Expanding the available RNA labeling toolbox with CutA nucleotidyltransferase for efficient transcript labeling with purine and pyrimidine nucleotide analogs.

RNA labeling is an invaluable tool for investigation of the function and localization of nucleic acids. Labels are commonly incorporated into 3' end of RNA and the primary enzyme used for this purpose is RNA poly(A) polymerase (PAP), which belongs to the class of terminal nucleotidyltransferases (NTases). However, PAP preferentially adds ATP analogs, thus limiting the number of available substrates. Here, we report the use of another NTase, CutA from the fungus Thielavia terrestris. Using this enzyme, we were able to incorporate into the 3' end of RNA not only purine analogs, but also pyrimidine analogs. We engaged strain-promoted azide-alkyl cycloaddition (SPAAC) to obtain fluorescently labeled or biotinylated transcripts from RNAs extended with azide analogs by CutA. Importantly, modified transcripts retained their biological properties. Furthermore, fluorescently labeled mRNAs were suitable for visualization in cultured mammalian cells. Finally, we demonstrate that either affinity studies or molecular dynamic (MD) simulations allow for rapid screening of NTase substrates, what opens up new avenues in the search for the optimal substrates for this class of enzymes.

Substrate Specificity of T7 RNA Polymerase toward Hypophosphoric Analogues of ATP.

Modified nucleotides are commonly used in molecular biology as substrates or inhibitors for several enzymes but also as tools for the synthesis of modified DNA and RNA fragments. Introduction of modification into RNA, such as phosphorothioate (PS), has been demonstrated to provide higher stability, more effective transport, and enhanced activity of potential therapeutic molecules. Hence, in order to achieve widespread use of RNA molecules in medicine, it is crucial to continuously refine the techniques that enable the effective introduction of modifications into RNA strands. Numerous analogues of nucleotides have been tested for their substrate activity with the T7 RNA polymerase and therefore in the context of their utility for use in in vitro transcription. In the present studies, the substrate preferences of the T7 RNA polymerase toward ß,γ-hypophospho-modified ATP derivatives for the synthesis of unmodified RNA and phosphorothioate RNA (PS) are presented. The performed studies revealed the stereoselectivity of this enzyme for α-thio-ß,γ-hypo-ATP derivatives, similar to that for α-thio-ATP. Additionally, it is demonstrated herein that hypodiphosphoric acid may inhibit in vitro transcription catalyzed by T7 RNA polymerase.

Lysophosphatidylcholines Enriched with cis and trans Palmitoleic Acid Regulate Insulin Secretion via GPR119 Receptor.

Among lipids, lysophosphatidylcholines (LPCs) with various fatty acyl chains have been identified as potential agonists of G protein-coupled receptors (GPCRs). Recently, targeting GPCRs has been switched to diabetes and obesity. Concomitantly, our last findings indicate the insulin secretagogue properties of cis and trans palmitoleic acid (16:1, n-7) resulting from GPCR activation, however, associated with different signaling pathways. We here report the synthesis of LPCs bearing two geometrical isomers of palmitoleic acids and investigation of their impact on human pancreatic ß cells viability, insulin secretion, and activation of four GPCRs previously demonstrated to be targeted by free fatty acids and LPCs. Moreover, molecular modeling was exploited to investigate the probable binding sites of tested ligands and calculate their affinity toward GPR40, GPR55, GPR119, and GPR120 receptors.

Novichok Nerve Agents as Inhibitors of Acetylcholinesterase-In Silico Study of Their Non-Covalent Binding Affinity.

In silico studies were performed to assess the binding affinity of selected organophosphorus compounds toward the acetylcholinesterase enzyme (AChE). Quantum mechanical calculations, molecular docking, and molecular dynamics (MD) with molecular mechanics Generalized-Born surface area (MM/GBSA) were applied to assess quantitatively differences between the binding energies of acetylcholine (ACh; the natural agonist of AChE) and neurotoxic, synthetic correlatives (so-called "Novichoks", and selected compounds from the G- and V-series). Several additional quantitative descriptors like root-mean-square fluctuation (RMSF) and the solvent accessible surface area (SASA) were briefly discussed to give-to the best of our knowledge-the first quantitative in silico description of AChE-Novichok non-covalent binding process and thus facilitate the search for an efficient and effective treatment for Novichok intoxication and in a broader sense-intoxication with other warfare nerve agents as well.

AlphaFold2 captures the conformational landscape of the HAMP signaling domain.

In this study, we present a conformational landscape of 5000 AlphaFold2 models of the Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases (HAMP) domain, a short helical bundle that transduces signals from sensors to effectors in two-component signaling proteins such as sensory histidine kinases and chemoreceptors. The landscape reveals the conformational variability of the HAMP domain, including rotations, shifts, displacements, and tilts of helices, many combinations of which have not been observed in experimental structures. HAMP domains belonging to a single family tend to occupy a defined region of the landscape, even when their sequence similarity is low, suggesting that individual HAMP families have evolved to operate in a specific conformational range. The functional importance of this structural conservation is illustrated by poly-HAMP arrays, in which HAMP domains from families with opposite conformational preferences alternate, consistent with the rotational model of signal transduction. The only poly-HAMP arrays that violate this rule are predicted to be of recent evolutionary origin and structurally unstable. Finally, we identify a family of HAMP domains that are likely to be dynamic due to the presence of a conserved pi-helical bulge. All code associated with this work, including a tool for rapid sequence-based prediction of the rotational state in HAMP domains, is deposited at https://github.com/labstructbioinf/HAMPpred.

Trans-palmitoleic acid, a dairy fat biomarker, stimulates insulin secretion and activates G protein-coupled receptors with a different mechanism from the cis isomer.

Dietary trans-palmitoleic acid (trans 16:1n-7, tPOA), a biomarker for high-fat dairy product intake, has been associated with a lower risk of type 2 diabetes mellitus (T2DM) in some cross-sectional and prospective epidemiological studies. Here, we investigated the insulin secretion-promoting activity of tPOA and compared them with the effects evoked by the cis-POA isomer (cPOA), an endogenous lipokine biosynthesized in the liver and adipose tissue, and found in some natural food sources. The debate about the positive and negative relationships of those two POA isomers with metabolic risk factors and the underlying mechanisms is still going on. Therefore, we examined the potency of both POA isomers to potentiate insulin secretion in murine and human pancreatic ß cell lines. We also investigated whether POA isomers activate G protein-coupled receptors proposed as potential targets for T2DM treatment. We show that tPOA and cPOA augment glucose-stimulated insulin secretion (GSIS) to a similar extent; however, their insulin secretagogue activity is associated with different signaling pathways. We also performed ligand docking and molecular dynamics simulations to predict the preferred orientation of POA isomers and the strength of association between those two fatty acids and GPR40, GPR55, GPR119, and GPR120 receptors. Overall, this study provides insight into the bioactivity of tPOA and cPOA toward selected GPCR functions, indicating them as targets responsible for the insulin secretagogue action of POA isomers. It reveals that both tPOA and cPOA may promote insulin secretion and subsequently regulate glucose homeostasis.

QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and in silico methods for automated in silico bio-activity discovery for any compound of interest.

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided below. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax.HIGHLIGHTSDeep learning based network pharmacology approach to predict the bio-activity of compounds.Further optimization of the compound toward drug like properties using deep learning techniques.Automated in silico modeling and interaction profiling of deep learning predicted target protein-ligand interaction.Communicated by Ramaswamy H. Sarma.

Escherichia coli tRNA 2-selenouridine synthase SelU selects its prenyl substrate to accomplish its enzymatic function.

Bacterial tRNA 2-selenouridine synthase (SelU) in vitro converts S2U-RNA to its selenium analog (Se2U-RNA) in a two-step process: (i) geranylation of S2U-RNA (with geranyl pyrophosphate, gePP), and (ii) selenation of the resulting geS2U-RNA (with the selenophosphate anion, SePO33-). Using an S2U-containing anticodon stem-loop fragment derived from tRNALys (S2U-RNA) and recombinant SelU with an MBP tag, we found that only geranyl (C10) pyrophosphate is the substrate for this enzyme, while other pyrophosphates such as isopentenyl (C5), dimethylallyl (C5), farnesyl (C15) and geranylgeranyl (C20) are not. Interestingly, methyl (C1)- and C5-, C10-, and C15-prenyl-containing S2U-RNAs (which were chemically obtained) underwent the selenation reaction promoted by SelU, although the Se2U-RNA product was obtained in decreasing yields in the following order: geranyl ≥ farnesyl > dimethylallyl ≫ methyl. Microscale thermophoresis showed an affinity between gePP and SelU in the micromolar range, while the other pyrophosphates tested, such as isopentenyl, dimethylallyl, farnesyl and geranylgeranyl, either did not bind to the protein or their binding affinity was above 1 mM. These results agree well with the in silico analysis, with gePP being the best binding substrate (the lowest relative free energy of binding (ΔG) and a small solvent-accessible surface area (SASA)). These results suggest that SelU has high substrate specificity for the prenylation reaction (only gePP is accepted), whereas there is little discrimination for the selenation reaction. We therefore suggest that only gePP and the geranylated tRNA serve as substrates for the conversion of 2-thio-tRNAs to 2-seleno-tRNAs, as it is found in the bacterial system.

A program to automate the discovery of drugs for West Nile and Dengue virus-programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated in silico modelling.

Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning-based AutoQSAR algorithm to generate drug leads for the flaviviruses-Dengue and West Nile. The drug leads generated by the program are fed as programmatic inputs to AutoDock Vina package for automated in silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning-based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated, each time the program is run, are reflective of the constantly growing PubChem database which is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue viruses. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus was modelled in an automated way through the tool. The results are stored in the working folder of the user. Thus, our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning-based AutoQSAR algorithm and an automated in silico modelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus. The program is hosted, maintained and supported at the GitHub repository link given belowCommunicated by Ramaswamy H. Sarma.

Target2DeNovoDrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest.

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrugCommunicated by Ramaswamy H. Sarma.

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins.

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved ßαß motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the ßαß motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the ßαß motif. A benchmark on two independent test sets, one containing ßαß motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.

Tissue-Nonspecific Alkaline Phosphatase (TNAP) as the Enzyme Involved in the Degradation of Nucleotide Analogues in the Ligand Docking and Molecular Dynamics Approaches.

Tissue-nonspecific alkaline phosphatase (TNAP) is known to be involved in the degradation of extracellular ATP via the hydrolysis of pyrophosphate (PPi). We investigated, using three different computational methods, namely molecular docking, thermodynamic integration (TI) and conventional molecular dynamics (MD), whether TNAP may also be involved in the utilization of ß,γ-modified ATP analogues. For that, we analyzed the interaction of bisphosphonates with this enzyme and evaluated the obtained structures using in silico studies. Complexes formed between pyrophosphate, hypophosphate, imidodiphosphate, methylenediphosphonic acid monothiopyrophosphate, alendronate, pamidronate and zoledronate with TNAP were generated and analyzed based on ligand docking, molecular dynamics and thermodynamic integration. The obtained results indicate that all selected ligands show high affinity toward this enzyme. The forming complexes are stabilized through hydrogen bonds, electrostatic interactions and van der Waals forces. Short- and middle-term molecular dynamics simulations yielded very similar affinity results and confirmed the stability of the protein and its complexes. The results suggest that certain effectors may have a significant impact on the enzyme, changing its properties.

In silico exploration of binding of selected bisphosphonate derivatives to placental alkaline phosphatase via docking and molecular dynamics.

Bisphosphonates constitute a group of pyrophosphate analogues therapeutically active against bone diseases. Numerous studies confirm their anticancer and antimetastatic potential as well as ability to relieve pathological pain. Although this is a known class of compounds, many aspects of their action remain unexplained and their new interaction partners are still being discovered. Due to the structural similarity to pyrophosphate, their interaction with pyrophosphate-recognizing enzymes seems to be feasible. In current work, the placental alkaline phosphatase (PLAP) is considered as a potential target for these class of compounds. PLAP is one of the enzymes responsible for degradation of pyrophosphate with high clinical significance. An elevation of PLAP level are considered as a potential cancer marker. An in silico study of complexes formed between selected phosphate derivatives and PLAP was performed. It indicates that all tested compounds: alendronic acid, clodronic acid, etidronic acid, zoledronic acid, imidodiphosphoric acid, pyrophosphoric acid, medronic acid, chloromethylenediphosphonic acid and hypophosphoric acid form a complexes with PLAP, stabilized by hydrogen bonds, hydrophobic and van der Waals interactions. Zoledronic acid, drug used in prevention of bone complications during cancer treatment was found to have the lowest estimated energy of binding (-6.6 kcal/mol). In silico study yielded very low energy of binding also for hypophosphate, equal -6.4 kcal/mol, despite having no identified hydrogen bonds. Subsequent molecular dynamic simulations, followed by molecular mechanics generalized-born surface area with pairwise decomposition calculations confirmed the stability of protein-ligand complexes. The results indicate that selected phosphate derivatives may potentially interact with the enzyme, changing its function, what should be investigated during in vitro studies.