Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Allosteric site identification, virtual screening and discovery of a sulfonamide Hsp110-STAT3 interaction inhibitor for the treatment of hypoxic pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a severe pulmonary vascular disorder marked by vascular remodeling, which is linked to the malignant phenotypes of pulmonary vascular cells. The prevailing therapeutic approaches for PAH tend to neglect the potential role of vascular remodeling, leading to the clinical prognosis remains poor. Previously, we first demonstrated that heat shock protein (Hsp110) was significantly activated to boost Hsp110-STAT3 interaction, which resulted in abnormal proliferation and migration of human pulmonary arterial endothelial cells (HPAECs) under hypoxia. In the present study, we initially postulated the allosteric site of Hsp110, performed a virtual screening and biological evaluation studies to discover novel Hsp110-STAT3 interaction inhibitors. Here, we identified compound 29 (AN-329/43448068) as the effective inhibitor of HPAECs proliferation and the Hsp110-STAT3 association with good druggability. In vitro, 29 significantly impeded the chaperone function of Hsp110 and the malignant phenotypes of HPAECs. In vivo, 29 remarkably attenuated pulmonary vascular remodeling and right ventricular hypertrophy in hypoxia-induced PAH rats (i.g). Altogether, our data support the conclusion that it not only provides a novel lead compound but also presents a promising approach for subsequent inhibitor development targeting Hsp110-STAT3 interaction.

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.

Rational fragment-based design of compounds targeting the PWWP domain of the HRP family

Lens epithelium-derived growth factor p75 (LEDGF/p75), member of the hepatoma-derived growth-factor-related protein (HRP) family, is a transcriptional co-activator and involved in several pathologies including HIV infection and malignancies such as MLL-rearranged leukemia. LEDGF/p75 acts by tethering proteins to the chromatin through its integrase binding domain. This chromatin interaction occurs between the PWWP domain of LEDGF/p75 and nucleosomes carrying a di- or trimethylation mark on histone H3 Lys36 (H3K36me2/3). Our aim is to rationally devise small molecule drugs capable of inhibiting such interaction. To bootstrap this development, we resorted to X-ray crystallography-based fragment screening (FBS-X). Given that the LEDGF PWWP domain crystals were not suitable for FBS-X, we employed crystals of the closely related PWWP domain of paralog HRP-2. As a result, as many as 68 diverse fragment hits were identified, providing a detailed sampling of the H3K36me2/3 pocket pharmacophore. Subsequent structure-guided fragment expansion in three directions yielded multiple compound series binding to the pocket, as verified through X-ray crystallography, nuclear magnetic resonance and differential scanning fluorimetry. Our best compounds have double-digit micromolar affinity and optimally sample the interactions available in the pocket, judging by the Kd-based ligand efficiency exceeding 0.5 kcal/mol per non-hydrogen atom. Beyond π-stacking within the aromatic cage of the pocket and hydrogen bonding, the best compounds engage in a σ-hole interaction between a halogen atom and a conserved water buried deep in the pocket. Notably, the binding pocket in LEDGF PWWP is considerably smaller compared to the related PWWP1 domains of NSD2 and NSD3 which feature an additional subpocket and for which nanomolar affinity compounds have been developed recently. The absence of this subpocket in LEDGF PWWP limits the attainable affinity. Additionally, these structural differences in the H3K36me2/3 pocket across the PWWP domain family translate into a distinct selectivity of the compounds we developed. Our top-ranked compounds are interacting with both homologous LEDGF and HRP-2 PWWP domains, yet they showed no affinity for the NSD2 PWWP1 and BRPF2 PWWP domains which belong to other PWWP domain subfamilies. Nevertheless, our developed compound series provide a strong foundation for future drug discovery targeting the LEDGF PWWP domain as they can further be explored through combinatorial chemistry. Given that the affinity of H3K36me2/3 nucleosomes to LEDGF/p75 is driven by interactions within the pocket as well as with the DNA-binding residues, we suggest that future compound development should target the latter region as well. Beyond drug discovery, our compounds can be employed to devise tool compounds to investigate the mechanism of LEDGF/p75 in epigenetic regulation.

Drug repositioning for rosacea disease: Biological TARGET identification, molecular docking, pharmacophore mapping, and molecular dynamics analysis

Rosacea is a chronic dermatological condition that currently lacks a clear treatment approach due to an uncomprehensive knowledge of its pathogenesis. The main obstacle lies in understanding its etiology and the mode of action of the different drugs used. This study aims to clarify these aspects by employing drug repositioning. Using an in silico approach, we performed a transcriptomic analysis comparing samples from individuals with diverse types of rosacea to those from healthy controls to identify genes deregulated in this disease. Subsequently, we realized molecular docking and molecular dynamics studies to assess the binding affinity of drugs currently used to treat rosacea and drugs that target proteins interacting with, and thus affecting, proteins deregulated in rosacea. Our findings revealed that the downregulation of SKAP2 and upregulation of S100A7A in rosacea, could be involved in the pathogenesis of the disease. Furthermore, considering the drugs currently used for rosacea management, we demonstrated stable interactions between isotretinoin and BFH772 with SKAP2, and permethrin and PAC-14028 with S100A7A. Similarly, considering drugs targeting SKAP2 and S100A7A interactome proteins, we found that pitavastatin and dasatinib exert stable interactions with SKAP2, and lovastatin and tirbanibulin with S100A7A. In addition, we determine that the types of bonds involved in the interactions were different in SKAP2 from S100A7A. The drug-SKAP2 interactions are hydrogen bonds, whereas the drug-S100A7A interactions are of the hydrophobic type. In conclusion, our study provides evidence for the possible contribution of SKAP2 and S100A7A to rosacea pathology. Furthermore, it provides significant information on the molecular interactions between drugs and these proteins, highlighting the importance of considering structural features and binding interactions in the design of targeted therapies for skin disorders such as rosacea.

PocketDTA: an advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets

MOTIVATION

Accurately predicting the drug-target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of 3D information, and poor interpretability.

RESULTS

To alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first 3 (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. In addition, it uses a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at: https://github.com/zhaolongNCU/PocketDTA.

In silico identification of putative druggable pockets in PRL3, a significant oncology target

Protein tyrosine phosphatases (PTP) have emerged as targets in diseases characterized by aberrant phosphorylations such as cancers. The activity of the phosphatase of regenerating liver 3, PRL3, has been linked to several oncogenic and metastatic pathways, particularly in breast, ovarian, colorectal, and blood cancers. Development of small molecules that directly target PRL3, however, has been challenging. This is partly due to the lack of structural information on how PRL3 interacts with its inhibitors. Here, computational methods are used to bridge this gap by evaluating the druggability of PRL3. In particular, web-based pocket prediction tools, DoGSite3 and FTMap, were used to identify binding pockets using structures of PRL3 currently available in the Protein Data Bank. Druggability assessment by molecular dynamics simulations with probes was also performed to validate these results and to predict the strength of binding in the identified pockets. While several druggable pockets were identified, those in the closed conformation show more promise given their volume and depth. These two pockets flank the active site loops and roughly correspond to pockets predicted by molecular docking in previous papers. Notably, druggability simulations predict the possibility of low nanomolar affinity inhibitors in these sites implying the potential to identify highly potent small molecule inhibitors for PRL3. Putative pockets identified here can be leveraged for high-throughput virtual screening to further accelerate the drug discovery against PRL3 and development of PRL3-directed therapeutics.

Structural basis for the ligand recognition and G protein subtype selectivity of kisspeptin receptor

Kisspeptin receptor (KISS1R), belonging to the class A peptide-GPCR family, plays a key role in the regulation of reproductive physiology after stimulation by kisspeptin and is regarded as an attractive drug target for reproductive diseases. Here, we demonstrated that KISS1R can couple to the Gi/o pathway besides the well-known Gq/11 pathway. We further resolved the cryo-electron microscopy (cryo-EM) structure of KISS1R-Gq and KISS1R-Gi complexes bound to the synthetic agonist TAK448 and structure of KISS1R-Gq complex bound to the endogenous agonist KP54. The high-resolution structures provided clear insights into mechanism of KISS1R recognition by its ligand and can facilitate the design of targeted drugs with high affinity to improve treatment effects. Moreover, the structural and functional analyses indicated that conformational differences in the extracellular loops (ECLs), intracellular loops (ICLs) of the receptor, and the "wavy hook" of the Gα subunit may account for the specificity of G protein coupling for KISS1R signaling.

Docking analysis of phenolic acid and flavonoids with selected TAS2R receptors and in vitro experiment

Cornelian cherry fruits contain a wide range of phenolic acids, flavonoids, and other secondary metabolites. Selected flavonoids may inhibit the perceiving of bitterness, however, the full mechanism with all TAS2R bitter taste receptors is not known. The aim of the study was to determine the inhibitory effect of Cornus mas phenolics against the bitterness receptors TAS2R13 and TAS2R3 through functional in vitro assays and coupling studies. The overall effect was validated by analysing the inhibition of the receptors activity in cells treated with tested cornelian cherry extracts. The strength of interaction with both TAS2R receptors varied between studied compounds with different binding affinity. Most compounds bonded with the TAS2R3 receptor through a long-distant hydrophobic interaction with Trp89A and π-π orbital overlapping-between phenolic and tryptophane aromatic rings. For TAS2R13 observed were various mechanisms of interaction with the compounds. Nonetheless, naringin and quercetin had most similar binding affinity to chloroquine and denatonium-the model agonists for the receptor.

Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets

SUMMARY

Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts.

AVAILABILITY AND IMPLEMENTATION

PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.

Valuation of the significant hypoglycemic activity of black currant anthocyanin extract by both starch structure transformation and glycosidase activity inhibition

The objective of this study was to investigate the impact of anthocyanin-rich black currant extract (BCE) on the structural properties of starch and the inhibition of glycosidases, gathering data and research evidence to support the use of low glycemic index (GI) foods. The BCE induced a change in the starch crystal structure from A-type to V-type, resulting in a drop in digestibility from 81.41 % to 65.57 %. Furthermore, the inhibitory effects of BCE on glycosidases activity (α-glucosidase: IC50 = 0.13 ± 0.05 mg/mL and α-amylase: IC50 = 2.67 ± 0.16 mg/mL) by inducing a change in spatial conformation were confirmed through in vitro analysis. The presence of a 5'-OH group facilitated the interaction between anthocyanins and receptors of amylose, α-amylase, and α-glucosidase. The glycosyl moiety enhanced the affinity for amylose yet lowered the inhibitory effect on α-amylase. The in vivo analysis demonstrated that BCE resulted in a reduction of 3.96 mM·h in blood glucose levels (Area Under Curve). The significant hypoglycemic activity, particularly the decrease in postprandial blood glucose levels, highlights the potential of utilizing BCE in functional foods for preventing diabetes.

User-centric design of a 3D search interface for protein-ligand complexes

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine's new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine's 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server ( https://proteins.plus ).

Undescribed Cyclohexene and Benzofuran Alkenyl Derivatives from Choerospondias axillaris, a Potential Hypoglycemic Fruit

The fruit of Choerospondias axillaris (Anacardiaceae), known as south wild jujube in China, has been consumed widely in several regions of the world to produce fruit pastille and leathers, juice, jam, and candy. A comprehensive chemical study on the fresh fruits led to the isolation and identification of 18 compounds, including 7 new (1-7) and 11 known (8-18) comprised of 5 alkenyl (cyclohexenols and cyclohexenones) derivatives (1-5), 3 benzofuran derivatives (6-8), 6 flavonoids (9-14) and 4 lignans (15-18). Their structures were elucidated by extensive spectroscopic analysis. The known lignans 15-18 were isolated from the genus Choerospondias for the first time. Most of the isolates exhibited significant inhibitory activity on α-glucosidase with IC50 values from 2.26 ± 0.06 to 43.9 ± 0.96 μM. Molecular docking experiments strongly supported the potent α-glucosidase inhibitory activity. The results indicated that C. axillaris fruits could be an excellent source of functional foods that acquire potential hypoglycemic bioactive components.

SiteMine: Large-scale binding site similarity searching in protein structure databases

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

Novel SK channel positive modulators prevent ferroptosis and excitotoxicity in neuronal cells

Small conductance calcium-activated potassium (SK) channel activity has been proposed to play a role in the pathology of several neurological diseases. Besides regulating plasma membrane excitability, SK channel activation provides neuroprotection against ferroptotic cell death by reducing mitochondrial Ca2+ uptake and reactive oxygen species (ROS). In this study, we employed a multifaceted approach, integrating structure-based and computational techniques, to strategically design and synthesize an innovative class of potent small-molecule SK2 channel modifiers through highly efficient multicomponent reactions (MCRs). The compounds' neuroprotective activity was compared with the well-studied SK positive modulator, CyPPA. Pharmacological SK channel activation by selected compounds confers neuroprotection against ferroptosis at low nanomolar ranges compared to CyPPA, that mediates protection at micromolar concentrations, as shown by an MTT assay, real-time cell impedance measurements and propidium iodide staining (PI). These novel compounds suppress increased mitochondrial ROS and Ca2+ level induced by ferroptosis inducer RSL3. Moreover, axonal degeneration was rescued by these novel SK channel activators in primary mouse neurons and they attenuated glutamate-induced neuronal excitability, as shown via microelectrode array. Meanwhile, functional afterhyperpolarization of the novel SK2 channel modulators was validated by electrophysiological measurements showing more current change induced by the novel modulators than the reference compound, CyPPA. These data support the notion that SK2 channel activation can represent a therapeutic target for brain diseases in which ferroptosis and excitotoxicity contribute to the pathology.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Ligand-binding properties of XaffOBP9, a Minus-C odorant-binding protein from Xyleborus affinis (Coleoptera: Curculionidae: Scolytinae)

Xyleborus affinis, one of the most important pests of rubber trees, has caused severe damage to the natural rubber industry in Hainan province. The ability to detect host plants through a sensitive and specific olfactory system is crucial for Xyleborus affinis. Odorant binding proteins (OBPs) are believed to bind and carry hydrophobic active compounds from the environment to the surface of olfactory receptor neurons. To investigate the potential functional role of the highly expressed XaffOBP9 in binding with semiochemicals, we cloned and analyzed the cDNA sequence of XaffOBP9. The results showed that XaffOBP9 contains a 411bp open reading frame that encodes 136 amino acids. Then XaffOBP9 was expressed in Escherichia coli. The binding affinity of the recombinant OBP to 15 different ligands (14 host plant volatiles and 1 aggregation pheromone) was then examined using a fluorescence competitive binding approach. The results demonstrated that XaffOBP9 exhibited broad binding capabilities and strong affinities for 14 ligands. The structure of XaffOBP9 and its interactions with fourteen ligands were further analyzed by modeling and molecular docking, respectively. Based on the docking result, we found hydrophobic interactions are important between XaffOBP9 to these ligands and three amino acid residues (L71, Y106, and L114) were highly overlapped and contributed to the interaction with ligands. Mutation functional assays confirmed that the mutant L114A showed significantly reduced binding capacity to these ligands. This study suggested that XaffOBP9 may be involved in the chemoreception of semiochemicals and that it is helpful for the integrated management of X. affinis.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape

Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔGTOTAL = - 30.78 kcal/mol) and 74977521 (ΔGTOTAL = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.

PoseEdit: enhanced ligand binding mode communication by interactive 2D diagrams

In this article, we present PoseEdit, a new, interactive frontend of the popular pose visualization tool PoseView. PoseEdit automatically produces high-quality 2D diagrams of intermolecular interactions in 3D binding sites calculated from ligands in complex with protein, DNA, and RNA. The PoseView diagrams have been improved in several aspects, most notably in their interactivity. Thanks to the easy-to-use 2D editor of PoseEdit, the diagrams are extensively editable and extendible by the user, can be merged with other diagrams, and even be created from scratch. A large variety of graphical objects in the diagram can be moved, rotated, selected and highlighted, mirrored, removed, or even newly added. Furthermore, PoseEdit enables a synchronized 2D-3D view of macromolecule-ligand complexes simplifying the analysis of structural features and interactions. The representation of individual diagram objects regarding their visualized chemical properties, like stereochemistry, and general graphical styles, like the color of interactions, can additionally be edited. The primary objective of PoseEdit is to support scientists with an enhanced way to communicate ligand binding mode information through graphical 2D representations optimized with the scientist's input in accordance with objective criteria and individual needs. PoseEdit is freely available on the ProteinsPlus web server ( https://proteins.plus ).

Searching similar local 3D micro-environments in protein structure databases with MicroMiner

The available protein structure data are rapidly increasing. Within these structures, numerous local structural sites depict the details characterizing structure and function. However, searching and analyzing these sites extensively and at scale poses a challenge. We present a new method to search local sites in protein structure databases using residue-defined local 3D micro-environments. We implemented the method in a new tool called MicroMiner and demonstrate the capabilities of residue micro-environment search on the example of structural mutation analysis. Usually, experimental structures for both the wild-type and the mutant are unavailable for comparison. With MicroMiner, we extracted $>255 \times 10^{6}$ amino acid pairs in protein structures from the PDB, exemplifying single mutations' local structural changes for single chains and $>45 \times 10^{6}$ pairs for protein-protein interfaces. We further annotate existing data sets of experimentally measured mutation effects, like $\Delta \Delta G$ measurements, with the extracted structure pairs to combine the mutation effect measurement with the structural change upon mutation. In addition, we show how MicroMiner can bridge the gap between mutation analysis and structure-based drug design tools. MicroMiner is available as a command line tool and interactively on the https://proteins.plus/ webserver.