Protein function prediction using neighbor relativity in protein–protein interaction network

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called “Neighbor Relativity Coefficient” (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.     

QSAR classification and regression models for β-secretase inhibitors using relative distance matrices

The development of robust QSAR models to predict the activity of molecules of β-secretase inhibitors is an area of interest due to the increase of Alzheimer’s disease in patients in the global population. In this paper, we present a proposal based on the use of relative distance matrices as input data to the QSAR algorithms. These matrices store measurements of distances between the structural characteristics of pairs of molecules and between the molecules and a structural pattern extracted from the whole data set, thus efficiently representing a correlation between structural changes and activity. For the building of the classification and regression models support vector machine, tree complex and Gaussian process algorithms have been used; and for the validation of the models cross-validation, bootstrapping and y-randomizing techniques have been applied. The results obtained are close to 100% in accuracy and area under receiver operating characteristic values in classification, and close to 1.0 for r ² and 0.1 for root mean square error in regression in training and in external validation, proving the ‘goodness’ of the proposal.     

Investigating Protein–Protein Allosteric Network using Current-Flow Scheme

Protein dynamic network analysis provides a powerful tool for investigating protein allosteric regulation. We recently developed a current-flow betweenness scheme for protein network analysis and demonstrated that this method, that is, using current-flow betweenness as edge weights, is more appropriate and more robust for investigating the signal transmission between two predefined protein residues or domains as compared with direct usage of correlation scores as edge weights. Here we seek to expand the current-flow scheme to study allosteric regulations involving protein–protein binding. Specifically, we investigated three gain-of-function mutations located at the binding interface of ALK2 (also known as ACVR1) kinase and its inhibitory protein FKBP12. We first searched for the optimal smoothing function for contact network construction and then calculated the subnetwork between FKBP12 protein and the kinase ATP binding site using current-flow betweenness. By comparing the networks between the wild-type and three mutants, we have identified statistically significant changes in the protein–protein networks that are common among all three mutants that allosterically shift the kinase toward a catalytically competent configuration. © 2019 Wiley Periodicals, Inc.     

Computational systematic selectivity of the Fasalog inhibitors between ROCK-I and ROCK-II kinase isoforms in Alzheimer’s disease

Human Rho-associated coiled-coil forming kinase (ROCK) is a class of essential neurokinases that consists of two structurally conserved isoforms ROCK-I and ROCK-II; they have been revealed to play distinct roles in the pathogenesis of Alzheimer’s disease (AD) and other neurological disorders. Selective targeting of the two kinase isoforms with small-molecule inhibitors is a great challenge due to the surprisingly high homology in kinase domain (92 %) and the full identity in kinase active site (100 %). Here, we describe a computational protocol to systematically profile the selectivity of Fasudil and its 25 analogs (termed as Fasalogs) between the two kinase isoforms. It is suggested that the substitution of Fasudil’s 1,4-diazepane moiety with rigid ring such as Ripasudil and Dimehtylfasudil would render the resulting inhibitors of ROCK-II over ROCK-I (II-o-I) selectivity, while the substitution with long, flexible group such as H-89 and BDBM92607 tends to have I-o-II selectivity. Structural analysis reveals that the inhibitor affinity is not only determined by the identical active site, but also contributed from the non-identical first and second shells of the site as well as other non-conserved kinase regions, which can indirectly influence the active site and inhibitor binding through allosteric effect. A further kinase assay basically confirms the computational findings, which also exhibits a good consistence with theoretical selectivity over 10 tested samples (R_p = 0.89). In particular, the Fasalog compounds Dimehtylfasudil and H-89 are identified as II-o-I and I-o-II selective inhibitors. They can be considered as promising lead molecular entities to develop new specific ROCK isoform-selective Fasalog inhibitors.     

Generation of cytidine 5′-triphosphate using adenylate kinase

A membrane-enclosed enzyme reactor containing adenylate kinase (E.C. 2.7.4.3, rabbit muscle) and pyruvate kinase (E.C. 2.7.1.40) converts cytidine 5′-monophosphate (CMP) and phosphoenolpyruvate (PEP) to cytidine 5′-triphosphate (CTP) and pyruvate on a gram scale.     

Naïve Bayes classification using 2D pharmacophore feature triplet vectors

A na?ve Bayes classifier, employed in conjunction with 2D pharmacophore feature triplet vectors describing the molecules, is presented and validated. Molecules are described using a vector where each element in the vector contains the number of times a particular triplet of atom-based features separated by a set of topological distances occurs. Using the feature triplet vectors it is possible to generate na?ve Bayes classifiers that predict whether molecules are likely to be active against a given target (or family of targets). Two retrospective validation experiments were performed using a range of actives from WOMBAT, the Prous Integrity database, and the Arena screening library. The performance of the classifiers was evaluated using enrichment curves, enrichment factors, and the BEDROC metric. The classifiers were found to give significant enrichments for the various test sets.     

Can MM‐PBSA calculations predict the specificities of protein kinase inhibitors?

An application of the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) protocol to the prediction of protein kinase inhibitor selectivity is presented. Six different inhibitors are placed in equivalent orientations in each of six different receptors. Fully solvated molecular dynamics is then run for 1 ns on each of the 36 complexes, and the resulting trajectories scored, using the implicit solvent model. The results show some correlation with experimentally-determined specificities; anomalies may be attributed to a variety of causes, including difficulties in quantifying induced fit penalties and variabilities in normal modes calculations. Decomposing interaction energies on a per-residue basis yields more useful insights into the natures of the binding modes and suggests that the real value of such calculations lies in understanding interactions rather than outright prediction.     

Novel molecular tumour classification using MALDI–mass spectrometry imaging of tissue micro-array

The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation–ion mobility separation–mass spectrometry (MALDI–IMS–MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI–IMS–MSI and principal component analysis–discriminant analysis (PCA–DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA–DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.     

Inhibition of Wnt/β-catenin signaling by p38 MAP kinase inhibitors is explained by cross-reactivity with casein kinase Iδ/ɛ

Wnt/β-catenin signaling plays essential roles in embryonic development, adult stem cell maintenance, and disease. Screening of a small molecule compound library with a β-galactosidase fragment complementation assay measuring β-catenin nuclear entry revealed TAK-715 and AMG-548 as inhibitors of Wnt-3a-stimulated β-catenin signaling. TAK-715 and AMG-548 are inhibitors of p38 mitogen-activated protein kinase, which has been suggested to regulate activation of Wnt/β-catenin signaling. However, two highly selective and equally potent p38 inhibitors, VX-745 and Scio-469, did not inhibit Wnt-3a-stimulated β-catenin signaling. Profiling of TAK-715 and AMG-548 against a panel of over 200 kinases revealed cross-reactivity with casein kinase Iδ and ?, which are known activators of Wnt/β-catenin signaling. Our data demonstrate that this cross-reactivity accounts for the inhibition of β-catenin signaling by TAK-715 and AMG-548 and argue against a role of p38 in Wnt/β-catenin signaling.     

Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.     

An automated method for the measurement of a range of tyrosine kinase inhibitors in human plasma or serum using turbulent flow liquid chromatography–tandem mass spectrometry

Tyrosine kinase inhibitors (TKIs) are used to treat a number of cancers, including chronic myeloid leukaemia and hepatocellular carcinoma. Therapeutic drug monitoring (TDM) may be indicated to (1) monitor adherence, (2) guide dosage, and (3) minimise the risk of drug-drug interactions and dose-related toxicity. On-line, automated sample preparation provided by TurboFlow technology (ThermoFisher Scientific) in conjunction with the sensitivity and selectivity of tandem mass spectrometry (MS/MS) detection may be applied to the analysis of single drugs and metabolites. We report the use of TurboFlow LC-MS/MS for the analysis of nine TKIs and metabolites (imatinib, N-desmethylimatinib, dasatinib, nilotinib, erlotinib, gefitinib, lapatinib, sorafenib, sunitinib) in human plasma or serum for TDM purposes. An Aria Transcend TLX-II system coupled with a TSQ Vantage was used. Samples (50 μL) were vortex mixed with internal standard solution (150 μL imatinib-D(8), gefitinib-D(8), sunitinib-D(10), and nilotinib-(13)C (2) (15) N(2) in acetonitrile) and, after centrifugation 100 μL supernatant were injected directly onto a 50 × 0.5-mm Cyclone TurboFlow column. Analytes were focussed onto a 50 × 2.1-mm (3 μm) Hypersil GOLD analytical column and eluted with an acetonitrile/water gradient. Analytes were monitored in selected reaction monitoring mode (positive APCI). Total analysis time was 7 min without multiplexing. Calibration was linear (R(2) > 0.99) for all analytes. Inter- and intra-assay precision (in percent relative standard deviation, RSD) was <11 % and accuracy 89-117 % for all analytes. No matrix effects were observed. This method is suitable for high-throughput TDM in patients undergoing chronic therapy with TKIs and has been utilised in the analysis of clinical samples.     

Design,synthesis and biological evaluation of O-linked indoles as VEGFR-2 kinase inhibitors(Ⅰ)

Inhibition of VEGFR-2 signaling pathway has already become one of the most promising approaches for the treatment of cancer.In this study,we describe the design,synthesis,and biological evaluation of a series of O-linked indoles as potent inhibitors of VEGFR-2.Among these compounds,18 showed significant anti-angiogenesis activities via VEGFR-2 in enzymatic proliferation assays,with IC_(50) value of3.8 nmol/L Kinase selectivity profiling revealed that 18 was a multitargeted inhibitor,and it also exhibited good potency against VEGFR-1,PDCFR-α and β.     

Design,synthesis and biological evaluation of biphenylurea derivatives as VEGFR-2 kinase inhibitors(Ⅱ)

Inhibition of VEGFR-2 signaling pathway is one of the most promising approaches for the treatment of cancer. In this paper, we reported the design, synthesis, and biological evaluation of a series of biphenylurea derivatives as VEGFR-2 inhibitors. Among these compounds, 39 exhibited potent inhibitory activity against VEGFR-2 both in vitro and in vivo. The antiangiogenesis activity of 39 was further confirmed by both tube formation assay and chick chorioallantoic membrane assay.     

Protein–ligand docking using FFT based sampling: D3R case study

Fast Fourier transform (FFT) based approaches have been successful in application to modeling of relatively rigid protein–protein complexes. Recently, we have been able to adapt the FFT methodology to treatment of flexible protein–peptide interactions. Here, we report our latest attempt to expand the capabilities of the FFT approach to treatment of flexible protein–ligand interactions in application to the D3R PL-2016-1 challenge. Based on the D3R assessment, our FFT approach in conjunction with Monte Carlo minimization off-grid refinement was among the top performing methods in the challenge. The potential advantage of our method is its ability to globally sample the protein–ligand interaction landscape, which will be explored in further applications.     

Identification of new dual spleen tyrosine kinase (Syk) and phosphoionositide-3-kinase δ (PI3Kδ) inhibitors using ligand and structure-based integrated ideal pharmacophore models

Owing to the complex pathophysiology of autoimmune disorders, it is very challenging to develop successful treatment strategies. Single-target agents are not desired therapeutics for such multi-factorial disorders. Considering the current need for the treatment of complex autoimmune disorders, dual inhibitors of Syk and PI3Kδ have been designed using ligand and structure-based molecular modelling strategies. In the present work, structure and ligand-based pharmacophore modelling was implemented for a varied set of Syk and PI3Kδ inhibitors. Ligand-based pharmacophore models (LBPMs) were developed for two kinases: ADPR.14 (r²_train = 0.809) for Syk, comprising one hydrogen bond acceptor, one hydrogen bond donor, one positive ionisable and one ring aromatic feature, and for PI3Kδ: AAARR.45 (r²_train = 0.942) consisting of three hydrogen bond acceptor and two ring aromatic features. The generated e-pharmacophore models revealed an additional ring aromatic and hydrophobic feature important for Syk and PI3Kδ inhibition, respectively. Subsequently, LBPMs were modified resulting in APDRR.14 hypothesis for Syk inhibitors and AAAHRR.45 hypothesis for PI3Kδ inhibitors employed for virtual screening. Thus, the combination of ligand and structure-based pharmacophore modelling helped in developing ideal pharmacophore models that may be an efficient tool for the designing of novel dual inhibitors of Syk and PI3Kδ.