Ligand representation Dotaz Zobrazit nápovědu
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.
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.
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.
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.
- MeSH
- adenokarcinom genetika metabolismus patologie MeSH
- apoptóza účinky léků genetika MeSH
- cytochromy c sekrece 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
AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 μM on neuroblastoma cells.
- MeSH
- histondeacetylasy * metabolismus MeSH
- inhibitory histondeacetylas * farmakologie chemie chemická syntéza MeSH
- lidé MeSH
- molekulární struktura MeSH
- nádorové buněčné linie MeSH
- neuroblastom * farmakoterapie patologie MeSH
- protinádorové látky * farmakologie chemie chemická syntéza MeSH
- racionální návrh léčiv * MeSH
- simulace molekulární dynamiky MeSH
- simulace molekulového dockingu MeSH
- umělá inteligence MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články 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 (R(2) = 0.49). However, the addition of the active-site waters resulted in significant improvement (R(2) = 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.
- 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
G-quadruplexes (G4) are non-canonical DNA and/or RNA secondary structures formed in guanine-rich regions. Given their over-representation in specific regions in the genome such as promoters and telomeres, they are likely to play important roles in key processes such as transcription, replication or RNA maturation. Putative G4-forming sequences (G4FS) have been reported in humans, yeast, bacteria, viruses and many organisms. Here we present the first mapping of G-quadruplex sequences in Dictyostelium discoideum, the social amoeba. 'Dicty' is an ameboid protozoan with a small (34 Mb) and extremely AT rich genome (78%). As a consequence, very few G4-prone motifs are expected. An in silico analysis of the Dictyostelium genome with the G4Hunter software detected 249-1055 G4-prone motifs, depending on G4Hunter chosen threshold. Interestingly, despite an even lower GC content (as compared to the whole Dicty genome), the density of G4 motifs in Dictyostelium promoters and introns is significantly higher than in the rest of the genome. Fourteen selected sequences located in important genes were characterized by a combination of biophysical and biochemical techniques. Our data show that these sequences form highly stable G4 structures under physiological conditions. Five Dictyostelium genes containing G4-prone motifs in their promoters were studied for the effect of a new G4-binding porphyrin derivative on their expression. Our results demonstrated that the new ligand significantly decreased their expression. Overall, our results constitute the first step to adopt Dictyostelium discoideum as a 'G4-poor' model for studies on G-quadruplexes.
Acid-Base Concepts 73 -- I CHAPTÍR 4 Exploring Proteins 77 -- 4.04 The Proteome Is the Functional Representation An Overview 396 -- 15.1 Seven-Transmembrane-Helix Receptors Change -- Conformation in Response to Ligand Binding and Activate G Proteins 398 -- 15.1.1 Ligand Binding to 7TM Receptors Leads to the -- Activation Which Stimulates Many Enzymes and Tranporters 410 -- 15.4 Some Receptors Dimerize in Response to Ligand Tails 884 -- 31.3.5 Histone Deacetylases Contribute to -- Transcriptional Repression 885 -- 31.3.6 Ligand
5th ed. xvii, 974 s. : il., tab., grafy ; 32 cm
- Konspekt
- Biochemie. Molekulární biologie. Biofyzika
- NLK Obory
- biochemie
Molecular Properties of the Acetylcholine-Gated Channel at the Nerve-Muscle Synapse Are Known 196 -- Ligand-Gated Readings 379 -- References 380 -- 20 From Nerve Cells to Cognition: -- The Internal Cellular Representation Integrates Five Major Approaches to the Study of Cognitive Function 383 -- The Brain Has an Orderly Representation -- Is the Basis of the Accuracy of -- Clinical Neurological Examinations 388 -- The Internal Representation of Personal Space Is Modifiable by Experience 388 -- The Cortical Representation of the Human Hand Area
4th ed. xxxiii, 1414 s. : il., tab., grafy ; 30 cm
- MeSH
- chování MeSH
- molekulární biologie MeSH
- nemoci nervového systému MeSH
- nervový systém MeSH
- neurochemie MeSH
- neurofyziologie MeSH
- neurony MeSH
- neurovědy MeSH
- Publikační typ
- monografie MeSH
- Konspekt
- Fyziologie člověka a srovnávací fyziologie
- NLK Obory
- neurovědy
- biologie
