Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models.

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 µs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.

Computer-aided discovery of novel aryl hydrocarbon receptor ligands to regulate CYP1A1 expression in inflammatory macrophages.

The environmental factor aryl hydrocarbon receptor (AhR), a key protein connecting the external environmental signals (e.g., environmental endocrine disruptor TCDD) to internal cellular processes, is involved in the activation of peripheral macrophages and inflammatory response in human body. Thus, there is widespread interest in finding compounds to anti-inflammatory response in macrophages by targeting human AhR. Here, ensemble docking based-virtual screening was first used to screen a library (~200,000 compounds) against human AhR ligand binding domain (LBD) and 25 compounds were identified as potential inhibitors. Then, 9 out of the 25 ligands were found to down-regulate the mRNA expression of CYP1A1 (a downstream gene of AhR signaling) in AhR overexpressing macrophages. The most potent compound AE-411/41415610 was selected for further study and found to reduce both mRNA and protein expressions level of CYP1A1 in mouse peritoneal macrophage. Moreover, protein chip signal pathway analysis indicated that AE-411/41415610 play a role in regulating JAK-STAT and AKT-mTOR pathways. In sum, the discovered hits with novel scaffolds provided a starting point for future design of more effective AhR-targeted lead compounds to regulate CYP1A1 expression of inflammatory peritoneal macrophages.

Building a novel TRUCK by harnessing the endogenous IFN-gamma promoter for cytokine expression.

Despite the remarkable success of chimeric antigen receptor (CAR) T therapy in hematological malignancies, its efficacy in solid tumors remains limited. Cytokine-engineered CAR T cells offer a promising avenue, yet their clinical translation is hindered by the risks associated with constitutive cytokine expression. In this proof-of-concept study, we leverage the endogenous interferon (IFN)-γ promoter for transgenic interleukin (IL)-15 expression. We demonstrate that IFN-γ expression is tightly regulated by T cell receptor signaling. By introducing an internal ribosome entry site IL15 into the 3' UTR of the IFN-γ gene via homology directed repair-mediated knock-in, we confirm that IL-15 expression can co-express with IFN-γ in an antigen stimulation-dependent manner. Importantly, the insertion of transgenes does not compromise endogenous IFN-γ expression. In vitro and in vivo data demonstrate that IL-15 driven by the IFN-γ promoter dramatically improves CAR T cells' antitumor activity, suggesting the effectiveness of IL-15 expression. Last, as a part of our efforts toward clinical translation, we have developed an innovative two-gene knock-in approach. This approach enables the simultaneous integration of CAR and IL-15 genes into TRAC and IFN-γ gene loci using a single AAV vector. CAR T cells engineered to express IL-15 using this approach demonstrate enhanced antitumor efficacy. Overall, our study underscores the feasibility of utilizing endogenous promoters for transgenic cytokines expression in CAR T cells.

OPTICS: An interactive online platform for photosensory and bio-functional proteins in optogenetic systems.

High-precise modulation of bio-functional proteins related to signaling is crucial in life sciences and human health. The cutting-edge technology of optogenetics, which combines optical method with genetically encoded protein expression, pioneered new pathways for the control of cellular bio-functional proteins (CPs) using optogenetic tools (OTs) in spatial and temporal. Over the past decade, hundreds of optogenetic systems (OSs) have been developed for various applications from living cells to freely moving organisms. However, no database has been constructed to comprehensively provide the valuable information of OSs yet. In this work, a new database named OPTICS (an interactive online platform for photosensory and bio-functional proteins in optogenetic systems) is introduced. Our OPTICS is unique in (i) systematically describing diverse OSs from the perspective of photoreceptor-based classification and mechanism of action, (ii) featuring the detailed biophysical properties and functional data of OSs, (iii) providing the interaction between OT and CP for each OS referring to distinct applications in research, diagnosis, and therapy, and (iv) enabling a light response property-based search against all OSs in the database. Since the information on OSs is essential for rapid and predictable design of optogenetic controls, the comprehensive data provided in OPTICS lay a solid foundation for the future development of novel OSs. OPTICS is freely accessible without login requirement at https://idrblab.org/optics/.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders.

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering.

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

Effective cryopreservation of human brain tissue and neural organoids.

Human brain tissue models and organoids are vital for studying and modeling human neurological disease. However, the high cost of long-term cultured organoids inhibits their wide-ranging application. It is therefore urgent to develop methods for the cryopreservation of brain tissue and organoids. Here, we establish a method using methylcellulose, ethylene glycol, DMSO, and Y27632 (termed MEDY) for the cryopreservation of cortical organoids without disrupting the neural cytoarchitecture or functional activity. MEDY can be applied to multiple brain-region-specific organoids, including the dorsal/ventral forebrain, spinal cord, optic vesicle brain, and epilepsy patient-derived brain organoids. Additionally, MEDY enables the cryopreservation of human brain tissue samples, and pathological features are retained after thawing. Transcriptomic analysis shows that MEDY can protect synaptic function and inhibit the endoplasmic reticulum-mediated apoptosis pathway. MEDY will enable the large-scale and reliable storage of diverse neural organoids and living brain tissue and will facilitate wide-ranging research, medical applications, and drug screening.

Improvement in Grain Size Distribution Uniformity for Nuclear-Grade Austenitic Stainless Steel through Thermomechanical Treatment.

In this work, thermomechanical treatment (single-pass rolling at 800 °C and solution treatment) was applied to nuclear-grade hot-rolled austenitic stainless steel to eliminate the mixed grain induced by the uneven hot-rolled microstructure. By employing high-temperature laser scanning confocal microscopy, microstructure evolution during solution treatment was observed in situ, and the effect of single-pass rolling reduction on it was investigated. In uneven hot-rolled microstructure, the millimeter-grade elongated grains (MEGs) possessed an extremely large size and a high Schmid factor for slip compared to the fine grains, which led to greater plastic deformation and increased dislocation density and deformation energy storage during single-pass rolling. During subsequent solution treatment, there were fewer nucleation sites for the new grain, and the grain boundary (GB) was the main nucleation site in MEGs at a lower rolling reduction. In contrast, at a higher reduction, increased uniformly distributed rolling deformation and more nucleation sites were developed in MEGs. As the reduction increased, the number of in-grain nucleation sites gradually exceeded that of GB nucleation sites, and in-grain nucleation preferentially occurred. This was beneficial for promoting the refinement of new recrystallized grains and a reduction in the size difference of new grains during recrystallization. The single-pass rolling reduction of 15-20% can effectively increase the nucleation sites and improve the uniformity of rolling deformation distribution in the MEGs, promote in-grain nucleation, and finally refine the abnormally coarse elongated grain, and eliminate the mixed-grain structure after solution treatment.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Design, Synthesis, and Biological Evaluation of ß-Trifluoroethoxydimethyl Selenides as Potent Antiosteoporosis Agents.

An efficient protocol for the synthesis of ß-trifluoroethoxydimethyl selenides was achieved under mild reaction conditions, and 39 compounds were prepared. All compounds were evaluated for their abilities to inhibit RANKL-induced osteoclastogenesis, compound 4aa exhibited the most potent activity. Further investigations revealed that 4aa could inhibit F-actin ring generation, bone resorption, and osteoclast-specific gene expression in vitro. Western blot analyses demonstrated that compound 4aa abrogated the RANKL-induced mitogen-activated protein kinase and NF-kB-signaling pathways. In addition, 4aa also displayed a notable impact on the osteoblastogenesis of MC3T3-E1 preosteoblasts. In vivo experiments revealed that compound 4aa significantly ameliorated bone loss in an ovariectomized (OVX) mice model. Furthermore, the surface plasmon resonance experiment results revealed that 4aa probably bound to RANKL. Collectively, the above-mentioned findings suggested that compound 4aa as a potential RANKL inhibitor averted OVX-triggered osteoporosis by regulating the inhibition of osteoclast differentiation and stimulation of osteoblast differentiation.

JMJD2A mediates transcriptional activation of SFRP4 and regulates oxidative stress and mitochondrial dysfunction in heart failure.

The importance of mitochondrial dysfunction and oxidative stress has been indicated in the progression of heart failure (HF). The molecular mechanisms, however, remain to be fully elucidated. This study aimed to explore the role and underlying mechanism of secreted frizzled-related protein 4 (SFRP4) in these two events in HF. Mice with HF were developed using transverse aortic constriction, and hematoxylin-eosin staining, MASSON staining, and Terminal deoxynucleotidyl transferase (TdT)-mediated 2'-Deoxyuridine 5'- Triphosphate nick end labeling (TUNEL assays) were conducted to detect morphological damage in the myocardial tissues of mice. HL-1 mouse cardiomyocytes were induced with isoproterenol (ISO), and cell viability and apoptosis were examined using cell counting kit-8 and TUNEL assays. SFRP4 and Jumonji domain-containing protein 2A (JMJD2A) were highly expressed in myocardial tissues. Suppression of SFRP4 alleviated apoptosis and fibrosis in myocardial tissues of mice. In addition, the extent of mitochondrial dysfunction and oxidative stress in damaged myocardial tissues and HL-1 cells was mitigated by SFRP4 inhibition as well. JMJD2A catalyzed demethylation modification of the SFRP4 promoter, thus promoting SFRP4 transcription in the development of HF. JMJD2A is responsible for SFRP4 transcription activation in the failing hearts of mice. Blockade of JMJD2A or SFRP4 might be a novel therapy effective in mitigating HF progression.

Structure-Based Discovery of the SARS-CoV-2 Main Protease Noncovalent Inhibitors from Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) has been extensively employed for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is demand for discovering more SARS-CoV-2 Mpro inhibitors with diverse scaffolds to optimize anti-SARS-CoV-2 lead compounds. In this study, comprehensive in silico and in vitro assays were utilized to determine the potential inhibitors from TCM compounds against SARS-CoV-2 Mpro, which is an important therapeutic target for SARS-CoV-2. The ensemble docking analysis of 18263 TCM compounds against 15 SARS-CoV-2 Mpro conformations identified 19 TCM compounds as promising candidates. Further in vitro testing validated three compounds as inhibitors of SARS-CoV-2 Mpro and showed IC50 values of 4.64 ± 0.11, 7.56 ± 0.78, and 11.16 ± 0.26 µM, with EC50 values of 12.25 ± 1.68, 15.58 ± 0.77, and 29.32 ± 1.25 µM, respectively. Molecular dynamics (MD) simulations indicated that the three complexes remained stable over the last 100 ns of production run. An analysis of the binding mode revealed that the active compounds occupy different subsites (S1, S2, S3, and S4) of the active site of SARS-CoV-2 Mpro via specific poses through noncovalent interactions with key amino acids (e.g., HIS 41, ASN 142, GLY 143, MET 165, GLU 166, or GLN 189). Overall, this study provides evidence indicating that the three natural products obtained from TCM could be further used for anti-COVID-19 research, justifying the investigation of Chinese herbal medicinal ingredients as bioactive constituents for therapeutic targets.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

SEPTIN2 suppresses an IFN-γ-independent, proinflammatory macrophage activation pathway.

Interferon-gamma (IFN-γ) signaling is necessary for the proinflammatory activation of macrophages but IFN-γ-independent pathways, for which the initiating stimuli and downstream mechanisms are lesser known, also contribute. Here we identify, by high-content screening, SEPTIN2 (SEPT2) as a negative regulation of IFN-γ-independent macrophage autoactivation. Mechanistically, endoplasmic reticulum (ER) stress induces the expression of SEPT2, which balances the competition between acetylation and ubiquitination of heat shock protein 5 at position Lysine 327, thereby alleviating ER stress and constraining M1-like polarization and proinflammatory cytokine release. Disruption of this negative feedback regulation leads to the accumulation of unfolded proteins, resulting in accelerated M1-like polarization, excessive inflammation and tissue damage. Our study thus uncovers an IFN-γ-independent macrophage proinflammatory autoactivation pathway and suggests that SEPT2 may play a role in the prevention or resolution of inflammation during infection.

Jian Yun Qing Hua Decoction inhibits malignant behaviors of gastric carcinoma cells via COL12A1 mediated ferroptosis signal pathway.

BACKGROUND: Jian Yun Qing Hua Decoction (JYQHD), a traditional Chinese medicine decoction, which has been applied in the treatment of gastric cancer (GC). We attempt to confirm the anti-gastric cancer effect of JYQHD and explore the mechanism of JYQHD. METHODS: Acute toxicity test was used to understand the toxicity of JYQHD. We studied the expression and prognostic outcome of COL12A1 within GC tissues through the network databases. Using several web-based databases, we analyzed the major components and targets of JYQHD, as well as known therapeutic targets in gastric cancer. The Venn diagram was utilized to obtain the overlapped genes. Lentiviral vector, shRNAs and plasmids, were used to transfect GC cells. Cell counting kit-8 (CCK8), sphere formation, malondialdehyde (MDA), glutathione (GSH), reactive oxygen species (ROS), Fe2+, transmission electron microscopy (TEM), quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), Western-Blot (WB), and immunohistochemical (IHC) assays were employed to investigate the role and mechanism of COL12A1 and JYQHD in GC. RESULTS: The results showed that JYQHD was non-toxic and safe. JYQHD inhibited growth and sphere formation ability through inducing the ferroptosis of GC cells, and suppressed the GC cells induced subcutaneous xenograft tumor growth. COL12A1 was highly expressed in gastric cancer tissues, indicating poor prognosis. COL12A1 specifically enhanced GC cell progression and stemness via suppressing ferroptosis. JYQHD down-regulated COL12A1 in order to suppress the stemness of GC cells via inducing ferroptosis. CONCLUSION: COL12A1 inhibited ferroptosis and enhanced stemness in GC cells. JYQHD inhibited the development of GC cells by inhibiting cancer cell stemness via the ferroptosis pathway mediated by COL12A1.

Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter.

Human dopamine transporter (hDAT) regulates the reuptake of extracellular dopamine (DA) and is an essential therapeutic target for central nervous system (CNS) diseases. The allosteric modulation of hDAT has been identified for decades. However, the molecular mechanism underlying the transportation is still elusive, which hinders the rational design of allosteric modulators against hDAT. Here, a systematic structure-based method was performed to explore allosteric sites on hDAT in inward-open (IO) conformation and to screen compounds with allosteric affinity. First, the model of the hDAT structure was constructed based on the recently reported Cryo-EM structure of the human serotonin transporter (hSERT) and Gaussian-accelerated molecular dynamics (GaMD) simulation was further utilized for the identification of intermediate energetic stable states of the transporter. Then, with the potential druggable allosteric site on hDAT in IO conformation, virtual screening of seven enamine chemical libraries (∼440,000 compounds) was processed, resulting in 10 compounds being purchased for in vitro assay and with Z1078601926 discovered to allosterically inhibit hDAT (IC50 = 0.527 [0.284; 0.988] µM) when nomifensine was introduced as an orthosteric ligand. Finally, the synergistic effect underlying the allosteric inhibition of hDAT by Z1078601926 and nomifensine was explored using additional GaMD simulation and postbinding free energy analysis. The hit compound discovered in this work not only provides a good starting point for lead optimization but also demonstrates the usability of the method for the structure-based discovery of novel allosteric modulators of other therapeutic targets.

The NSP4 T492I mutation increases SARS-CoV-2 infectivity by altering non-structural protein cleavage.

The historically dominant SARS-CoV-2 Delta variant and the currently dominant Omicron variants carry a T492I substitution within the non-structural protein 4 (NSP4). Based on in silico analyses, we hypothesized that the T492I mutation increases viral transmissibility and adaptability, which we confirmed with competition experiments in hamster and human airway tissue culture models. Furthermore, we showed that the T492I mutation increases the replication capacity and infectiveness of the virus and improves its ability to evade host immune responses. Mechanistically, the T492I mutation increases the cleavage efficiency of the viral main protease NSP5 by enhancing enzyme-substrate binding, which increases production of nearly all non-structural proteins processed by NSP5. Importantly, the T492I mutation suppresses viral-RNA-associated chemokine production in monocytic macrophages, which may contribute to the attenuated pathogenicity of Omicron variants. Our results highlight the importance of NSP4 adaptation in the evolutionary dynamics of SARS-CoV-2.

Computational protein design - where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Mapping synthetic binding proteins epitopes on diverse protein targets by protein structure prediction and protein-protein docking.

Synthetic binding proteins (SBPs) are a class of artificial proteins engineered from privileged protein scaffolds, which can form highly specific molecular recognition interfaces with a variety of targets. Due to the characteristics of small size, high stability, and good tissue permeability, SBPs have important applications in biomedical research, disease diagnosis and treatment. However, knowledge of SBPs epitopes on the structures of target proteins is still limited, which hinder the development of novel SBPs. In this study, based on the currently available information of SBPs and their targets, 96 pairs of interacting proteins referring to 96 representative SBPs and 80 different targets, were systemically investigated using the state-of-the-art computational modeling techniques including AlphaFold2 protein structure prediction and Rosetta protein-protein docking. As a result, 71 out of the 96 pairs were successfully docked, of which 18, 33, and 20 pairs were defined as models with high, medium, and acceptable quality, respectively. In addition, the interface information was analyzed to decipher the interaction types driven SBPs and targets recognition. Overall, this work not only provides important structural information for understanding the mechanism of action of other SBPs with same protein scaffold, but also for aiding the rational protein engineering and to design of novel SBPs with biomedical applications.