Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

• Klíčová slova
• 3D pharmacophore hash, 3D pharmacophore signatures, ligand-based modeling, pharmacophore modeling,
• MeSH
• antagonisté adenosinového receptoru A2 chemie   MeSH
• cholinesterasové inhibitory chemie   MeSH
• inhibitory cytochromu P450 CYP3A chemie   MeSH
• ligandy MeSH
• molekulární modely * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• antagonisté adenosinového receptoru A2 MeSH
• cholinesterasové inhibitory MeSH
• inhibitory cytochromu P450 CYP3A MeSH
• ligandy MeSH

BACKGROUND: Protein structures and their interaction with ligands have been in the focus of biochemistry and structural biology research for decades. The transportation of ligand into the protein active site is often complex process, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter and impasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path. RESULTS: To address the needs of the domain experts we design an explorative visualization solution based on a multi-scale simplification model. It helps to navigate the user to the most interesting parts of the ligand trajectory by exploring different attributes of the ligand and its movement, such as its distance to the active site, changes of amino acids lining the ligand, or ligand "stuckness". The process is supported by three linked views - 3D representation of the simplified trajectory, scatterplot matrix, and bar charts with line representation of ligand-lining amino acids. CONCLUSIONS: The usage of our tool is demonstrated on molecular dynamics simulations provided by the domain experts. The tool was tested by the domain experts from protein engineering and the results confirm that it helps to navigate the user to the most interesting parts of the ligand trajectory and to understand the ligand behavior.

• Klíčová slova
• Bioinformatics visualization, Computational proteomics, Molecular visualization,
• MeSH
• aminokyseliny chemie   MeSH
• katalytická doména MeSH
• konformace proteinů MeSH
• ligandy MeSH
• molekulární modely MeSH
• počítačové zpracování obrazu MeSH
• proteiny chemie   MeSH
• simulace molekulární dynamiky * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• aminokyseliny MeSH
• ligandy MeSH
• proteiny MeSH

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

• MeSH
• konformace proteinů MeSH
• ligandy MeSH
• molekulární modely * MeSH
• proteiny * chemie   metabolismus   MeSH
• racionální návrh léčiv * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• ligandy MeSH
• proteiny * MeSH

• MeSH
• biologické modely MeSH
• kompetitivní vazba MeSH
• radioligandová zkouška metody   MeSH
• Publikační typ
• časopisecké články MeSH

Neural crest cells (NCCs) are migratory, multipotent embryonic cells that are unique to vertebrates and form an array of clade-defining adult features. The evolution of NCCs has been linked to various genomic events, including the evolution of new gene-regulatory networks1,2, the de novo evolution of genes3 and the proliferation of paralogous genes during genome-wide duplication events4. However, conclusive functional evidence linking new and/or duplicated genes to NCC evolution is lacking. Endothelin ligands (Edns) and endothelin receptors (Ednrs) are unique to vertebrates3,5,6, and regulate multiple aspects of NCC development in jawed vertebrates7-10. Here, to test whether the evolution of Edn signalling was a driver of NCC evolution, we used CRISPR-Cas9 mutagenesis11 to disrupt edn, ednr and dlx genes in the sea lamprey, Petromyzon marinus. Lampreys are jawless fishes that last shared a common ancestor with modern jawed vertebrates around 500 million years ago12. Thus, comparisons between lampreys and gnathostomes can identify deeply conserved and evolutionarily flexible features of vertebrate development. Using the frog Xenopus laevis to expand gnathostome phylogenetic representation and facilitate side-by-side analyses, we identify ancient and lineage-specific roles for Edn signalling. These findings suggest that Edn signalling was activated in NCCs before duplication of the vertebrate genome. Then, after one or more genome-wide duplications in the vertebrate stem, paralogous Edn pathways functionally diverged, resulting in NCC subpopulations with different Edn signalling requirements. We posit that this new developmental modularity facilitated the independent evolution of NCC derivatives in stem vertebrates. Consistent with this, differences in Edn pathway targets are associated with differences in the oropharyngeal skeleton and autonomic nervous system of lampreys and modern gnathostomes. In summary, our work provides functional genetic evidence linking the origin and duplication of new vertebrate genes with the stepwise evolution of a defining vertebrate novelty.

• MeSH
• buněčný rodokmen MeSH
• crista neuralis cytologie   MeSH
• endoteliny genetika   metabolismus   MeSH
• hlava růst a vývoj   MeSH
• kosti a kostní tkáň cytologie   metabolismus   MeSH
• larva růst a vývoj   MeSH
• ligandy MeSH
• molekulární evoluce * MeSH
• Petromyzon genetika   růst a vývoj   metabolismus   MeSH
• receptory endotelinů nedostatek   genetika   metabolismus   MeSH
• signální transdukce * MeSH
• srdce růst a vývoj   MeSH
• vývoj kostí MeSH
• Xenopus genetika   růst a vývoj   metabolismus   MeSH
• zvířata MeSH
• Check Tag
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• srovnávací studie MeSH
• Názvy látek
• endoteliny MeSH
• ligandy MeSH
• receptory endotelinů MeSH

Docosahexaenoic acid (DHA), an n-3 polyunsaturated fatty acid present in fish oil, may exert cytotoxic and/or cytostatic effects on colon cancer cells when applied individually or in combination with some anticancer drugs. Here we demonstrate a selective ability of subtoxic doses of DHA to enhance antiproliferative and apoptotic effects of clinically useful cytokine TRAIL (tumor necrosis factor-related apoptosis inducing ligand) in cancer but not normal human colon cells. DHA-mediated stimulation of TRAIL-induced apoptosis was associated with extensive engagement of mitochondrial pathway (Bax/Bak activation, drop of mitochondrial membrane potential, cytochrome c release), activation of endoplasmic reticulum stress response (CHOP upregulation, changes in PERK level), decrease of cellular inhibitor of apoptosis protein (XIAP, cIAP1) levels and significant changes in sphingolipid metabolism (intracellular levels of ceramides, hexosyl ceramides, sphingomyelines, sphingosines; HPLC/MS/MS). Interestingly, we found significant differences in representation of various classes of ceramides (especially C16:0, C24:1) between the cancer and normal colon cells treated with DHA and TRAIL, and suggested their potential role in the regulation of the cell response to the drug combination. These study outcomes highlight the potential of DHA for a new combination therapy with TRAIL for selective elimination of colon cancer cells via simultaneous targeting of multiple steps in apoptotic pathways.

• Klíčová slova
• Apoptosis, Colon cancer, Docosahexaenoic acid, Lipid metabolism, TRAIL,
• MeSH
• adenokarcinom genetika   metabolismus   patologie   MeSH
• apoptóza účinky léků   genetika   MeSH
• cytochromy c metabolismus   MeSH
• inhibitory apoptózy MeSH
• kinasa eIF-2 genetika   metabolismus   MeSH
• kyseliny dokosahexaenové farmakologie   MeSH
• lidé MeSH
• membránový potenciál mitochondrií účinky léků   MeSH
• mitochondrie účinky léků   metabolismus   MeSH
• nádorové buněčné linie MeSH
• nádory tračníku genetika   metabolismus   patologie   MeSH
• protein Bak genetika   metabolismus   MeSH
• protein TRAIL farmakologie   MeSH
• protein X asociovaný s bcl-2 genetika   metabolismus   MeSH
• regulace genové exprese u nádorů * MeSH
• sfingolipidy chemie   klasifikace   metabolismus   MeSH
• signální transdukce MeSH
• stres endoplazmatického retikula účinky léků   MeSH
• synergismus léků MeSH
• transkripční faktor CHOP genetika   metabolismus   MeSH
• X-vázaný inhibitor apoptózy genetika   metabolismus   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• BAK1 protein, human MeSH Prohlížeč
• BAX protein, human MeSH Prohlížeč
• cytochromy c MeSH
• DDIT3 protein, human MeSH Prohlížeč
• EIF2AK3 protein, human MeSH Prohlížeč
• inhibitory apoptózy MeSH
• kinasa eIF-2 MeSH
• kyseliny dokosahexaenové MeSH
• protein Bak MeSH
• protein TRAIL MeSH
• protein X asociovaný s bcl-2 MeSH
• sfingolipidy MeSH
• TNFSF10 protein, human MeSH Prohlížeč
• transkripční faktor CHOP MeSH
• X-vázaný inhibitor apoptózy MeSH
• XIAP protein, human MeSH Prohlížeč

LiteMol suite is an innovative solution that enables near-instant delivery of model and experimental biomacromolecular structural data, providing users with an interactive and responsive experience in all modern web browsers and mobile devices. LiteMol suite is a combination of data delivery services (CoordinateServer and DensityServer), compression format (BinaryCIF), and a molecular viewer (LiteMol Viewer). The LiteMol suite is integrated into Protein Data Bank in Europe (PDBe) and other life science web applications (e.g., UniProt, Ensemble, SIB, and CNRS services), it is freely available at https://litemol.org , and its source code is available via GitHub. LiteMol suite provides advanced functionality (annotations and their visualization, powerful selection features), and this chapter will describe their use for visual inspection of protein structures.

• Klíčová slova
• Atom selection, Electron density, Ligand representation, Protein visualization, Validation report,
• MeSH
• databáze proteinů MeSH
• internet MeSH
• internetový prohlížeč MeSH
• konformace proteinů * MeSH
• proteiny chemie   MeSH
• software MeSH
• uživatelské rozhraní počítače MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa MeSH
• Názvy látek
• proteiny MeSH

Hydrogen/deuterium exchange (HDX) is a well-established analytical technique that enables monitoring of protein dynamics and interactions by probing the isotope exchange of backbone amides. It has virtually no limitations in terms of protein size, flexibility, or reaction conditions and can thus be performed in solution at different pH values and temperatures under controlled redox conditions. Thanks to its coupling with mass spectrometry (MS), it is also straightforward to perform and has relatively high throughput, making it an excellent complement to the high-resolution methods of structural biology. Given the recent expansion of artificial intelligence-aided protein structure modeling, there is considerable demand for techniques allowing fast and unambiguous validation of in silico predictions; HDX-MS is well-placed to meet this demand. Here we present a protocol for HDX-MS and illustrate its use in characterizing the dynamics and structural changes of a dimeric heme-containing oxygen sensor protein as it responds to changes in its coordination and redox state. This allowed us to propose a mechanism by which the signal (oxygen binding to the heme iron in the sensing domain) is transduced to the protein's functional domain.

• Klíčová slova
• Globin-coupled histidine kinase, Heme-containing oxygen sensors, Hydrogen/deuterium exchange, Ligand binding, Mass spectrometry, Protein conformational dynamics, Signal transduction,
• MeSH
• deuterium MeSH
• hem chemie   MeSH
• hemoproteiny * MeSH
• hmotnostní spektrometrie metody   MeSH
• kyslík metabolismus   MeSH
• umělá inteligence MeSH
• vodík-deuteriová výměna metody   MeSH
• vodík chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• deuterium MeSH
• hem MeSH
• hemoproteiny * MeSH
• kyslík MeSH
• vodík MeSH

We present comprehensive testing of solvent representation in quantum mechanics (QM)-based scoring of protein-ligand affinities. To this aim, we prepared 21 new inhibitors of cyclin-dependent kinase 2 (CDK2) with the pyrazolo[1,5-a]pyrimidine core, whose activities spanned three orders of magnitude. The crystal structure of a potent inhibitor bound to the active CDK2/cyclin A complex revealed that the biphenyl substituent at position 5 of the pyrazolo[1,5-a]pyrimidine scaffold was located in a previously unexplored pocket and that six water molecules resided in the active site. Using molecular dynamics, protein-ligand interactions and active-site water H-bond networks as well as thermodynamics were probed. Thereafter, all the inhibitors were scored by the QM approach utilizing the COSMO implicit solvent model. Such a standard treatment failed to produce a correlation with the experiment (R2 = 0.49). However, the addition of the active-site waters resulted in significant improvement (R2 = 0.68). The activities of the compounds could thus be interpreted by taking into account their specific noncovalent interactions with CDK2 and the active-site waters. In summary, using a combination of several experimental and theoretical approaches we demonstrate that the inclusion of explicit solvent effects enhance QM/COSMO scoring to produce a reliable structure-activity relationship with physical insights. More generally, this approach is envisioned to contribute to increased accuracy of the computational design of novel inhibitors.

• Klíčová slova
• ATP-competitive type I inhibitors, Cyclin-dependent kinase 2, Molecular dynamics, Protein-ligand binding, Pyrazolo[1,5-a]pyrimidine, Quantum mechanical scoring, Water thermodynamics, X-ray crystal structure,
• MeSH
• cyklin A metabolismus   MeSH
• cyklin-dependentní kinasa 2 antagonisté a inhibitory   chemie   metabolismus   MeSH
• inhibitory proteinkinas chemie   metabolismus   farmakologie   MeSH
• katalytická doména * MeSH
• kvantová teorie * MeSH
• lidé MeSH
• pyrimidiny chemie   metabolismus   farmakologie   MeSH
• racionální návrh léčiv MeSH
• rozpouštědla chemie   MeSH
• simulace molekulární dynamiky MeSH
• voda chemie   MeSH
• vztahy mezi strukturou a aktivitou MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• cyklin A MeSH
• cyklin-dependentní kinasa 2 MeSH
• inhibitory proteinkinas MeSH
• pyrimidiny MeSH
• rozpouštědla MeSH
• voda MeSH

BACKGROUND: Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment. OBJECTIVES: This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication. METHODS: A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation. RESULTS: The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability. CONCLUSION: By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

• Klíčová slova
• AI-based screening, Biomarker-driven classification, Breast cancer diagnosis, Ensemble learning, Machine learning in healthcare, Multi-grade prediction,
• MeSH
• alfa-fetoproteiny analýza   MeSH
• algoritmy * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádorové biomarkery * krev   MeSH
• nádory prsu * diagnóza   mortalita   MeSH
• prognóza MeSH
• senioři MeSH
• strojové učení * MeSH
• stupeň nádoru MeSH
• umělá inteligence MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• alfa-fetoproteiny MeSH
• nádorové biomarkery * MeSH