Fpocket: an open source platform for ligand pocket detection

Druggable cavities and allosteric modulators of the cell division cycle 7 (CDC7) kinase

Cell division cycle 7 kinase (CDC7) has been found overexpressed in many cancer cell lines being also one of the kinases involved in the nuclear protein TDP-43 phosphorylation in vivo. Thus, inhibitors of CDC7 are emerging drug candidates for the treatment of oncological and neurodegenerative unmet diseases. All the known CDC7 inhibitors are ATP-competitives, lacking of selectivity enough for success in clinical trials. As allosteric sites are less conserved among kinase proteins, discovery of allosteric modulators of CDC7 is a great challenge and opportunity in this field.Using different computational approaches, we have here identified new druggable cavities on the human CDC7 structure and subsequently selective CDC7 inhibitors with allosteric modulation mainly targeting the pockets where the interaction between this kinase and its activator DBF4 takes place.

Free energy landscape of the PI3Kα C-terminal activation

The gene PIK3CA, encoding the catalytic subunit p110α of PI3Kα, is the second most frequently mutated gene in cancer, with the highest frequency oncogenic mutants occurring in the C-terminus of the kinase domain. The C-terminus has a dual function in regulating the kinase, playing a putative auto-inhibitory role for kinase activity and being absolutely essential for binding to the cell membrane. However, the molecular mechanisms by which these C-terminal oncogenic mutations cause PI3Kα overactivation remain unclear. To understand how a spectrum of C-terminal mutations of PI3Kα alter kinase activity compared to the WT, we perform unbiased and biased Molecular Dynamics simulations of several C-terminal mutants and report the free energy landscapes for the C-terminal "closed-to-open" transition in the WT, H1047R, G1049R, M1043L and N1068KLKR mutants. Results are consistent with HDX-MS experimental data and provide a molecular explanation why H1047R and G1049R reorient the C-terminus with a different mechanism compared to the WT and M1043L and N1068KLKR mutants. Moreover, we show that in the H1047R mutant, the cavity, where the allosteric ligands STX-478 and RLY-2608 bind, is more accessible contrary to the WT. This study provides insights into the molecular mechanisms underlying activation of oncogenic PI3Kα by C-terminal mutations and represents a valuable resource for continued efforts in the development of mutant selective inhibitors as therapeutics.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

PFOS and PFOSA induce oxidative stress-mediated cardiac defects in zebrafish via PPARγ and AHR pathways, respectively

Perfluorooctane sulfonate (PFOS) and its precursor, perfluorooctane sulfonamide (PFOSA), are widespread in the environment. Evidence suggests a strong link between maternal exposure to PFOS/PFOSA and congenital heart diseases in the offspring, but the underlying mechanisms remain unclear. We hypothesized that PFOS and PFOSA induce cardiac defects through the peroxisome proliferator-activated receptor gamma (PPARγ) and aryl hydrocarbon receptor (AHR) pathways, respectively. In this study, we demonstrated that exposing zebrafish embryos to either PFOSA or PFOS caused cardiac malformations and dysfunction. Both PFOS and PFOSA induced reactive oxygen species (ROS) overproduction, mitochondrial damage, and apoptosis in zebrafish larvae hearts. Blockade of PPARγ through either pharmaceutical inhibition or genetic knockdown only attenuated the changes caused by PFOS, but not those elicited by PFOSA. Conversely, inhibition of AHR alleviated the adverse effects induced by PFOSA but not by PFOS. Both PFOSA and PFOS exhibited similar binding affinities to AHR using molecular docking techniques. The varying ability of PFOS and PFOSA to induce AHR activity in zebrafish embryonic hearts can be attributed to their different capabilities for activating PPARγ. In summary, our findings indicate that PFOS and PFOSA induce excessive ROS production in zebrafish larvae via the PPARγ and AHR pathways, respectively. This oxidative stress in turn causes mitochondrial damage and apoptosis, leading to cardiac defects.

Indole-core inhibitors of influenza a neuraminidase: iterative medicinal chemistry and molecular modeling

Influenza viruses that cause seasonal and pandemic flu are a permanent health threat. The surface glycoprotein, neuraminidase, is crucial for the infectivity of the virus and therefore an attractive target for flu drug discovery campaigns. We have designed and synthesized more than 40 3-indolinone derivatives. We mainly investigated the role of substituents at the 2 position of the core as well as the introduction of substituents or a nitrogen atom in the fused phenyl ring of the core for inhibition of influenza virus neuraminidase activity and replication in vitro and in vivo. After evaluating the compounds for their ability to inhibit the viral neuraminidase, six potent inhibitors 3c, 3e, 7c, 12o, 12v, 18d were progressed to evaluate for cytotoxicity and inhibition of influenza virus A/PR/8/34 replication in in MDCK cells. Two hit compounds 3e and 12o were tested in an animal model of influenza virus infection. Molecular mechanism of the 3-indolinone derivatives interactions with the neuraminidase was revealed in molecular dynamic simulations. Proposed inhibitors bind to the 430-cavity that is different from the conventional binding site of commercial compounds. The most promising 3-indolinone inhibitors demonstrate stronger interactions with the neuraminidase in molecular models that supports proposed binding site.

SARS-CoV-2 uses Spike glycoprotein to control the host's anaerobic metabolism by inhibiting LDHB

The SARS-CoV-2 pandemic, responsible for approximately 7 million deaths worldwide, highlights the urgent need to understand the molecular mechanisms of the virus in order to prevent future outbreaks. The Spike glycoprotein of SARS-CoV-2, which is critical for viral entry through its interaction with ACE2 and other host cell receptors, has been a focus of this study. The present research goes beyond receptor recognition to explore Spike's influence on cellular metabolism. AP-MS interactome analysis revealed an interaction between the Spike S1 domain and lactate dehydrogenase B (LDHB), which was further confirmed by co-immunoprecipitation and immunofluorescence, indicating colocalisation in cells expressing the S1 domain. The study showed that Spike inhibits the catalytic activity of LDHB, leading to increased lactate levels in HEK-293T cells overexpressing the S1 subunit. In the hypothesised mechanism, Spike deprives LDHB of NAD+, facilitating a metabolic switch from aerobic to anaerobic energy production during infection. The Spike-NAD+ interacting region was characterised and mainly involves the W436 within the RDB domain. This novel hypothesis suggests that the Spike protein may play a broader role in altering host cell metabolism, thereby contributing to the pathophysiology of viral infection.

Preclinical alternative drug discovery programs for monogenic rare diseases. Should small molecules or gene therapy be used? The case of hereditary spastic paraplegias

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation

BACKGROUND

Locating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources.

METHODS

We present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-level 3D CNN outputs, our model overcomes the problems of CNN-only architectures. Second, by configuring the fundamental units of computation as residues and pockets instead of voxels, our method reduced the information loss from post-processing. Lastly, by employing inter-resolution transfer learning and homology-based augmentation, our method maximizes the utilization of available data sources to a significant extent.

RESULTS

The proposed method significantly outperformed all state-of-the-art baselines regarding both resolutions-pocket and residue. An ablation study demonstrated the indispensability of our proposed architecture, as well as transfer learning and homology-based augmentation, for achieving optimal performance. We further scrutinized our model's performance through a case study involving human serum albumin, which demonstrated our model's superior capability in identifying multiple binding sites of the protein, outperforming the existing methods.

CONCLUSIONS

We believe that our contribution to the literature is twofold. Firstly, we introduce a novel computational method for binding site prediction with practical applications, substantiated by its strong performance across diverse benchmarks and case studies. Secondly, the innovative aspects in our method- specifically, the design of the model architecture, inter-resolution transfer learning, and homology-based augmentation-would serve as useful components for future work.

Catalytic mechanism study of ATP-citrate lyase during citryl-CoA synthesis process

ATP-citrate lyase (ACLY) is a critical metabolic enzyme and promising target for drug development. The structure determinations of ACLY have revealed its homotetramer states with various subunit symmetries, but catalytic mechanism of ACLY tetramer and the importance of subunit symmetry have not been clarified. Here, we constructed the free energy landscape of ACLY tetramer with arbitrary subunit symmetries and investigated energetic and conformational coupling of subunits during citryl-CoA synthesis process. The optimal conformational pathway indicates that ACLY tetramer encounters three critical conformational barriers and undergoes a loss of rigid-D2 symmetry to gain an energetic advantage. Energetic coupling of conformational changes and biochemical reactions suggests that these biological events are not independent but rather coupled with each other, showing a comparable energy barrier to the experimental data for the rate-limiting step. These findings could contribute to further research on catalytic mechanism, functional modulation, and inhibitor design of ACLY.

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in both homeostatic and pathological states. It also participates in organogenesis, HIV-1 infection, and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. Activation of CXCR4 by CXCL12 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation to chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues in TMV and TMVI. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to identify negative allosteric modulators of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small molecule that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo while preserving ligand binding and receptor internalization.

Toward Unveiling Putative Binding Sites of Interleukin-33: Insights from Mixed-Solvent Molecular Dynamics Simulations of the Interleukin-1 Family

The interleukin (IL)-1 family is a major proinflammatory cytokine family, ranging from the well-studied IL-1s to the most recently discovered IL-33. As a new focus, IL-33 has attracted extensive research for its crucial immunoregulatory roles, leading to the development of notable monoclonal antibodies as clinical candidates. Efforts to develop small molecules disrupting IL-33/ST2 interaction remain highly desired but encounter challenges due to the shallow and featureless interfaces. The information from relative cytokines has shown that traditional binding site identification methods still struggle in mapping cryptic sites, necessitating dynamic approaches to uncover druggable pockets on IL-33. Here, we employed mixed-solvent molecular dynamics (MixMD) simulations with diverse-property probes to map the hotspots of IL-33 and identify potential binding sites. The protocol was first validated using the known binding sites of two IL-1 family members and then applied to the structure of IL-33. Our simulations revealed several binding sites and proposed side-chain rearrangements essential for the binding of a known inhibitor, aligning well with experimental NMR findings. Further microsecond-time scale simulations of this IL-33-protein complex unveiled distinct binding modes with varying occurrences. These results could facilitate future efforts in developing ligands to target challenging flexible pockets of IL-33 and IL-1 family cytokines in general.

A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma

Mucinous ovarian carcinoma (MOC) is a subtype of ovarian cancer that is distinct from all other ovarian cancer subtypes and currently has no targeted therapies. To identify novel therapeutic targets, we developed and applied a new method of differential network analysis comparing MOC to benign mucinous tumours (in the absence of a known normal tissue of origin). This method mapped the protein-protein network in MOC and then utilised structural bioinformatics to prioritise the proteins identified as upregulated in the MOC network for their likelihood of being successfully drugged. Using this protein-protein interaction modelling, we identified the strongest 5 candidates, CDK1, CDC20, PRC1, CCNA2 and TRIP13, as structurally tractable to therapeutic targeting by small molecules. siRNA knockdown of these candidates performed in MOC and control normal fibroblast cell lines identified CDK1, CCNA2, PRC1 and CDC20, as potential drug targets in MOC. Three targets (TRIP13, CDC20, CDK1) were validated using known small molecule inhibitors. Our findings demonstrate the utility of our pipeline for identifying new targets and highlight potential new therapeutic options for MOC patients.

Unveiling crucial amino acids in the carbohydrate recognition domain of a viral protein through a structural bioinformatic approach

Carbohydrate binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1's tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, a finding we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of protein-carbohydrate interactions.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model

Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction. The source code is available at https://github.com/ChunhuaLab/AllosES.

Protein ligand binding site prediction using graph transformer neural network

Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to be still insufficient. In this study, we introduce an approach that leverages a graph transformer neural network to rank the results of a geometry-based pocket detection method. We also created a larger training dataset compared to the conventionally used sc-PDB and investigated the correlation between the dataset size and prediction performance. Our findings indicate that utilizing a graph transformer-based method alongside a larger training dataset could enhance the performance of ligand binding site prediction.

Ribosomal translation of fluorinated non-canonical amino acids for de novo biologically active fluorinated macrocyclic peptides

Fluorination has emerged as a promising strategy in medicinal chemistry to improve the pharmacological profiles of drug candidates. Similarly, incorporating fluorinated non-canonical amino acids into macrocyclic peptides expands chemical diversity and enhances their pharmacological properties, from improved metabolic stability to enhanced cell permeability and target interactions. However, only a limited number of fluorinated non-canonical amino acids, which are canonical amino acid analogs, have been incorporated into macrocyclic peptides by ribosomes for de novo construction and target-based screening of fluorinated macrocyclic peptides. In this study, we report the ribosomal translation of a series of distinct fluorinated non-canonical amino acids, including mono-to tri-fluorinated variants, as well as fluorinated l-amino acids, d-amino acids, β-amino acids, etc. This enabled the de novo discovery of fluorinated macrocyclic peptides with high affinity for EphA2, and particularly the identification of those exhibiting broad-spectrum activity against Gram-negative bacteria by targeting the BAM complex. This study not only expands the scope of ribosomally translatable fluorinated amino acids but also underscores the versatility of fluorinated macrocyclic peptides as potent therapeutic agents.

Dual-site molecular glues for enhancing protein-protein interactions of the CDK12-DDB1 complex

Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues.

Odorant receptor orthologues from moths display conserved responses to cis-jasmone

In insects, the odorant receptor (OR) multigene family evolves by the birth-and-death evolutionary model, according to which the OR repertoire of each species has undergone specific gene gains and losses depending on their chemical environment, resulting in taxon-specific OR lineage radiations with different sizes in the phylogenetic trees. Despite the general divergence in the gene family across different insect orders, the ORs in moths seem to be genetically conserved across species, clustered into 23 major clades containing multiple orthologous groups with single-copy gene from each species. We hypothesized that ORs in these orthologous groups are tuned to ecologically important compounds and functionally conserved. cis-Jasmone is one of the compounds that not only primes the plant defense of neighboring receiver plants, but also functions as a behavior regulator to various insects. To test our hypothesis, using Xenopus oocyte recordings, we functionally assayed the orthologues of BmorOR56, which has been characterized as a specific receptor for cis-jasmone. Our results showed highly conserved response specificity of the BmorOR56 orthologues, with all receptors within this group exclusively responding to cis-jasmone. This is supported by the dN/dS analysis, showing that strong purifying selection is acting on this group. Moreover, molecular docking showed that the ligand binding pockets of BmorOR56 orthologues to cis-jasmone are similar. Taken together, our results suggest the high conservation of OR for ecologically important compounds across Heterocera.

High-throughput prioritization of target proteins for development of new antileishmanial compounds

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

Effect of Lipid Bilayer Anchoring on the Conformational Properties of the Cytochrome P450 2D6 Binding Site

Human cytochrome P450 (CYP) proteins metabolize 75% of small-molecule pharmaceuticals, which makes structure-based modeling of CYP metabolism and inhibition, bolstered by the current availability of X-ray crystal structures of CYP globular catalytic domains, an attractive prospect. Accounting for this broad metabolic capacity is a combination of the existence of multiple different CYP proteins and the capacity of a single CYP protein to metabolize multiple different small molecules. It is thought that structural plasticity and flexibility contribute to this latter property; therefore, incorporating diverse conformational states of a particular CYP is likely an important consideration in structure-based CYP metabolism and inhibition modeling. While all-atom explicit-solvent molecular dynamics simulations can be used to generate conformational ensembles under biologically relevant conditions, existing CYP crystal structures are of the globular domain only, whereas human CYPs contain N-terminal transmembrane and linker peptides that anchor the globular catalytic domain to the endoplasmic reticulum. To determine whether this can cause significant differences in the sampled binding site conformations, microsecond scale all-atom explicit-solvent molecular dynamics simulations of the CYP2D6 globular domain in an aqueous environment were compared with those of the full-length protein anchored in a POPC lipid bilayer. While bilayer-anchoring damped some structural fluctuations in the globular domain relative to the aqueous simulations, none of the affected residues included binding site pocket residues. Furthermore, clustering of molecular dynamics snapshots based on either pairwise binding site pocket RMSD or volume differences demonstrated a lack of separation of snapshots from the two simulation conditions into different clusters. These results suggest the substantially simpler and computationally cheaper aqueous simulation approach can be used to generate a relevant conformational ensemble of the CYP2D6 binding site for structure-based metabolism and inhibition modeling.

Oncogenic R248W mutation induced conformational perturbation of the p53 core domain and the structural protection by proteomimetic amyloid inhibitor ADH-6

The involvement of p53 aggregation in cancer pathogenesis emphasizes the importance of unraveling the mechanisms underlying mutation-induced p53 destabilization. And understanding how small molecule inhibitors prevent the conversion of p53 into aggregation-primed conformations is pivotal for the development of therapeutics targeting p53-aggregation-associated cancers. A recent experimental study highlights the efficacy of the proteomimetic amyloid inhibitor ADH-6 in stabilizing R248W p53 and inhibiting its aggregation in cancer cells by interacting with the p53 core domain (p53C). However, it remains mostly unclear how R248W mutation induces destabilization of p53C and how ADH-6 stabilizes this p53C mutant and inhibits its aggregation. Herein, we conducted all-atom molecular dynamics simulations of R248W p53C in the absence and presence of ADH-6, as well as that of wild-type (WT) p53C. Our simulations reveal that the R248W mutation results in a shift of helix H2 and β-hairpin S2-S2' towards the mutation site, leading to the destruction of their neighboring β-sheet structure. This further facilitates the formation of a cavity in the hydrophobic core, and reduces the stability of the β-sandwich. Importantly, two crucial aggregation-prone regions (APRs) S9 and S10 are disturbed and more exposed to solvent in R248W p53C, which is conducive to p53C aggregation. Intriguingly, ADH-6 dynamically binds to the mutation site and multiple destabilized regions in R248W p53C, partially inhibiting the shift of helix H2 and β-hairpin S2-S2', thus preventing the disruption of the β-sheets and the formation of the cavity. ADH-6 also reduces the solvent exposure of APRs S9 and S10, which disfavors the aggregation of R248W p53C. Moreover, ADH-6 can preserve the WT-like dynamical network of R248W p53C. Our study elucidates the mechanisms underlying the oncogenic R248W mutation induced p53C destabilization and the structural protection of p53C by ADH-6.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

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.

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We identified common resistance sites across type I, type II, and type I ½ inhibitors, unveiled unique resistance and sensitizing mutations for each inhibitor, and validated non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

A conserved pheromone receptor in the American and the Asian palm weevils is also activated by host plant volatiles

The evolution of chemosensory receptors is key for the adaptation of animals to their environment. Recent knowledge acquired on the tri-dimensional structure of insect odorant receptors makes it possible to study the link between modifications in the receptor structure and evolution of response spectra in more depth. We investigated this question in palm weevils, several species of which are well-known invasive pests of ornamental or cultivated palm trees worldwide. These insects use aggregation pheromones to gather on their host plants for feeding and reproduction. An odorant receptor detecting the aggregation pheromone components was characterised in the Asian palm weevil Rhynchophorus ferrugineus. This study compared the response spectra of this receptor, RferOR1, and its ortholog in the American palm weevil R. palmarum, RpalOR1. Sequences of these two receptors exhibit more than 70 amino acid differences, but modelling of their 3D structures revealed that their putative binding pockets differ by only three amino acids, suggesting possible tuning conservation. Further functional characterization of RpalOR1 confirmed this hypothesis, as RpalOR1 and RferOR1 exhibited highly similar responses to coleopteran aggregation pheromones and chemically related molecules. Notably, we showed that R. ferrugineus pheromone compounds strongly activated RpalOR1, but we did not evidence any response to the R. palmarum pheromone compound rhynchophorol. Moreover, we discovered that several host plant volatiles also activated both pheromone receptors, although with lower sensitivity. This study not only reveals evolutionary conservation of odorant receptor tuning across the two palm weevil species, but also questions the specificity of pheromone detection usually observed in insects.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

Cytochrome P450 Enzyme Design by Constraining the Catalytic Pocket in a Diffusion Model

Although cytochrome P450 enzymes are the most versatile biocatalysts in nature, there is insufficient comprehension of the molecular mechanism underlying their functional innovation process. Here, by combining ancestral sequence reconstruction, reverse mutation assay, and progressive forward accumulation, we identified 5 founder residues in the catalytic pocket of flavone 6-hydroxylase (F6H) and proposed a "3-point fixation" model to elucidate the functional innovation mechanisms of P450s in nature. According to this design principle of catalytic pocket, we further developed a de novo diffusion model (P450Diffusion) to generate artificial P450s. Ultimately, among the 17 non-natural P450s we generated, 10 designs exhibited significant F6H activity and 6 exhibited a 1.3- to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1. This work not only explores the design principle of catalytic pockets of P450s, but also provides an insight into the artificial design of P450 enzymes with desired functions.

LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification

BACKGROUND

Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes.

RESULTS

We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance.

SCIENTIFIC CONTRIBUTION

We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.

MolModa: accessible and secure molecular docking in a web browser

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.

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.

Enhanced mapping of small-molecule binding sites in cells

Photoaffinity probes are routinely utilized to identify proteins that interact with small molecules. However, despite this common usage, resolving the specific sites of these interactions remains a challenge. Here we developed a chemoproteomic workflow to determine precise protein binding sites of photoaffinity probes in cells. Deconvolution of features unique to probe-modified peptides, such as their tendency to produce chimeric spectra, facilitated the development of predictive models to confidently determine labeled sites. This yielded an expansive map of small-molecule binding sites on endogenous proteins and enabled the integration with multiplexed quantitation, increasing the throughput and dimensionality of experiments. Finally, using structural information, we characterized diverse binding sites across the proteome, providing direct evidence of their tractability to small molecules. Together, our findings reveal new knowledge for the analysis of photoaffinity probes and provide a robust method for high-resolution mapping of reversible small-molecule interactions en masse in native systems.

Modulating mycobacterial envelope integrity for antibiotic synergy with benzothiazoles

Developing effective tuberculosis drugs is hindered by mycobacteria's intrinsic antibiotic resistance because of their impermeable cell envelope. Using benzothiazole compounds, we aimed to increase mycobacterial cell envelope permeability and weaken the defenses of Mycobacterium marinum, serving as a model for Mycobacterium tuberculosis Initial hit, BT-08, significantly boosted ethidium bromide uptake, indicating enhanced membrane permeability. It also demonstrated efficacy in the M. marinum-zebrafish embryo infection model and M. tuberculosis-infected macrophages. Notably, BT-08 synergized with established antibiotics, including vancomycin and rifampicin. Subsequent medicinal chemistry optimization led to BT-37, a non-toxic and more potent derivative, also enhancing ethidium bromide uptake and maintaining synergy with rifampicin in infected zebrafish embryos. Mutants of M. marinum resistant to BT-37 revealed that MMAR_0407 (Rv0164) is the molecular target and that this target plays a role in the observed synergy and permeability. This study introduces novel compounds targeting a new mycobacterial vulnerability and highlights their cooperative and synergistic interactions with existing antibiotics.

Discovery and Development of NLRP3 Inhibitors Targeting the LRR Domain to Disrupt NLRP3-NEK7 Interaction for the Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease. Targeting NLRP3 inflammasome, specifically its interaction with NEK7 via the LRR domain of NLRP3, is a promising therapeutic strategy. Our research aimed to disrupt this interaction by focusing on the LRR domain. Through virtual screening, we identified five compounds with potent anti-inflammatory effects and ideal LRR binding affinity. Lead compound C878-1943 underwent structural optimization, yielding pyridoimidazole derivatives with different anti-inflammatory activities. Compound I-19 from the initial series effectively inhibited caspase-1 and IL-1β release in an adjuvant-induced arthritis (AIA) rat model, significantly reducing joint swelling and spleen/thymus indices. To further enhance potency and extend in vivo half-life, a second series including II-8 was developed, demonstrating superior efficacy and longer half-life. Both I-19 and II-8 bind to the LRR domain, inhibiting NLRP3 inflammasome activation. These findings introduce novel small molecule inhibitors targeting the LRR domain of NLRP3 protein and disrupt NLRP3-NEK7 interaction, offering a novel approach for RA treatment.

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.

S. C, Gangadhar S, A. S, Murali M, Shivamallu C, Achar RR, Silina E, Stupin V, Manturova N, Shati AA, Alfaifi MY, Elbehairi SEI, Kollur SP, Amruthesh KN. Exploration of Type III effector Xanthomonas outer protein Q (XopQ) inhibitor from Picrasma quassioides as an antibacterial agent using chemoinformatics analysis

The present study was focused on exploring the efficient inhibitors of closed state (form) of type III effector Xanthomonas outer protein Q (XopQ) (PDB: 4P5F) from the 44 phytochemicals of Picrasma quassioides using cutting-edge computational analysis. Among them, Kumudine B showed excellent binding energy (-11.0 kcal/mol), followed by Picrasamide A, Quassidine I and Quassidine J with the targeted closed state of XopQ protein compared to the reference standard drug (Streptomycin). The molecular dynamics (MD) simulations performed at 300 ns validated the stability of top lead ligands (Kumudine B, Picrasamide A, and Quassidine I)-bound XopQ protein complex with slightly lower fluctuation than Streptomycin. The MM-PBSA calculation confirmed the strong interactions of top lead ligands (Kumudine B and QuassidineI) with XopQ protein, as they offered the least binding energy. The results of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed that Quassidine I, Kumudine B and Picrasamide A were found to qualify most of the drug-likeness rules with excellent bioavailability scores compared to Streptomycin. Results of the computational studies suggested that Kumudine B, Picrasamide A, and Quassidine I could be considered potential compounds to design novel antibacterial drugs against X. oryzae infection. Further in vitro and in vivo antibacterial activities of Kumudine B, Picrasamide A, and Quassidine I are required to confirm their therapeutic potentiality in controlling the X. oryzae infection.

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

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.

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.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction

Accurate ligand binding site prediction (LBSP) within proteins is essential for drug discovery. We developed ProteinUNetResNetV2.0 (PUResNetV2.0), leveraging sparse representation of protein structures to improve LBSP accuracy. Our training dataset included protein complexes from 4729 protein families. Evaluations on benchmark datasets showed that PUResNetV2.0 achieved an 85.4% Distance Center Atom (DCA) success rate and a 74.7% F1 Score on the Holo801 dataset, outperforming existing methods. However, its performance in specific cases, such as RNA, DNA, peptide-like ligand, and ion binding site prediction, was limited due to constraints in our training data. Our findings underscore the potential of sparse representation in LBSP, especially for oligomeric structures, suggesting PUResNetV2.0 as a promising tool for computational drug discovery.

Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows

Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.

Label-free cell phenotypic profiling of histamine H4R receptor and discovery of non-competitive H4R antagonist from natural products

Histamine 4 receptor (H4R), the most recently identified subtype of histamine receptor, primarily induces inflammatory reactions upon activation. Several H4R antagonists have been developed for the treatment of inflammatory bowel disease (IBD) and atopic dermatitis (AD), but their use has been limited by adverse side effects, such as a short half-life and toxicity. Natural products, as an important source of anti-inflammatory agents, offer minimal side effects and reduced toxicity. This work aimed to identify novel H4R antagonists from natural products. An H4R target-pathway model deconvoluted downstream Gi and MAPK signaling pathways was established utilizing cellular label-free integrative pharmacology (CLIP), on which 148 natural products were screened. Cryptotanshinone was identified as selective H4R antagonist, with an IC50 value of 11.68 ± 1.30 μM, which was verified with Fluorescence Imaging Plate Reader (FLIPR) and Cellular Thermal Shift (CTS) assays. The kinetic binding profile revealed the noncompetitive antagonistic property of cryptotanshinone. Two allosteric binding sites of H4R were predicted using SiteMap, Fpocket and CavityPlus. Subsequent molecular docking and dynamics simulation indicated that cryptotanshinone interacts with H4R at a pocket formed by the outward interfaces between TM3/4/5, potentially representing a new allosteric binding site for H4R. Overall, this study introduced cryptotanshinone as a novel H4R antagonist, offering promise as a new hit for drug design of H4R antagonist. Additionally, this study provided a novel screening model for the discovery of H4R antagonists.

GTExome: Modeling commonly expressed missense mutations in the human genome

A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/.

Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

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.