Identification of Potent Leads for Human cAMP Dependent Protein Kinase Catalytic Subunit Alpha: A Strategic Application of Virtual Screening for Cancer Therapeutics

The advancement in therapeutic applications focused on specific macromolecular compounds of deregulated cell signaling pathways bestowed novel approach to design the ligands as drug molecules against several life threatening diseases such as Cancer. In humans, protein kinase A is one of the important kinases those were involved in cell signaling mechanism. cAMP, G-proteins and ATP molecules were required for activation of protein kinase A (PKA), upon activation, PKA catalytic subunits (PRKACA,PRKACB and PRKACG) undergoes many cellular functions like cell proliferations, cell cycle regulation, and survival of cells through acting on many substrates. Overexpression of extracellular cAMP dependent protein kinase A catalytic subunits (PRKACA) causes severe tumorgenesis in different organs (prostate gland, breast, lungs and pancreas) leading to cancer. High throughput virtual screening was implemented herein to identify the potent leads for human PRKACA that stimulates chronic form of cancers. In silico functional and phylogenetic analysis of PRKACA protein provided enough evidences towards its cancer stimulating nature. The human PRKACA crystal structure in complex with inhibitor ‘796’ (PDB ID: 2GU8) was optimized in Maestro v9.0 and the amino acid residues constituting inhibitor interaction site were determined. Fifteen published inhibitors were selected including HA1077, Flavopiridol, Roscovitine, MLN-518, PP2 and Gleevec which were already in clinical trials for high throughput screening at Ligand.Info database. An in house library of 5388 compounds was designing from the above screening procedure were prepared in LigPrep for molecular docking with human PRKACA. Maestro Glide docking from lesser to higher stringency towards minor steric classes were applied subsequently to the prepared ligand dataset against a grid around centroid of the identified inhibitor interaction site of human PRKACA and 21 lead molecules with good docking scores were obtained. Lead ‘1’ (Leptosidin) with relatively least docking score (-11.02 Kcal/mol) compared to other 20 lead molecules and 15 published inhibitors delineates it as potentially the best competitive inhibitor among all. The promising inhibitory activity of Leptosidin is further supported from analysis of binding orientations of human PRKACA- Leptosidin complex deciphering the Lead 1 blocks the active site residues Thr51, Glu121, Val123, Glu127 and Thr183 by forming hydrogen bond. Thus, Leptosidin could be futuristic perspective chemical compound to design drug molecule against human PRKACA in numerous cancers, however, further in vitro and in vivo studies were required to verify the computational strategic prediction of PKA holoenzyme against cancer therapeutics

In Silico Analysis To Explore Novel Inhibitors For Human Proto-oncogene Tyrosine Protein Kinase Src

The first oncogene and the first non receptor tyrosine kinase, Src, plays a key role in cell morphology, motility, proliferation and survival. Over expression of Src kinase activity disrupts the RAS pathway in signaling pathway, where it loses its ability to hydrolyse GTP and thus, leads to cancer. A wide range of evidences indicated that Src-signaling was important in the oncogenesis of prostate cancer and other tumours. Src-signaling is involved in androgen-induced proliferation of prostate cancer in cancer tissue of patients having castration-refractory prostate cancer. Once prostate cancer becomes castration-resistant, bone metastases become significant problem for which treatment options are limited. As Src is involved in multiple signaling pathways, central to prostate cancer development, progression, and metastasis, in addition to normal and pathologic osteoclast activities, Src inhibition becomes a valid therapeutic strategy for investigation. Existing Src kinase inhibitors are less efficient towards prostate cancer and bone metastasis. Hence an in silico work was carried out to identify novel potent inhibitors. Three published inhibitors of human Src kinase those are currently under clinical trial such as AZDO530, bosutinib and dasatinib were subjected to high-throughput screening from more than million entries of Ligand.Info Meta-Database, based on the assumption that small molecules with similar structure have similar pharmacological properties. The ligand dataset of 1152 generated through this approach were prepared using LigPrep to generate possible conformations of each ligand molecule, and at the same time duplicate conformers, conformations with reactive functional group and ADME violaters were rejected. The human Src kinase co-crystal structure with AZDO530 was analyzed to find the inhibitor binding site. The crystal structure was optimized and energy was minimized applying OPLS force field in Maestro v9.0. Glide 5.5 docking was performed to predict the binding orientation of prepared ligand molecule into a grid of 20 x 20 x 20 Å created around the centroid of optimized human Src kinase. Ten lead molecules with good binding affinity with human Src were identified. In silico pharmacokinetics study for these ten lead molecules had shown no ADME violation. Analysis of Lead ‘1’ - human Src docking complex had revealed a XP Gscore of -11.56 kcal/mol with highly stabilized hydrogen bond network with Ala390, Asn391, Lys295, W543 and W640 and good Van der Waals interactions. The docking complex coincides well with the native co-crystallized human Sac and inhibitor AZDO530 complex. Thus, Fisetin, identified as Lead ‘1’ in the present study would be highly useful for developing potential drug molecules for treatment of advanced prostate cancer

Teacher Beliefs and Practices in Designing and Implementing Problem Based Learning in the Secondary Mathematics Classroom: A Case Study

Problem-based learning (PBL), an instructional approach anchored in the framework of the learning theory “constructivism,” is a shift toward student-centered learning; students build content knowledge and problem-solving skills by solving real world problems (Hmelo-Silver, 2004; Savery, 2006; Stepien & Gallagher, 1993). Most of the existing literature on PBL comes from the higher education setting. However, researchers (Hmelo-Silver, 2004; Maxwell, Mergendoller, & Bellisimo, 2005; Ravitz, 2009; Savery, 2006; Strobel & van Barneveld, 2009) have stressed the need for more PBL research that examines its effectiveness in the K through 12 classrooms. The purpose of this dissertation study was to examine the factors affecting teachers’ design and implementation of PBL in the high school mathematics classroom after a prolonged engagement in a professional learning on PBL implementation. The research question for the study was: After prolonged engagement in professional learning on problem-based learning (PBL), what factors influence teachers’ beliefs in designing and implementing PBL as an instructional approach in the high school mathematics classroom? The research question was answered by examining the changes in teachers’ beliefs about mathematics teaching and learning, changes in their instructional practices and by analyzing their challenges while implementing PBL in the classroom. This dissertation uses the characteristics of PBL (Savery & Duffy, 2001) as a framework, and Green’s (1971) analysis of beliefs as a lens, to answer the research question. A qualitative case study approach was selected in the current study and teacher beliefs, instructional practices were analyzed (Merriam, 1988 and Yin, 1989). Teachers from a high school located in an urban area in the southeastern region of the United States participated in a prolonged professional development on PBL for a total of 60 hours each year by being a part of the project called “Collaboration for Mathematics and Science Achievement” during 2011, and 2012. Three teachers that attended both professional development sessions were purposefully selected for this case study. I collected data through surveys, interviews, and observations and also utilized field notes and teachers’ journal entries. Results of this dissertation could be beneficial to classroom teachers and could influence curriculum writers to support classroom teachers in implementing PBL in the mathematics classroom. The findings from this qualitative research study revealed four major themes: 1) Teacher collaboration is essential in influencing teacher beliefs in the design and implementation of PBL, 2) Pressure from the school district to increase students test scores in standardized tests prioritized learning of mathematics in the classroom, 3) Changes in terms of teaching assignment, and changes in the current curriculum discouraged teachers from embracing any innovative reform-based instructional practices like PBL in the classroom and 4) Low expectations in students’ ability and performance dissuaded teachers from implementing reform-based instructional practices like PBL in the classroom. A major implication from the current study is that in general, teachers should hold the core belief that “All students can learn.” This statement implies that all students can learn according to the standards set forth by challenging curriculum. Mathematics teachers will have to create opportunities for students to explore inquiry-based lessons and cognitively demanding tasks such as PBL cases

Computerized Protein Modeling and Molecular Docking Analysis of Human Proto Oncogene Tyrosine Protein Kinase YES for Discovery of Novel Lead Molecules

Human proto-oncogene tyrosine-protein kinase YES (YES) is a non receptor kinase belongs to Src family. This gene lies in close proximity to thymidylate synthase gene on chromosome 18, and a corresponding pseudogene has been found on chromosome 22. In hepatocellular carcinoma and colorectal carcinoma elevated human YES activity was observed. Inhibitors of human YES reported till date are in clinical trials and associated with several side effects. The present study was mainly aimed in homology modeling of human YES and discovery of novel lead molecules that inhibit YES kinase more efficiently with fewer side effects. Virtual screening and docking techniques were applied to identify novel lead molecule of YES kinase. As there was no reported human YES crystal structural data, the three dimensional structure of human YES was constructed based on template structure (PDB ID: 2H8H) obtained through homology search using MODELLER 9V7. The model was refined, energy minimized and assessed through PROCHECK. Active site residues of human YES were identified from the homology model in complex with template ligand AZD0530 and were further confirmed using CASTp. Five published inhibitors of YES family (Dasatinib, Bosutinib, SU6656, AZD0530 and CGP77675) were identified through literature search. High throughput virtual screening method at Ligand.Info was applied for these five inhibitors to establish a library of 1932 structural analogs. LigPrep was used to generate possible conformations of each ligand molecules from structural analog library. The ligand duplicates conformers, ligands having reactive functional group and poor ADME properties were rejected from the prepared dataset. Glide 5.5 was used to generate a grid box by picking the active site residues of human YES protein. Through sequential applications of stringent mode glide docking procedures from Glide HTVS to SP to XP respectively, 13 potential inhibitors were proposed. The docking complexes of each inhibitor with human YES protein were analyzed and lead ‘1’ molecule was identified to have higher binding affinity to human YES protein (XP Gscore: -12.07 Kcal/mol) compared to existing published inhibitors and other 12 lead molecules. The lead ‘1’ - human YES docking complex was highly stabilized through hydrogen bond network with amino acid residues Thr348, Asp358, Asp414 and Phe415. Moreover, from the results obtained we could decipher that lead ‘1’ molecule can be raised into potential inhibitors after binding assays, substantiated experimental investigations and passing several phases of clinical trials

In silico design of potent agonists for human PPAR γ

Peroxisome proliferator-activated receptor (PPAR γ) acts as a key regulator on adipocyte differentiation and glucose homeostasis. PPAR γ has been implicated in the pathology of type 2 diabetes. As human PPAR γ activity is considered important in improving insulin sensitivity, in silico screening was carried out to find potent agonists for human PPAR γ protein. The co-crystal structure of PPAR γ, solved through X-Ray diffraction method was retrieved from the protein data bank. Four PPAR γ agonists selected from literature were submitted to subsequent 2D searching protocol using Ligand.Info, which yielded 1699 structural analogs. The PPAR γ co-crystal structure and ligand dataset were preprocessed using protein preparation wizard and LigPrep, respectively. Further, docking was performed by using three phased docking protocol of Maestro v9.2 that implements Glide v5.7. The obtained thirteen leads through docking were compared with the existing inhibitors and seven leads with good binding affinity with PPAR γ were proposed. The binding orientations of the seven leads were coinciding well with the native co-crystal structure of human PPAR γ. Thus, the proposed seven leads can be suggested as potential agonists for improving insulin sensitivity in the treatment of type 2 diabetes mellitus if synthesized and validated in animal model

Stochastic Modelling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak in India

Coronavirus disease 2019 (COVID-19) emerged in Wuhan city, China, at the end of December 2019. As of July 26, 2020, 16258353 COVID-19 cases were confirmed worldwide, including  649848 deaths. The spread of COVID-19 is currently very high. Under the classical SIR (Susceptible-Infected-Recovered) model, epidemiological data for India up to 26th July 2020 were used to forecast the COVID-19 outbreak. For controlling the spreading of the virus, we have to prepare for precaution and futuristic calculation for infection spreading. We used the data from the COVID-2019 Outbreak of India on July 26th, 2020 in this report. In these results, for the initial level of experimental intent, we used 16291331 susceptible cases, 481248 infectious cases, and 910298 rewards / removed cases. Through the aid of the SIR model, data on a wide range of infectious diseases have been analyzed.  SIR model is one of the most effective models which can predict the spreading rate of the virus. We have validated the model with the current spreading rate with this SIR model. The findings of the SIR model can be used to forecast transmission and avoid the outbreak of COVID-2019 in India. The results of the study will shed light on understanding the outbreak patterns and indicate those regions' epidemiological points. Finally, from this study, we have found that the outbreak of the COVID-2019 epidemic in India will be at its peak on 09 August 2020 and after that, it will work slowly and on the verge of ending in the second or third week of November 2020

In silico identification of potential inhibitors for human aurora kinase b

Cell cycle progression through mitosis and meiosis involves regulation by serine/threonine kinases from the aurora family. Aurora kinase b (Aurkb) is mainly involved in the proper segregation of chromosomes during mitosis as well as meiosis. However, over expression of Aurkb leads to the unequal distribution of genetic information creating aneuploid cells, a hallmark of cancer. Thus, Aurkb can be used as an effective molecular target for computer-aided drug discovery against cancer. Existing Aurkb inhibitors are less efficient, hence an in silico work was carried out to identify novel potent inhibitors. Three published inhibitors azd1152, zm447439 and N-(4-{[6-methoxy-7-(3-morpholin-4-ylpropoxy) quinazolin- 4-yl] amino} phenyl) benzamide were subjected to high throughput virtual screening of over 1 million entries from a ligand info meta database, to generate a 1161 compound library. The crystal structure was optimized and energy was minimized applying an OPLS force field in Maestro v9.0. Molecular docking using Glide was performed to predict the binding orientation of the prepared ligand molecule into a grid of 20*20*20 Å created around the centroid of the optimized human Aurkb protein. Nine lead molecules with good binding affinity with human Aurkb were identified. In silico pharmacokinetics study for these nine lead molecules has shown no ADME violation. Analysis of lead ‘1’- human Aurkb docking complex has revealed a XP Gscore of -10.20 kcal/mol with a highly stabilized hydrogen bond network with Asp218 and Ala157 and good Van der wall interactions. The docking complex coincides well with the native co- crystallized human Aurkb and inhibitor zm447439 complex. Thus, lead 1 would be highly useful for developing potential drug molecules for the treatment of cancer

Impregnable Defence Architecture using Dynamic Correlation-based Graded Intrusion Detection System for Cloud

Data security and privacy are perennial concerns related to cloud migration, whether it is about applications, business or customers. In this paper, novel security architecture for the cloud environment designed with intrusion detection and prevention system (IDPS) components as a graded multi-tier defense framework. It is a defensive formation of collaborative IDPS components with dynamically revolving alert data placed in multiple tiers of virtual local area networks (VLANs). The model has two significant contributions for impregnable protection, one is to reduce alert generation delay by dynamic correlation and the second is to support the supervised learning of malware detection through system call analysis. The defence formation facilitates malware detection with linear support vector machine- stochastic gradient descent (SVM-SGD) statistical algorithm. It requires little computational effort to counter the distributed, co-ordinated attacks efficiently. The framework design, then, takes distributed port scan attack as an example for assessing the efficiency in terms of reduction in alert generation delay, the number of false positives and learning time through comparison with existing techniques is discussed

Exploration of Deep Learning Models for Video Based Multiple Human Activity Recognition

Human Activity Recognition (HAR) with Deep Learning is a challenging and a highly demanding classification task. Complexity of the activity detection and the number of subjects are the main issues. Data mining approaches improved decision-making performance. This work presents one such model for Human activity recognition for multiple subjects carrying out multiple activities. Involving real time datasets, the work developed a rapid algorithm for minimizing the problems of neural networks classifier. An optimal feature extraction happens and develops a multi-modal classification technique and predicts solutions with better accuracy when compared to other traditional methods. This paper discussing on HAR prediction in four phases namely (i) Depthwise Separable Convolution with BiLSTM (DSC-BLSTM); (ii) Enhanced Bidirectional Grated Recurrent Unit with Long Short Term Memory (BGRU-LSTM); (iii) Enhanced TimeSformer Model with Multi-Layer Perceptron Neural Networks classification and (iv) Filtering Single Activity Recognition are described.In comparison to previous efforts like the DSC-BLSTM and BGRU-LSTM classifications, the experimental result of the ETMLP classification attained 98.90%, which was more efficient. The end outcome revealed that the new model performed better in terms of accuracy than the other models