AlphaFold Dotaz Zobrazit nápovědu
- Klíčová slova
- Alphafold,
- MeSH
- databáze proteinů MeSH
- konformace proteinů * MeSH
AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and β hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
- Publikační typ
- časopisecké články MeSH
HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 μM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 μM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.
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.
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
The AlphaFold2 prediction algorithm opened up the possibility of exploring proteins' structural space at an unprecedented scale. Currently, >200 million protein structures predicted by this approach are deposited in AlphaFoldDB, covering entire proteomes of multiple organisms, including humans. Predicted structures are, however, stored without detailed functional annotations describing their chemical behaviour. Partial atomic charges, which map electron distribution over a molecule and provide a clue to its chemical reactivity, are an important example of such data. We introduce the web application αCharges: a tool for the quick calculation of partial atomic charges for protein structures from AlphaFoldDB. The charges are calculated by the recent empirical method SQE+qp, parameterised for this class of molecules using robust quantum mechanics charges (B3LYP/6-31G*/NPA) on PROPKA3 protonated structures. The computed partial atomic charges can be downloaded in common data formats or visualised via the powerful Mol* viewer. The αCharges application is freely available at https://alphacharges.ncbr.muni.cz with no login requirement.
- MeSH
- algoritmy MeSH
- konformace proteinů MeSH
- lidé MeSH
- proteiny * chemie MeSH
- proteom MeSH
- software * MeSH
- výpočetní biologie * přístrojové vybavení metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.
- Publikační typ
- časopisecké články MeSH
Adar null mutant mouse embryos die with aberrant double-stranded RNA (dsRNA)-driven interferon induction, and Adar Mavs double mutants, in which interferon induction is prevented, die soon after birth. Protein kinase R (Pkr) is aberrantly activated in Adar Mavs mouse pup intestines before death, intestinal crypt cells die, and intestinal villi are lost. Adar Mavs Eifak2 (Pkr) triple mutant mice rescue all defects and have long-term survival. Adenosine deaminase acting on RNA 1 (ADAR1) and PKR co-immunoprecipitate from cells, suggesting PKR inhibition by direct interaction. AlphaFold studies on an inhibitory PKR dsRNA binding domain (dsRBD)-kinase domain interaction before dsRNA binding and on an inhibitory ADAR1 dsRBD3-PKR kinase domain interaction on dsRNA provide a testable model of the inhibition. Wild-type or editing-inactive human ADAR1 expressed in A549 cells inhibits activation of endogenous PKR. ADAR1 dsRNA binding is required for, but is not sufficient for, PKR inhibition. Mutating the ADAR1 dsRBD3-PKR contact prevents co-immunoprecipitation, ADAR1 inhibition of PKR activity, and co-localization of ADAR1 and PKR in cells.
- MeSH
- adenosindeaminasa * metabolismus genetika MeSH
- aktivace enzymů MeSH
- buňky A549 MeSH
- dvouvláknová RNA * metabolismus MeSH
- kinasa eIF-2 * metabolismus MeSH
- lidé MeSH
- myši MeSH
- proteinové domény MeSH
- proteiny vázající RNA * metabolismus genetika MeSH
- vazba proteinů MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
The ISWI family protein SMARCA5 contains the ATP-binding pocket that coordinates the catalytic Mg2+ ion and water molecules for ATP hydrolysis. In this study, we demonstrate that SMARCA5 can also possess an alternative metal-binding ability. First, we isolated SMARCA5 on the cobalt column (IMAC) to near homogeneity. Examination of the interactions of SMARCA5 with metal-chelating supports showed that, apart from Co2+, it binds to Cu2+, Zn2+ and Ni2+. The efficiency of the binding to the last-listed metal was influenced by the chelating ligand, resulting in a strong preference for Ni-NTA over the Ni-CM-Asp equivalent. To gain insight in the preferential affinity for the Ni-NTA ligand, QM calculations were performed on model systems and metal-ligand complexes with a limited protein fragment of SMARCA5 containing the double-histidine (dHis) motif. The calculations correlated the observed affinity with the relative stability of the d-block metals to tetradentate ligand coordination over tridentate, as well as their overall octahedral coordination capacity. Likewise, binding free energies derived from model imidazole complexes mirrored the observed Ni-NTA/Ni-CM-Asp preferential affinity. Finally, similar calculations on complexes with a SMARCA5 peptide fragment derived from the AlphaFold structural prediction, captured almost accurately the expected relative stability of the TM complexes, and produced a large energetic separation (~10 kcal∙mol-1) between Ni-NTA and Ni-CM-Asp in favour of the former.
- MeSH
- adenosintrifosfatasy MeSH
- chromozomální proteiny, nehistonové metabolismus chemie MeSH
- kovy chemie metabolismus MeSH
- kvantová teorie MeSH
- lidé MeSH
- ligandy MeSH
- molekulární modely MeSH
- restrukturace chromatinu MeSH
- vazba proteinů * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Neurotropic pathogens, notably, herpesviruses, have been associated with significant neuropsychiatric effects. As a group, these pathogens can exploit molecular mimicry mechanisms to manipulate the host central nervous system to their advantage. Here, we present a systematic computational approach that may ultimately be used to unravel protein-protein interactions and molecular mimicry processes that have not yet been solved experimentally. Toward this end, we validate this approach by replicating a set of pre-existing experimental findings that document the structural and functional similarities shared by the human cytomegalovirus-encoded UL144 glycoprotein and human tumor necrosis factor receptor superfamily member 14 (TNFRSF14). We began with a thorough exploration of the Homo sapiens protein database using the Basic Local Alignment Search Tool (BLASTx) to identify proteins sharing sequence homology with UL144. Subsequently, we used AlphaFold2 to predict the independent three-dimensional structures of UL144 and TNFRSF14. This was followed by a comprehensive structural comparison facilitated by Distance-Matrix Alignment and Foldseek. Finally, we used AlphaFold-multimer and PPIscreenML to elucidate potential protein complexes and confirm the predicted binding activities of both UL144 and TNFRSF14. We then used our in silico approach to replicate the experimental finding that revealed TNFRSF14 binding to both B- and T-lymphocyte attenuator (BTLA) and glycoprotein domain and UL144 binding to BTLA alone. This computational framework offers promise in identifying structural similarities and interactions between pathogen-encoded proteins and their host counterparts. This information will provide valuable insights into the cognitive mechanisms underlying the neuropsychiatric effects of viral infections.
