fragment position. The proposed method was tested for its performance using four epigenetic target proteins and their respective fragment binders and showed high accuracy in identifying the binding sites as well as predicting the native binding modes. The approach presented here represents an alternative method for the prediction of fragment binding modes and may be useful in fragment-based drug discovery when the corresponding experimental structural data are limited.

New insights into protein-protein interaction modulators in drug discovery and therapeutic advance

Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered to be undruggable targets have become increasingly feasible due to the progress that has been made over the last two decades and the rapid technological advances. This work explores the influence of technological innovations on PPI research and development. Additionally, the diverse strategies for discovering, modulating, and characterizing PPIs and their corresponding modulators are examined with the aim of presenting a streamlined pipeline for advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in PPI modulator discovery and development, we aim to illustrate the efficacy of various strategies for identifying, optimizing, and overcoming challenges associated with PPI modulator design. The valuable lessons and insights gained from the identification, optimization, and approval of PPI modulators are discussed with the aim of demonstrating that PPI modulators have transitioned beyond early-stage drug discovery and now represent a prime opportunity with significant potential. The selected examples of PPI modulators encompass those developed for cancer, inflammation and immunomodulation, as well as antiviral applications. This perspective aims to establish a foundation for the effective targeting and modulation of PPIs using PPI modulators and pave the way for future drug development.

Investigating the Effect of GLU283 Protonation State on the Conformational Heterogeneity of CCR5 by Molecular Dynamics Simulations

CCR5 is a class A GPCR and serves as one of the coreceptors facilitating HIV-1 entry into host cells. This receptor has vital roles in the immune system and is involved in the pathogenesis of different diseases. Various studies were conducted to understand its activation mechanism, including structural studies in which inactive and active states of the receptor were determined in complex with various binding partners. These determined structures provided opportunities to perform molecular dynamics (MD) simulations and to analyze conformational changes observed in the protein structures. The atomic-level dynamic studies allow us to explore the effects of ionizable residues on the receptor. Here, our aim was to investigate the conformational changes in CCR5 when it forms a complex with either the inhibitor maraviroc (MRV), an approved anti-HIV drug, or HIV-1 envelope protein GP120, and compare these changes to the receptor's apo form. In our simulations, we considered both ionized and protonated states of ionizable binding site residue GLU2837.39 in CCR5 as the protonation state of this residue was considered ambiguously in previous studies. Our molecular simulations results suggested that in fact, the change in the protonation state of GLU2837.39 caused interaction profiles to be different between CCR5 and its binding partners, GP120 or MRV. We observed that when the protonated state of GLU2837.39 was considered in complex with the envelope protein GP120, there were substantial structural changes in CCR5, indicating that it adopts a more active-like conformation. On the other hand, CCR5 in complex with MRV always adopted an inactive conformation regardless of the protonation state. Hence, the CCR5 coreceptor displays conformational heterogeneity not only depending on its binding partner but also influenced by the protonation state of the binding site binding site residue GLU2837.39. This outcome is also in accordance with some studies showing that GP120 binding could activate signaling pathways. This outcome could also have significant implications for discovering novel CCR5 inhibitors as anti-HIV drugs using in silico methods such as molecular docking, as it may be necessary to consider the protonated state of GLU2837.39.

Exploring the Structural and Dynamic Properties of a Chimeric Glycoside Hydrolase Protein in the Presence of Calcium Ions

GH10 xylanases and GH62 Arabinofuranosidases play key roles in the breakdown of arabinoxylans and are important tools in various industrial and biotechnological processes, such as renewable biofuel production, the paper industry, and the production of short-chain xylooligosaccharides (XOS) from plant biomass. However, the use of these enzymes in industrial settings is often limited due to their relatively low thermostability and reduced catalytic efficiency. To overcome these limitations, strategies based on enzymatic chimera construction and the use of metal ions and other cofactors have been proposed to produce new recombinant enzymes with improved catalytic activity and thermostability. Here, we examine the conformational dynamics of a GH10-GH62 chimera at different calcium ion concentrations through molecular dynamics simulations. While experimental data have demonstrated improved activity and thermostability in GH10-GH62 chimera, the mechanistic basis for these enhancements remains unclear. We explored the structural details of the binding subsites of Ca2+ in the parental enzymes GH62 from Aspergillus fumigatus (Afafu62) and a recombinant GH10 from Cryptococcus flavescens (Xyn10cf), as well as their chimeric combination, and how negatively charged electron pairing located at the protein surface affects Ca2+ capture. The results indicate that Ca2+ binding significantly contributes to structural stability and catalytic cavity modulation in the chimera, particularly evident at a concentration of 0.01 M. This effect, not observed in the parental GH10 and GH62 enzymes, highlights how Ca2+ enhances stability in the overall chimeric enzyme, while supporting a larger cavity volume in the chimera GH62 subunit. The increased catalytic site volume and reduced structural flexibility in response to Ca2+ suggest that calcium binding minimizes non-productive conformational states, which could potentially improve catalytic turnover. The findings presented here may aid in the development of more thermostable and efficient catalytic systems.

In silico validation of allosteric inhibitors targeting Zika virus NS2B-NS3 protease

The Zika virus (ZIKV), a member of the Flaviviridae family, poses a major threat to human health because of the lack of effective antiviral drugs. Although the NS2B-NS3 protease of ZIKV (NS2B-NS3pro) is regarded as a major target for antiviral inhibitors, viral mutations can lead to ineffective competitive inhibitors. Allosteric inhibitors bind to highly conserved nonprotease active sites, induce conformational changes in the protease active site, and prevent substrate binding. Currently, no molecular simulation techniques are available for accurately predicting and analysing conformational changes in the protease catalytic domain. In this study, we developed a combined approach that involves blind docking, Gaussian accelerated molecular dynamics, two-dimensional potential of mean force profiling, density functional theory (DFT) calculations, and interaction region indicator (IRI) analysis and employed it to examine the allosteric inhibitor-01 molecule and its interaction with ZIKV NS2B-NS3pro. Our results indicated that the binding of inhibitor-01 to NS2B-NS3pro resulted in two major conformational states, state I and state II, which in turn changed the volume of the protease active site from 1014 Å3 to 710 and 820 Å3, respectively. These two states had an inactive catalytic domain (residues His116, Asp140, and Ser200). DFT and IRI analyses revealed that, in state I, Lys138 and Gln139 formed hydrogen bonds with inhibitor-01, whereas Lys138, Leu214, Asn217, Val220, and Ile221 engaged in van der Waals interactions with inhibitor-01. Advancements in computational techniques and power are expected to facilitate further progress in overcoming challenges associated with designing allosteric inhibitors for viral proteases.

Potential allosteric pockets identification of glucagon receptor based on molecular dynamics simulations

Glucagon receptor (GCGR) is an important target for the treatment of type 2 diabetes mellitus. Although several small molecules with antagonistic activity have been discovered, so far, only one small molecule binding site has been resolved. To discover more novel allosteric pockets and allosteric molecules, we started with the unique full-length inactive conformation of GCGR and applied all-atom molecular dynamics (MD) simulations to obtain extensive dynamic conformations of the GCGR/glucagon complex. For the first time, MDpocket, FTMove and FTMap were used to detect allosteric pockets in simulation trajectories, selecting 4 stable pockets with a total of 14 structures as templates for virtual screening. From the results of virtual screening, 14 compounds were ultimately selected after a series of filtering steps. The cAMP accumulation assay indicated that compound gs6 has antagonistic activity, and MD simulations further revealed the allosteric mechanism of gs6. We are the first to identify new allosteric pockets and allosteric molecules in simulation trajectories of the GCGR/glucagon complex, providing a reference for research on other G-protein-coupled receptors (GPCR). However, there is still considerable room for improvement, such as using more simulation methods to obtain a richer set of dynamic conformations.

A Computational Pipeline Observes the Flexibility and Dynamics of Plant Cytochrome P450 Binding Sites

Binding site flexibility and dynamics strongly affect the ability of proteins to accommodate substrates and inhibitors. The significance of these properties is particularly pronounced for proteins that are inherently flexible, such as cytochrome P450 enzymes (CYPs). While the research on human CYPs provides detailed knowledge on both structural and functional level, such analyses are still lacking for their plant counterparts. This study aims to bridge this gap. We developed a novel computational pipeline consisting of two steps. Firstly, we use molecular dynamics (MD) simulations to capture the full conformational ensemble for a certain plant CYP. Subsequently, we developed and applied a comprehensive methodology to analyze a number of binding site properties-size, flexibility, shape, hydrophobicity, and accessibility-using the fpocket and mdpocket packages on MD-generated trajectories. The workflow was validated on human CYPs 1A2, 2A6, and 3A4, as their binding site characteristics are well known. Not only could we confirm known binding site properties, but we also identified and named previously unseen binding site channels for CYPs 1A2 and 2A6. The pipeline was then applied to plant CYPs, leading to the first categorization of 15 chosen plant CYPs based on their binding site's (dis)similarities. This study provides a foundation for the largely uncharted fields of plant CYP substrate specificity and facilitates a more precise understanding of their largely unknown specific biological functions. It offers new insights into the structural and functional dynamics of plant CYPs, which may facilitate a more accurate understanding of the fate of agrochemicals or the biotechnological design and exploitation of enzymes with specific functions. Additionally, it serves as a reference for future structural-functional analyses of CYP enzymes across various biological kingdoms.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Catalytic Redundancies and Conformational Plasticity Drives Selectivity and Promiscuity in Quorum Quenching Lactonases

Several enzymes from the metallo-β-lactamase-like family of lactonases (MLLs) degrade N-acyl L-homoserine lactones (AHLs). They play a role in a microbial communication system known as quorum sensing, which contributes to pathogenicity and biofilm formation. Designing quorum quenching (QQ) enzymes that can interfere with this communication allows them to be used in a range of industrial and biomedical applications. However, tailoring these enzymes for specific communication signals requires a thorough understanding of their mechanisms and the physicochemical properties that determine their substrate specificities. We present here a detailed biochemical, computational, and structural study of GcL, which is a highly proficient and thermostable MLL with broad substrate specificity. We show that GcL not only accepts a broad range of substrates but also hydrolyzes these substrates through at least two different mechanisms. Further, the preferred mechanism appears to depend on both the substrate structure and/or the nature of the residues lining the active site. We demonstrate that other lactonases, such as AiiA and AaL, show similar mechanistic promiscuity, suggesting that this is a shared feature among MLLs. Mechanistic promiscuity has been seen previously in the lactonase/paraoxonase PON1, as well as with protein tyrosine phosphatases that operate via a dual general acid mechanism. The apparent prevalence of this phenomenon is significant from both a biochemical and protein engineering perspective: in addition to optimizing for specific substrates, it may be possible to optimize for specific mechanisms, opening new doors not just for the design of novel quorum quenching enzymes but also of other mechanistically promiscuous enzymes.

Pixantrone as a novel MCM2 inhibitor for ovarian cancer treatment

BACKGROUND

Mini-chromosome maintenance protein 2 (MCM2) is a potential target for the development of cancer therapeutics. However, small molecule inhibitors targeting MCM2 need further investigation.

METHODS

Molecular dynamics simulation was performed to identify active pockets in the MCM2 protein structure (6EYC). The active pocket was used as a docking model to discover MCM2 inhibitors by using structure-based virtual screening and surface plasmon resonance (SPR) assay. Furthermore, the efficacy of pixantrone targeting MCM2 in ovarian cancer was evaluated in vitro and in vivo.

RESULTS

Pixantrone was identified as a novel inhibitor of MCM2 by virtual screening. SPR binding affinity analysis confirmed the direct binding of pixantrone to MCM2 protein. Pixantrone significantly reduced the viability of ovarian cancer cells A2780 and SKOV3 in a dose- and time-dependent manner. In addition, pixantrone inhibited DNA replication, and induced cell cycle arrest and apoptosis in ovarian cancer cells via targeting MCM2. Knockdown of MCM2 could attenuate the inhibitory activity of pixantrone in ovarian cancer cells. Furthermore, pixantrone significantly suppressed ovarian cancer growth in the A2780 cell xenograft mouse model and showed favorable safety.

CONCLUSION

These findings suggest that pixantrone may be a promising drug for ovarian cancer patients by targeting MCM2 in the clinic.

Examining Hemin and its Derivatives: Induction of Heme-Oxygenase-1 Activity and Oxidative Stress in Breast Cancer Cells through Collaborative Experimental Analysis and Molecular Dynamics Simulations

Hemin triggers intracellular reactive oxygen species (ROS) accumulation and enhances heme oxygenase-1 (HOX-1) activity, indicating its potential as an anticancer agent, though precise control of its intracellular levels is crucial. The study explores the impact of hemin and its derivatives, hemin-tyrosine, and hemin-styrene (H-Styr) conjugates on migration, HOX-1 expression, specific apoptosis markers, mitochondrial functions, and ROS generation in breast cancer cells. Molecular docking and dynamics simulations were used to understand the interactions among HOX-1, heme, and the compounds. Hemin outperforms its derivatives in inducing HOX-1 expression, exhibiting pro-oxidative effects and reducing cell migration. Molecular simulations show that heme binds favorably to HOX-1, followed by the other compounds, primarily through van der Waals and electrostatic forces. However, only van der Waals forces determine the H-Styr complexation. These interactions, influenced by metalloporphyrin characteristics, provide insights into HOX-1 regulation and ROS generation, potentially guiding the development of breast cancer therapies targeting oxidative stress.

Exploring an Intracellular Allosteric Site of CC-Chemokine Receptor 4 from 3D Models, Probe Simulations, and Mutagenesis

We applied our previously developed probe confined dynamic mapping protocol, which combines enhanced sampling molecular dynamics (MD) simulations and fragment-based approaches, to identify the binding site of GSK2239633A (N-[[3-[[3-[(5-chlorothiophen-2-yl)sulfonylamino]-4-methoxyindazol-1-yl]methyl]phenyl]methyl]-2-hydroxy-2-methylpropanamide), a selective CC-chemokine receptor type 4 (CCR4) negative allosteric modulator, using CCR4 homology and AlphaFold models. By comparing the performance across five computational models, we identified conserved (K3108.49 and Y3047.53) and non-conserved (M2436.36) residue hotspots for GSK2239633A binding, which were validated by mutagenesis and bioluminescence resonance energy transfer assay. Further analysis of 3D models and MD simulations highlighted the pair of residues 6.36 and 7.56 that might account for antagonist selectivity among chemokine receptors. Our in silico protocol provides a promising approach for characterizing ligand binding sites in membrane proteins, considering receptor dynamics and adaptability and guiding protein template selection for ligand design.

The conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action

Lipid transfer proteins (LTPs) are key players in cellular homeostasis and regulation, as they coordinate the exchange of lipids between different cellular organelles. Despite their importance, our mechanistic understanding of how LTPs function at the molecular level is still in its infancy, mostly due to the large number of existing LTPs and to the low degree of conservation at the sequence and structural level. In this work, we use molecular simulations to characterize a representative dataset of lipid transport domains (LTDs) of 12 LTPs that belong to 8 distinct families. We find that despite no sequence homology nor structural conservation, the conformational landscape of LTDs displays common features, characterized by the presence of at least 2 main conformations whose populations are modulated by the presence of the bound lipid. These conformational properties correlate with their mechanistic mode of action, allowing for the interpretation and design of experimental strategies to further dissect their mechanism. Our findings indicate the existence of a conserved, fold-independent mechanism of lipid transfer across LTPs of various families and offer a general framework for understanding their functional mechanism.

The Inferential Binding Sites of GCGR for Small Molecules Using Protein Dynamic Conformations and Crystal Structures

Glucagon receptor (GCGR) is a class B1 G-protein-coupled receptor that plays a crucial role in maintaining human blood glucose homeostasis and is a significant target for the treatment of type 2 diabetes mellitus (T2DM). Currently, six small molecules (Bay 27-9955, MK-0893, MK-3577, LY2409021, PF-06291874, and LGD-6972) have been tested or are undergoing clinical trials, but only the binding site of MK-0893 has been resolved. To predict binding sites for other small molecules, we utilized both the crystal structure of the GCGR and MK-0893 complex and dynamic conformations. We docked five small molecules and selected the best conformation based on binding mode, docking score, and binding free energy. We performed MD simulations to verify the binding mode of the selected small molecules. Moreover, when selecting conformations, results of competitive binding were referred to. MD simulation indicated that Bay 27-9955 exhibits moderate binding stability in Pocket 3. MK-3577, LY2409021, and PF-06291874 exhibited highly stable binding to Pocket 2, consistent with experimental results. However, LY2409021 may also bind to Pocket 5. Additionally, LGD-6972 exhibited relatively stable binding in Pocket 5. We also conducted structural modifications of LGD-6972 based on the results of MD simulations and predicted its analogues' bioavailability, providing a reference for the study of GCGR small molecules.

Unraveling the mechanism of small molecule induced activation of Staphylococcus aureus signal peptidase IB

Staphylococcus aureus signal peptidase IB (SpsB) is an essential enzyme for protein secretion. While inhibition of its activity by small molecules is a well-precedented mechanism to kill bacteria, the mode of activation is however less understood. We here investigate the activation mechanism of a recently introduced activator, the antibiotic compound PK150, and demonstrate by combined experimental and Molecular Dynamics (MD) simulation studies a unique principle of enzyme stimulation. Mass spectrometric studies with an affinity-based probe of PK150 unravel the binding site of PK150 in SpsB which is used as a starting point for MD simulations. Our model shows the localization of the molecule in an allosteric pocket next to the active site which shields the catalytic dyad from excess water that destabilizes the catalytic geometry. This mechanism is validated by the placement of mutations aligning the binding pocket of PK150. While the mutants retain turnover of the SpsB substrate, no stimulation of activity is observed upon PK150 addition. Overall, our study elucidates a previously little investigated mechanism of enzyme activation and serves as a starting point for the development of future enzyme activators.

CageCavityCalc (C3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages

Organic(porous) and metal-organic cages are promising biomimetic platforms with diverse applications spanning recognition, sensing, and catalysis. The key to the emergence of these functions is the presence of well-defined inner cavities capable of binding a wide range of guest molecules and modulating their properties. However, despite the myriad cage architectures currently available, the rational design of structurally diverse and functional cages with specific host-guest properties remains challenging. Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce CageCavityCalc (C3), a Python-based tool for calculating the cavity size of molecular cages. The code is available on GitHub at https://github.com/VicenteMartiCentelles/CageCavityCalc. C3 utilizes a novel algorithm that enables the rapid calculation of cavity sizes for a wide range of molecular structures and porous systems. Moreover, C3 facilitates easy visualization of the computed cavity size alongside hydrophobic and electrostatic potentials, providing insights into host-guest interactions within the cage. Furthermore, the calculated cavity can be visualized using widely available visualization software, such as PyMol, VMD, or ChimeraX. To enhance user accessibility, a PyMol plugin has been created, allowing nonspecialists to use this tool without requiring computer programming expertise. We anticipate that the deployment of this computational tool will significantly streamline cage cavity calculations, thereby accelerating the discovery of functional cages.

Identification of a Cryptic Pocket in Methionine Aminopeptidase-II Using Adaptive Bandit Molecular Dynamics Simulations and Markov State Models

Methionine aminopeptidase-II (MetAP-II) is a metalloprotease, primarily responsible for the cotranslational removal of the N-terminal initiator methionine from the nascent polypeptide chain during protein synthesis. MetAP-II has been implicated in angiogenesis and endothelial cell proliferation and is therefore considered a validated target for cancer therapeutics. However, there is no effective drug available against MetAP-II. In this study, we employ Adaptive Bandit molecular dynamics simulations to investigate the structural dynamics of the apo and ligand-bound MetAP-II. Our results focus on the dynamic behavior of the disordered loop that is not resolved in most of the crystal structures. Further analysis of the conformational flexibility of the disordered loop reveals a hidden cryptic pocket that is predicted to be potentially druggable. The network analysis indicates that the disordered loop region has a direct signaling route to the active site. These findings highlight a new way to target MetAP-II by designing inhibitors for the allosteric site within this disordered loop region.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Efficient and novel biosynthesis of myricetin α-triglucoside with improved solubility using amylosucrase from Deinococcus deserti

Although myricetin (3,3',4',5,5',7-hexahydroxyflavone, MYR) has a high antioxidant capacity and health functions, its use as a functional food material is limited owing to its low stability and water solubility. Amylosucrase (ASase) is capable of biosynthesizing flavonol α-glycoside using flavonols as acceptor molecules and sucrose as a donor molecule. Here, ASase from Deinococcus deserti (DdAS) efficiently biosynthesizes a novel MYR α-triglucoside (MYRαG3) using MYR as the acceptor molecule. Comparative homology analysis and computational simulation revealed that DdAS has a different active pocket for the transglycosylation reaction. DdAS produced MYRαG3 with a conversion efficiency of 67.4 % using 10 mM MYR and 50 mM sucrose as acceptor and donor molecules, respectively. The structure of MYRαG3 was identified as MYR 4'-O-4″,6″-tri-O-α-D-glucopyranoside using NMR and LC-MS. In silico analysis confirmed that DdAS has a distinct active pocket compared to other ASases. In addition, molecular docking simulations predicted the synthetic sequence of MYRαG3. Furthermore, MYRαG3 showed a similar DPPH radical scavenging activity of 49 %, comparable to MYR, but with significantly higher water solubility, which increased from 0.03 μg/mL to 511.5 mg/mL. In conclusion, this study demonstrated the efficient biosynthesis of a novel MYRαG3 using DdAS and highlighted the potential of MYRαG3 as a functional material.

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.

Exploring Aromatic Cage Flexibility Using Cosolvent Molecular Dynamics Simulations─An In-Silico Case Study of Tudor Domains

Cosolvent molecular dynamics (MD) simulations have proven to be powerful in silico tools to predict hotspots for binding regions on protein surfaces. In the current study, the method was adapted and applied to two Tudor domain-containing proteins, namely Spindlin1 (SPIN1) and survival motor neuron protein (SMN). Tudor domains are characterized by so-called aromatic cages that recognize methylated lysine residues of protein targets. In the study, the conformational transitions from closed to open aromatic cage conformations were investigated by performing MD simulations with cosolvents using six different probe molecules. It is shown that a trajectory clustering approach in combination with volume and atomic distance tracking allows a reasonable discrimination between open and closed aromatic cage conformations and the docking of inhibitors yields very good reproducibility with crystal structures. Cosolvent MDs are suitable to capture the flexibility of aromatic cages and thus represent a promising tool for the optimization of inhibitors.

Discovery of potent small molecule ubiquitin-specific protease 10 inhibitors with anti-hepatocellular carcinoma activity through regulating YAP expression

High expression of ubiquitin-specific protease 10 (USP10) promote the proliferation of hepatocellular carcinoma (HCC), thus the development of USP10 inhibitors holds promise as a novel therapeutic approach for HCC treatment. However, the development of selective USP10 inhibitor is still limited. In this study, we developed a novel USP10 inhibitor for investigating the feasibility of targeting USP10 for the treatment of HCC. Due to high USP10 inhibition potency and prominent selectivity, compound D1 bearing quinolin-4(1H)-one scaffold was identified as a lead compound. Subsequent research revealed that D1 significantly inhibits cell proliferation and clone formation in HCC cells. Mechanistic insights indicated that D1 targets the ubiquitin pathway, facilitating the degradation of YAP (Yes-associated protein), thereby triggering the downregulation of p53 and its downstream protein p21. Ultimately, this cascade leads to S-phase arrest in HCC cells, followed by cell apoptosis. Collectively, our findings highlight D1 as a promising starting point for USP10-positive HCC treatment, underscoring its potential as a vital tool for unraveling the functional intricacies of USP10.

Structure-Based Discovery of Allosteric Inhibitors Targeting a New Druggable Site in the Respiratory Syncytial Virus Polymerase

Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory infections for which effective treatment options remain limited. Herein, we employed a computational structure-based design strategy aimed at identifying potential targets for a new class of allosteric inhibitors. Our investigation led to the discovery of a previously undisclosed allosteric binding site within the RSV polymerase, the large (L) protein. This discovery was achieved through a combination of virtual screening and molecular dynamics simulations. Subsequently, we identified two inhibitors, 6a and 10b, which both exhibited promising antiviral activity in the low micromolar range. Resistance profiling revealed a distinctive pattern in how RSV evaded treatment with this class of inhibitors. This pattern strongly suggested that this class of small molecules was targeting a new binding site in the RSV L protein, aligning with the computational predictions made in our study. This study paves the way for the development of more potent inhibitors for combating RSV infections by targeting a new druggable pocket within the RdRp which does not overlap with previously known resistance sites.

Computational investigation of cis-1,4-polyisoprene binding to the latex-clearing protein LcpK30

Latex clearing proteins (Lcps) catalyze the oxidative cleavage of the C = C bonds in cis-1,4-polyisoprene (natural rubber), producing oligomeric compounds that can be repurposed to other materials. The active catalytic site of Lcps is buried inside the protein structure, thus raising the question of how the large hydrophobic rubber chains can access the catalytic center. To improve our understanding of hydrophobic polymeric substrate binding to Lcps and subsequent catalysis, we investigated the interaction of a substrate model containing ten carbon-carbon double bonds with the structurally characterized LcpK30, using multiple computational tools. Prediction of the putative tunnels and cavities in the LcpK30 structure, using CAVER-Pymol plugin 3.0.3, fpocket and Molecular Dynamic (MD) simulations provided valuable insights on how substrate enters from the surface to the buried active site. Two dominant tunnels were discovered that provided feasible routes for substrate binding, and the presence of two hydrophobic pockets was predicted near the heme cofactor. The larger of these pockets is likely to accommodate the substrate and to determine the size distribution of the oligomers. Protein-ligand docking was carried out using GOLD software to predict the conformations and interactions of the substrate within the protein active site. Deeper insight into the protein-substrate interactions, including close-contacts, binding energies and potential cleavage sites in the cis-1,4-polyisoprene, were obtained from MD simulations. Our findings provide further justification that the protein-substrate complexation in LcpK30 is mainly driven by the hydrophobic interactions accompanied by mutual conformational changes of both molecules. Two potential binding modes were identified, with the substrate in either extended or folded conformations. Whilst binding in the extended conformation was most favorable, the folded conformation suggested a preference for cleavage of a central double bond, leading to a preference for oligomers with 5 to 6 C = C bonds. The results provide insight into further enzyme engineering studies to improve catalytic activity and diversify the substrate and product scope of Lcps.

Catalytic Redundancies and Conformational Plasticity Drives Selectivity and Promiscuity in Quorum Quenching Lactonases

Several enzymes from the metallo-β-lactamase-like family of lactonases (MLLs) degrade N- acyl-L-homoserine lactones (AHLs). In doing so, they play a role in a microbial communication system, quorum sensing, which contributes to pathogenicity and biofilm formation. There is currently great interest in designing quorum quenching ( QQ ) enzymes that can interfere with this communication and be used in a range of industrial and biomedical applications. However, tailoring these enzymes for specific targets requires a thorough understanding of their mechanisms and the physicochemical properties that determine their substrate specificities. We present here a detailed biochemical, computational, and structural study of the MLL GcL, which is highly proficient, thermostable, and has broad substrate specificity. Strikingly, we show that GcL does not only accept a broad range of substrates but is also capable of utilizing different reaction mechanisms that are differentially used in function of the substrate structure or the remodeling of the active site via mutations. Comparison of GcL to other lactonases such as AiiA and AaL demonstrates similar mechanistic promiscuity, suggesting this is a shared feature across lactonases in this enzyme family. Mechanistic promiscuity has previously been observed in the lactonase/paraoxonase PON1, as well as with protein tyrosine phosphatases that operate via a dual general-acid mechanism. The apparent prevalence of this phenomenon is significant from both a biochemical and an engineering perspective: in addition to optimizing for specific substrates, it is possible to optimize for specific mechanisms, opening new doors not just for the design of novel quorum quenching enzymes, but also of other mechanistically promiscuous enzymes.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Thermostability Enhancement of GH 62 α-l-Arabinofuranosidase by Directed Evolution and Rational Design

GH 62 arabinofuranosidases are known for their excellent specificity for arabinoxylan of agroindustrial residues and their synergism with endoxylanases and other hemicellulases. However, the low thermostability of some GH enzymes hampers potential industrial applications. Protein engineering research highly desires mutations that can enhance thermostability. Therefore, we employed directed evolution using one round of error-prone PCR and site-saturation mutagenesis for thermostability enhancement of GH 62 arabinofuranosidase from Aspergillus fumigatus. Single mutants with enhanced thermostability showed significant ΔΔG changes (<-2.5 kcal/mol) and improvements in perplexity scores from evolutionary scale modeling inverse folding. The best mutant, G205K, increased the melting temperature by 5 °C and the energy of denaturation by 41.3%. We discussed the functional mechanisms for improved stability. Analyzing the adjustments in α-helices, β-sheets, and loops resulting from point mutations, we have obtained significant knowledge regarding the potential impacts on protein stability, folding, and overall structural integrity.

Synergy of Mutation-Induced Effects in Human Vitamin K Epoxide Reductase: Perspectives and Challenges for Allo-Network Modulator Design

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme transforming vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents delivered by its redox partner through thiol-disulfide exchange reactions. The luminal loop (L-loop) is the principal mediator of hVKORC1 activation, and it is a region frequently harbouring numerous missense mutations. Four L-loop hVKORC1 mutants, suggested in vitro as either resistant (A41S, H68Y) or completely inactive (S52W, W59R), were studied in the oxidised state by numerical approaches (in silico). The DYNASOME and POCKETOME of each mutant were characterised and compared to the native protein, recently described as a modular protein composed of the structurally stable transmembrane domain (TMD) and the intrinsically disordered L-loop, exhibiting quasi-independent dynamics. The DYNASOME of mutants revealed that L-loop missense point mutations impact not only its folding and dynamics, but also those of the TMD, highlighting a strong mutation-specific interdependence between these domains. Another consequence of the mutation-induced effects manifests in the global changes (geometric, topological, and probabilistic) of the newly detected cryptic pockets and the alternation of the recognition properties of the L-loop with its redox protein. Based on our results, we postulate that (i) intra-protein allosteric regulation and (ii) the inherent allosteric regulation and cryptic pockets of each mutant depend on its DYNASOME; and (iii) the recognition of the redox protein by hVKORC1 (INTERACTOME) depend on their DYNASOME. This multifaceted description of proteins produces "omics" data sets, crucial for understanding the physiological processes of proteins and the pathologies caused by alteration of the protein properties at various "omics" levels. Additionally, such characterisation opens novel perspectives for the development of "allo-network drugs" essential for the treatment of blood disorders.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/β7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/β7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.

Different binding modalities of quercetin to inositol-requiring enzyme 1 of S. cerevisiae and human lead to opposite regulation

The flavonoid Quercetin (Qe) was identified as an activator of Inositol-requiring enzyme 1 (IRE1) in S. cerevisiae (scIre1p), but its impact on human IRE1 (hIRE1) remains controversial due to the absence of a conserved Qe binding site. We have explored the binding modes and effect of Qe on both scIre1p and hIRE1 dimers using in silico and in vitro approaches. The activation site in scIre1p stably accommodates both Qe and its derivative Quercitrin (Qi), thus enhancing the stability of the RNase pocket. However, the corresponding region in hIRE1 does not bind any of the two molecules. Instead, we show that both Qe and Qi block the RNase activity of hIRE1 in vitro, with sub-micromolar IC50 values. Our results provide a rationale for why Qe is an activator in scIre1p but a potent inhibitor in hIRE1. The identification of a new allosteric site in hIRE1 opens a promising window for drug development and UPR modulation.

How Modulator Binding at the Amyloidβ-γ-Secretase Interface Enhances Substrate Binding and Attenuates Membrane Distortion

Inhibition of γ-secretase, an intramembrane protease, to reduce secretion of Amyloid-β (Aβ) peptides has been considered for treating Alzheimer's disease. However, γ-secretase inhibitors suffer from severe side effects. As an alternative, γ-secretase modulators (GSM) reduce the generation of toxic peptides by enhancing the cleavage processivity without diminishing the enzyme activity. Starting from a known γ-secretase structure without substrate but in complex with an E2012 GSM, we generated a structural model that included a bound Aβ43 peptide and studied interactions among enzyme, substrate, GSM, and lipids. Our result suggests that E2012 binding at the enzyme-substrate-membrane interface attenuates the membrane distortion by shielding the substrate-membrane interaction. The model predicts that the E2012 modulation is charge-dependent and explains the preserved hydrogen acceptor and the aromatic ring observed in many imidazole-based GSM. Predicted effects of γ-secretase mutations on E2012 modulation were confirmed experimentally. We anticipate that the study will facilitate the future development of effective GSMs.

AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics

Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.

Conformational response to ligand binding of TMPRSS2, a protease involved in SARS-CoV-2 infection: Insights through computational modeling

Thanks to the considerable research which has been undertaken in the last few years to improve our understanding of the biology and mechanism of action of SARS-CoV-2, we know how the virus uses its surface spike protein to infect host cells. The transmembrane prosthesis, serine 2 (TMPRSS2) protein, located on the surface of human cells, recognizes the cleavage site in the spike protein, leading to the release of the fusion peptide and entry of the virus into the host cells. Because of its role, TMPRSS2 has been proposed as a drug target to prevent infection by the virus. In this study, we aim to increase our understanding of TMPRSS2 using long scale microsecond atomistic molecular dynamics simulations, focusing on the conformational changes over time. The comparison between simulations conducted on the protein in the native (apo) and inhibited form (holo), has shown that in the holo form the inhibitor stabilizes the catalytic site and induces rearrangements in the extracellular domain of the protein. In turn, it leads to the formation of a new cavity in the vicinity of the ligand binding pocket that is stable in the microsecond time scale. Given the low specificity of known protease inhibitors, these findings suggest a new potential drug target site that can be used to improve TMPRSS2 specific recognition by newly designed inhibitors.