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 β,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-β,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 β 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 β 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.

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 βαβ 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 βαβ 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 βαβ motif. A benchmark on two independent test sets, one containing βαβ 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.

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.

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.

[Hamartomas of the optic disc and adjacent retina]

The authors discuss some formations on the optic disk and close neighbourhood resembling tumours. They originate from various cellular elements in that area and are described as hamartomas. A progressive growth was recorded only in vascular hamartomas after unsuccessful therapeutic intervention, the remainder were stationary during observation periods of various lengths.

[Early stages of angiomatosis of the retina and optic nerve disk]

Based on 13 years observations of three generations of relatives with v. Hippel-Lindau's disease the authors focus attention on early stages of clinically detectable retinal angiomatosis. They supplement these findings by an angioma on the optic disc in one sporadic case. They consider the ophthalmological diagnosis of priority importance for the patients and risk relatives and recommend a concept of presymptomatic screening to detect other organ sites of the disease. Early detection and treatment of lesions on eyes and other organs improves the prognosis and reduces early mortality.