In the world of technology, the Internet of Things (IoT) is a network to link entire things, that is, people, devices, and systems, with each other through an approach of common networking. This technology constructed a way, where many of the routine devices or things are interrelated and easily communicated with, their surroundings to gather or transfer the information over the network without the need of any human-to-system communication or human-to-human communication. It is born with features such as dynamics, scalability, and heterogeneity, and only that network solution can adapt to it which has strategy to incorporate its features. And here comes data centric interaction paradigm, it applies an approach of data naming to comprise the dynamics, scalability, and heterogeneity features to adapt to IoT and composes NDN of things, that is, Named Data Networking of Things (NDNoT). This paradigm also familiarizes the readers with the various kinds of security irruptions that occur in the network, and blockchain acts as a solution for those security attacks in NDNoT. The technology of blockchain is born with a cryptocurrency known as Bitcoin. It is an undoubtedly amazing and innovative invention in the world of technology. The cryptographic algorithms used by blockchains would make consumer data more private. So the technology of blockchain could perhaps be the silver bullet required by the industry of Internet of Things.

In ancient times, humans were much predisposed to utilizing their handcreated tools to finish any kind of work. From the last few years, businesses related to production have been expanding. People began to rely on tools, machines, and smart gadgets to process their work, as these kinds of tools help them to achieve their target in the given time. . Since the 18th century, the world has experienced a lot of revolutions in industry across the globe. This type of growth in technology has made a base for industrial revolutions. New technology was introduced called “blockchain.”Probably, blockchain technology is the most disruptive technology in the modern digital economy. The ability of blockchain technology has been greatly described in many research works, media, and majorly in the fields of finance and payment. One existing research is at the organizational stage, where it implements the architecture for internet safety as well as inconvertibility. “Industry 4.0” and “IIoT (Industrial Internet of Things)” are involved in its rising applications. Thus, in this paper, current applications of blockchain in IIoT as well as Industry 4.0 setups, existing open issues, modern application sectors, challenges related to IIoT, and their solutions are illustrated. The main focus of this paper is to empower and facilitate research in this field, which will help the developers in blockchain acquisition as well as investment in the “Industry 4.0” and “Industrial Internet of Things” space.

With the frequent growth in smartphones and media tablets, the industry of computer systems captured leadership from the industry of telecommunication in carry forwarding technological evolution. However, the principal component impacting the growth of the future will be the extent to which spectrum policy as well as management can rise spectrum capacity and give the necessary radio spectrum frequency efficiency. This entry describes why this is the bounding component for 5G mobile communication evolution so, around the world the research organizations begun to look beyond fifth-generation network, and it is expected from 6G to develop into green networks which supplies high capacity of energy and quality of service (QoS). With the goal of fulfill the requirements of forthcoming applications, considerable changes require to be made in structure of mobile communication networks. This paper gives a survey in detail on wireless development toward green 6G networks with an aim to show a pathway for further research works in the field of green 6G networks.

In the network of IoT, a huge amount of data is frequently generated, major messages through complicated networks serving device-to-device communications are swapped and also, sensitive smart world frameworks are controlled and monitored by thousands of gadgets and sensors. To extenuate the acceleration of overcrowding of resources in the network, as an approach edge computing, has risen as a modern approach to resolve requirements of confined computing as well as IoT. In this paper, a brief introduction of Internet of Things and edge computing is discussed which consists of general concepts of IoT and its components, basic introduction of edge computing, and structure of edge computing. After that, fundamental concepts of cloud computing, edge computing and IoT are introduced by comparing their features with each other and a structure of IoT based on edge computing is also illustrated with a slight introduction of the architecture of both the edge computing and IoT. Moreover, the advantages of using the technology of edge computing to assist the technology of IoT are provided as well as the efficiency of integrating these two technologies together is demonstrated. Then, the issues like security and privacy, advanced communication etc. related to combination of IoT and edge computing system are discussed and finally, the conclusion of the paper is presented. So, basically, in this paper, an extensive survey is conducted to analyze how the usage of edge computing progresses the performance of systems of IoT. The performance of edge computing is studied by comparing delay of network, occupation of bandwidth, power utilization, and many other characteristics.

In current years, Internet of Things has come a long way and is well emerged within many organizations and fields covering the sector of healthcare. The continuous execution of IoT within the field of healthcare will direct to a fast rise in productivity and examination of data. In reference to medical gadgets, developments in technology will enhance the results of patients with superior analytics. Thus, this chapter introduces Internet of Health Things with wearable healthcare systems in detail and shows the inter-association of interaction allowed medical gadgets and their combination to broader scale networks of healthcare to enhance the health of patients, and because of this sensitive behavior of systems related to health. Still, It meets various issues, specifically in regards of security, privacy and scalability. This work also presents an overview of approaches based on IoT for healthcare and healthcare aided living as well. Moreover, this chapter illustrates IoT networks for healthcare and the different characteristics of IoT confidentiality and safety including security needs. Also, how distinct technologies such as augmented reality, big data, cloud computing and many more can be implemented in the reference of healthcare are described and ultimately, some pathways for forthcoming work on healthcare based on IoT based on a collection of challenges and open issues are presented.

In every direction, there is a lot of noise about the Internet of Things (IoT) and its impact on everything. The technology of IoT is a huge network of interrelated devices and human beings that record and transmit the data to each other about the way they are used and about their surroundings. Conventional networks of IoT rely on a concentrated structure with finite scalability among other negative aspects. Hence, blockchain can deal with the IoT by providing many benefits and security to the data of the network. Globally, with the growth of technologies, companies and organizations are relying upon their data systems. Issues about missing or robbing of data are becoming a constant in news headlines because organizations depend more and more upon their computer systems to collect confidential information of customers. Therefore, in this chapter, the detailed background of IoT is introduced. Then, the problems of IoT are illustrated where blockchain can act as a rescue for security issues of IoT. Furthermore, the blockchain is described in detail with an introduction to its architecture, major features, approaches of data secrecy, and mining process. Moreover, an idea of IoT based on blockchain with applications, security, and confidentiality is described, and finally, disputes of blockchain are illustrated. The main motive of this chapter is to focus on the open research issues and directions of possible upcoming research on blockchain for IoT, as well as on the services of security and confidentiality for data using blockchain.

The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis . Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.

HER-2 belongs to the human epidermal growth factor receptor (HER) family. Via different signal transduction pathways, HER-2 regulates normal cell proliferation, survival, and differentiation. Recently, it was reported that MCF10A, BT474, and MDA-MB-231 cells bearing the HER2 K753E mutation were resistant to lapatinib. Present study revealed that HER-2 mutant K753E showed some contrasting behaviour as compared to wild, L768S and V773L HER-2 in complex with lapatinib while similar to previously known lapatinib resistant L755S HER-2 mutant. Lapatinib showed stable but reverse orientation in binding site of K753E and the highest binding energy among studied HER2-lapatinib complexes but slightly lesser than L755S mutant. Results indicate that K753E has similar profile as L755S mutant for lapatinib. The interacting residues were also found different from other three studied forms as revealed by free energy decomposition and ligplot analysis.

Fms‐like tyrosine kinase 3 (FLT3) belongs to the receptor tyrosine kinase family and expressed in hematopoietic progenitor cells. FLT3 gene mutations are reported in ~30% of acute myeloid leukemia cases. FLT3 kinase domain mutation F691L is one of the common causes of acquired resistance to the FLT3 inhibitors including quizartinib. MZH29 and crenolanib were previously reported to inhibit FLT3 F691L. However, crenolanib was reported for the moderate inhibition. We found that Glu661and Asp829 were the most significant residues to target the FLT3 F691L which contribute most significantly to the binding energy with MZH29 and crenolanib. These interactions were found absent with quizartinib. Further free energy landscape analysis revealed that FLT3 F691L bound to MZH29 and crenolanib was more stable as compared to quizartinib.

Drug resistance to anaplastic lymphoma kinase (ALK) inhibitors (crizotinib and ceritinib) is caused by mutation in the region encoding kinase domain of ALK. Compounds with potential ability to inhibit all strains of ALK are a solution to tackle the problem of drug resistance. In this study, we delineated positions of residues possessing the ability to make ALK drug resistant upon mutation by assessing them using five parameters (conservation index, binding-site root-mean-square deviation, protein structure stability, change in ATP, and drug-binding affinity). Four residual positions (Leu 1122, Thr 1151, Phe 1245, and Gly 1269) were ascertained. This study will be beneficial for designing drugs with better proficiency against ALK and the issues of drug resistance. This study can be taken as a pipeline for investigating drug-resistant mutations in other diseases as well.

Adverse drug reactions (ADRs) have become one of the primary reasons for the failure of drugs and a leading cause of deaths. Owing to the severe effects of ADRs, there is an urgent need for the generation of effective models which can accurately predict ADRs during early stages of drug development based on integration of various features of drugs. In the current study, we have focused on neurological ADRs and have used various properties of drugs that include biological properties (targets, transporters and enzymes), chemical properties (substructure fingerprints), phenotypic properties (side effects (SE) and therapeutic indications) and a combinations of the two and three levels of features. We employed relief-based feature selection technique to identify relevant properties and used machine learning approach to generated learned model systems which would predict neurological ADRs prior to preclinical testing. Additionally, in order to explain the efficiency and applicability of the models, we tested them to predict the ADRs for already existing anti-Alzheimer drugs and uncharacterized drugs, respectively in side effect resource (SIDER) database. The generated models were highly accurate and our results showed that the models based on chemical (accuracy 93.20%), phenotypic (accuracy 92.41%) and combination of three properties (accuracy 94.18%) were highly accurate while the models based on biological properties (accuracy 82.11%) were highly informative.

Chymase enzyme abundantly found in secretory granules of mast cells and catalyzes the hydrolysis of peptide bonds to generate angiotensin II via hydrolysis of angiotensin I and also activates transforming growth factor-b and MMP-9. MMP-9 and TGF-b have significant role in tissue inflammation and fibrosis. In present study, we investigated that Lys192Met mutation leads to a higher loss in binding energy of inhibitors than mutation Arg143Gln in chymase. The energy decomposition revealed that the contributing residues are almost same in all the forms with some change in energy value. All the results pointing that arginine and lysine residues of chymase play the most significant role in inhibitor binding revealed by energy decomposition. The Lys40, Arg90, Lys192 and Arg217 are found to be most prominent residues in two different inhibitor systems but the role of other lysine and arginine also important as they also have significant contribution in the total binding energy.

Mutations in the kinase domain encoding region of EGFR gene causes drug resistance to EGFR kinase inhibitors such as erlotinib and gefitinib. This problem can be addressed by a new lead compound effective against all mutants of EGFR. To predict positions of residues possessing the potential to render EGFR drug resistant upon mutation, residual positions known to interact with Erlotinib and Gefitinib were assessed using five parameters (conservation index, binding site RMSD, protein structure stability and change in ATP and drug binding affinity). Structural screening protocol was followed to identify novel lead compound. Four positions, Lys 745, Cys 797, Asp 800 and Thr 854, were most likely observed to acquire drug resistance by altering drug binding affinity without destabilizing the protein and ATP binding ability. A compound DHO was observed to possess better binding affinity for all EGFR models in comparison to Erlotinib and Gefitinib, using docking protocol. This information would pave the way for designing drugs effective against wild-type (WT) EGFR as well as against variant EGFRs models. Thus, authors report a lead compound as a long-term potential with the ability to inhibit predicted models of mutant, wild and known SNPs EGFR.

Mutations induce conformational changes in folliculin C-terminal domain: possible cause of loss of guanine exchange factor activity and Birt-Hogg-Dubé syndrome S. Verma, C. Tyagi, S. Goyal, B. Pandey, S. Jamal, A. Singh and A. Grover* School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India; Agricultural Knowledge Management Unit, Indian Agricultural Research Institute, New Delhi 110032, India; Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan 304022, India; Department of Biotechnology, Panjab University, Chandigarh 160014, India; Department of Biotechnology, TERI University, VasantKunj, New Delhi 110 070, India

ABSTRACTAlzheimer's is a neurodegenerative disease affecting large populations worldwide characterized mainly by progressive loss of memory along with various other symptoms. The foremost cause of the disease is still unclear, however various mechanisms have been proposed to cause the disease that include amyloid hypothesis, tau hypothesis, and cholinergic hypothesis in addition to genetic factors. Various genes have been known to be involved which are APOE, PSEN1, PSEN2, and APP among others. In the present study, we have used computational methods to examine the pathogenic effects of non‐synonymous single nucleotide polymorphisms (SNPs) associated with ABCA7, CR1, MS4A6A, CD2AP, PSEN1, PSEN2, and APP genes. The SNPs were obtained from dbSNP database followed by identification of deleterious SNPs and prediction of their functional impact. Prediction of disease‐associated mutations was performed and the impact of the mutations on the stability of the protein was carried out. To study the structural significance of the computationally prioritized mutations on the proteins, molecular dynamics simulation studies were carried out. On analysis, the SNPs with IDs rs76282929 ABCA7; CR1 rs55962594; MS4A6A rs601172; CD2AP rs61747098; PSEN1 rs63750231, rs63750265, rs63750526, rs63750577, rs63750687, rs63750815, rs63750900, rs63751037, rs63751163, rs63751399; PSEN2 rs63749851; and APP rs63749964, rs63750066, rs63750734, and rs63751039 were predicted to be deleterious and disease‐associated having significant structural impact on the proteins. The current study proposes a precise computational methodology for the identification of disease‐associated SNPs. J. Cell. Biochem. 118: 1471–1479, 2017. © 2016 Wiley Periodicals, Inc.

Streptomycin was the first antibiotic used for the treatment of tuberculosis by inhibiting translational proof reading. Point mutation in gidB gene encoding S-adenosyl methionine (SAM)-dependent 7-methylguanosine (m7G) methyltransferase required for methylation of 16S rRNA confers streptomycin resistance. As there was no structural substantiation experimentally, gidB protein model was built by threading algorithm. In this work, molecular dynamics (MD) simulations coupled with binding free energy calculations were performed to outline the mechanism underlying high-level streptomycin resistance associated with three novel missense mutants including S70R, T146M, and R187M. Results from dynamics analyses suggested that the structure distortion in the binding pocket of gidB mutants modulate SAM binding affinity. At the structural level, these conformational changes bring substantial decrease in the number of residues involved in hydrogen bonding and dramatically reduce thermodynamic stability of mutant gidB-SAM complexes. The outcome of comparative analysis of the MD simulation trajectories revealed lower conformational stability associated with higher flexibility in mutants relative to the wild-type, turns to be major factor driving the emergence of drug resistance toward antibiotic. This study will pave way toward design and development of resistant defiant gidB inhibitors as potent anti-TB agents.

BACKGROUNDAlzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides.METHODSThe present study is aimed to generate accurate classification models using machine learning techniques, which could identify IDE modulators from a bioassay dataset consisting of IDE inhibitors as well as non-inhibitors. The identified compounds were subjected to docking and Molecular dynamics (MD) studies for an in-depth analysis of the binding modes along with the complex stability. This study proposes that the identified potential active compounds, STK026154 (PubChem ID: CID2927418) with Glide score of -7.70 kcal/mol and BAS05901102 (PubChem ID: CID3152845) with Glide score of -7.06 kcal/mol, could serve as promising leads for the development of novel drugs against AD.CONCLUSIONThe present study shows that such in silico approaches can be effectively used to discover and select active compounds from unseen data for accelerated drug development process. The machine learning models generated in the present study were used to screen Traditional Chinese Medicine (TCM) database to identify the phytocompounds already been reported to have therapeutic effects against AD.