281 research outputs found
Altered dynamics upon oligomerization corresponds to key functional sites
No full textIt is known that over half of the proteins encoded by most organisms function as oligomeric complexes. Oligomerization confers structural stability and dynamics changes in proteins. We investigate the effects of oligomerization on protein dynamics and its functional significance for a set of 145 multimeric proteins. Using coarse-grained elastic network models, we inspect the changes in residue fluctuations upon oligomerization and then compare with residue conservation scores to identify the functional significance of these changes. Our study reveals conservation of about ½ of the fluctuations, with ¼ of the residues increasing in their mobilities and ¼ having reduced fluctuations. The residues with dampened fluctuations are evolutionarily more conserved and can serve as orthosteric binding sites, indicating their importance. We also use triosephosphate isomerase as a test case to understand why certain enzymes function only in their oligomeric forms despite the monomer including all required catalytic residues. To this end, we compare the residue communities (groups of residues which are highly correlated in their fluctuations) in the monomeric and dimeric forms of the enzyme. We observe significant changes to the dynamical community architecture of the catalytic core of this enzyme. This relates to its functional mechanism and is seen only in the oligomeric form of the protein, answering why proteins are oligomeric structures.This is the peer reviewed version of the following article: Mishra, Sambit Kumar, Kannan Sankar, and Robert L. Jernigan. "Altered dynamics upon oligomerization corresponds to key functional sites." Proteins: Structure, Function, and Bioinformatics 85, no. 8 (2017): 1422-1434, which has been published in final form at doi:10.1002/prot.25302. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.</p
Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites
No full textBinding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, AR-Pred (Active and Regulatory site Prediction), which supplements protein geometry, evolutionary and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. Since the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median AUC of 91% and MCC of 0.68, whereas the less welldefined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/ARPRED_ source.This is the peer reviewed version of the following article: Mishra, Sambit Kumar, Gaurav Kandoi, and Robert L. Jernigan. "Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites." Proteins: Structure, Function, and Bioinformatics (2019), which has been published in final form at doi: 10.1002/prot.25749. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.</p
Article and Author Level Measurements
No full textArticle and author level measurements have been discussed in this Unit. Author and researcher identifiers are absolutely essential for searching databases in the WWW because a name like D Singh can harbour a number of names such as Dan Singh, Dhan Singh, Dhyan Singh, Darbara Singh, Daulat Singh, Durlabh Singh and more. The ResearcherID.com, launched by Thomson Reuters, is a web-based global registry of authors and researchers that individualises each and every name. Open Researcher and Contributor ID (ORCID) is also a registry that uniquely identifies an author or researcher. Both have been discussed in this Unit. Article Level Metrics (Altmetrics) has been treated in this Unit with the discussion as to how altmetrics can be measured with Altmetric.com and ImpactStory.org. Altmetrics for Online Journals has also been touched. There are a number of academic social networks of which ResearchGate.net, Academia.edu, GetCited.org, etc. have been discussed. Regional journal networks with bibliometric indicators are also in existence. Two networks of this type such as SciELO – Scientific Electronic Library Online, and Redalyc have been dealt with. This Unit discusses in details aspects such as Unique Identifiers for Authors and Researchers; Article Level Metrics (Altmetrics); Academic Social Networks; and Regional Journal Networks with Bibliometric Indicators
PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model
No full textUnderstanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids, leading to increased or decreased stability of the encoded proteins. In this study, we describe a novel approach—Protein Stability Prediction with a Gaussian Network Model (PSP-GNM)—to measure the unfolding Gibbs free energy change (ΔΔG) and evaluate the effects of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa–Jernigan statistical potential. We used PSP-GNM to simulate partial unfolding of the wildtype and mutant protein structures, and then used the difference in the energies and entropies of the unfolded wildtype and mutant proteins to calculate ΔΔG. The extent of the agreement between the ΔΔG calculated by PSP-GNM and the experimental ΔΔG was evaluated on three benchmark datasets: 350 forward mutations (S350 dataset), 669 forward and reverse mutations (S669 dataset) and 611 forward and reverse mutations (S611 dataset). We observed a Pearson correlation coefficient as high as 0.61, which is comparable to many of the existing state-of-the-art methods. The agreement with experimental ΔΔG further increased when we considered only those measurements made close to 25 °C and neutral pH, suggesting dependence on experimental conditions. We also assessed for the antisymmetry (ΔΔGreverse = −ΔΔGforward) between the forward and reverse mutations on the Ssym+ dataset, which has 352 forward and reverse mutations. While most available methods do not display significant antisymmetry, PSP-GNM demonstrated near-perfect antisymmetry, with a Pearson correlation of −0.97. PSP-GNM is written in Python and can be downloaded as a stand-alone code
Adaptable Techniques for Making IT-Related Investment Decisions
No full textThe author investigated the main methods used for making IT-related investment decisions. In the view of these methods it can be said that no method was found that would help to make investment decisions 'routinelike'. The use of traditional financial indicators – which would be suitable for so-called regular investments - has a lot of obstructions and it would be dangerous to base decisions only on them. Return on Investment (ROI) methods are used in the case of classical investments (buildings, machineries) to analyze capital investments. Their simplest explanation is that net profit is expressed in percentage of invested capital. If it is a planned investment, a quotient is used for comparing variations. Cost-Benefit Analysis (CBA) has gained upon from the 60’s (in case of several IT-related investments as well), as it made possible to consider certain not quantifiable factors and uncertain elements too, so it meant a great help for choosing between different alternatives. Total Cost of Ownership (TCO)-model was created by GartnerGroup in the beginning of the 90’s, which is an excellent method for monitoring IT infrastructure and for analysing direct and indirect costs of possessed and used softwares and hardwares. An another method for making investment decisions, Rapid Economic Justification (REJ) is an attempt made by Microsoft and Intellectual Arbitrage to develop a better-balanced approach for examining and developing IT projects as it had been before. REJ offers the possibility of assessment balance against the cost-models dealing with only the cost side of a project
Methodological Issues in Management Research
No full textIn order to produce information that will be dependably useful to managers, research must be carefully planned, carried out, and interpreted, Good research does not just happen. It is the result of deliberate application of well-tested methods. The aim of this paper is to outline some of those methods and why they are important
Protein sequence-structure-dynamics-function relationships: The close association of dynamics with protein function
No full textThe intrinsic dynamics of globular proteins is the key to the understanding of their function, being a consequence of protein structure and geometry. The view of protein structures has recently changed from native structures being considered to be a single rigid, static object into one where conformational ensembles coexist. Besides, allostery, the transmission of signals from a distant site to the active site, is a direct outcome of the detailed dynamics of a given protein. Investigating how dynamics controls protein function is one of the overall aims of our studies. It is essential to probe protein function by combining information from all three types of data: sequence, structure and dynamics, which combine to define their functions. The abundance of protein sequence data in repositories like UniProt and Pfam is huge and is strongly complementary to the rich data of protein structures in PDB. Exploiting this wealth of information and coupling it with molecular simulations that provide information on protein dynamics, facilitates the understanding and predicting of protein function, which is the underlying motivation and overall objective of the present work.
The dynamic behavior of proteins is often altered upon the binding of ligands, partner proteins or other biological macromolecules such as DNA and RNA. This work describes the influence of binding on the intrinsic dynamics of proteins through studies on homooligomeric protein assemblies which are comprised of multiple subunits of the same protein. Specifically, this work compares the dynamics of functionally important residues of a single subunit in isolation with those in its assembled form. Next, is presented a systematic investigation of the extent of similarity between the protein dynamic communities obtained from molecular dynamics with those from a simpler molecular simulation method, the elastic network models. The focus is on the separate dynamic communities, which are those groups of residues, highly cohesive in terms of their motions and which move like a rigid unit. Elastic network models are models for protein cohesion and are particularly appropriate for application to this task. We also show how they can effectively capture the differences in community distributions for mutant and wild type forms of T4 lysozyme. Finally, a machine learning classification method is developed wherein protein dynamics information is coupled with structure, evolutionary and physicochemical properties to predict regulatory and functional binding sites.
This work emphasizes the collective interplay between sequence, structure and dynamics as the key to the understanding of protein function. It also highlights the use of simplified molecular representations for simulations, i.e., the elastic network model, which can often be suitable as a substitute for atomic molecular dynamics. The machine learning models developed as a part of this work strongly point up the importance of including protein dynamics to improve predictions. The methods developed have potential practical applications, for instance as predictive models for identification of hot spot residues for site-directed mutagenesis or even for the prediction of sites where potential therapeutics could bind to restore dynamics and other disturbed functions, or even to suggest ways to generate new functions.</p
Techniques to Minimize Energy Consumption in Cloud System
No full textCloud computing is being widely applied to a variety of large size computational problems.
These computational environments consist of many heterogeneous computing modules;
these modules incorporate with each other to implement the solution of various problems.
A typical cloud deployment consumes a significant amount of energy, and higher energy
consumption has an adverse impact on the environment. Reducing energy consumption in the cloud environment is both a research and an operational challenge for the current research community and industry. The objective of this research is to minimize the energy consumed by the cloud system, in particular, considering the execution of tasks (service requests) with the help of virtual machines. A survey of the state-of-the-art in an energy-efficient cloud computing system is presented. In this thesis, we have used four different approaches: (i) task allocation, (ii) virtual machine consolidation, (iii) virtual machine selection using Dynamic Voltage and Frequency Scaling (DVFS), and (iv) resource allocation in mobile cloud system to optimize energy consumption of the cloud system. All the proposedalgorithms are simulated with the help of CloudSim simulator.
The task allocation problem is a well known NP-complete problem. We have presented
two different approaches of task allocation to optimize the energy consumption and
makespan of the cloud system. The first approach deals with three task allocation algorithms based on metaheuristics techniques namely, Particle Swarm Optimization (PSO), Binary PSO (BPSO), and BAT. In the second approach, a deterministic Adaptive Task Allocation Algorithm (ATAA) is proposed to allocate tasks to the cloud system. The task allocated to the cloud system is represented with the help of an ETC (Expected Time to Compute) matrix. The ETC matrix holds the time required to compute a specific task on different Virtual Machines (VMs). The simulation is carried out to compare the performance of three proposed metaheuristic based task allocation algorithms by varying the number of VMs and tasks. And, also the performance of the proposed Adaptive Task Allocation Algorithm (ATAA) is analyzed by comparing with the random, and Round-Robin (inbuilt algorithm in CloudSim) algorithms. Simulation results indicate in favor of the proposed scheme (ATAA).
In a cloud system, VM consolidation deals with the allocation of VMs to hosts. In this thesis, a task-based VM-consolidation algorithm is proposed to minimize the energy
consumption, makespan, and task rejection rate of the cloud system. The proposed algorithm efficiently allocate tasks to VMs and then VMs to hosts. The performance of the proposed algorithm, i.e., Energy-aware Task-based Virtual Machine Consolidation (ETVMC) and the existing algorithms: First Come First Serve (FCFS), Round-Robin, and EERACC proposed in [31] are compared by varying the number task and number of VM with the help of CloudSim simulator. The simulation results indicate minimum energy consumed by the proposed algorithm in comparison to other existing algorithms
Dynamic Voltage and Frequency Scaling (DVFS) technique is a technique through which energy consumption can be minimized for computing resources. We have proposed a heuristic algorithm, i.e., Energy-Efficient DVFS-based Task Scheduling Algorithm (EEDTSA) for the selection of VM for each task to optimize the energy utilization by applying the DVFS technique. The DVFS Mechanism is applied to the virtual machines level to reduce the energy of the cloud system. Moreover, the performance of the diverse algorithms (Random allocation, and FCFS) are compared with the proposed DVFS-based VM selection algorithm. It can be observed from the simulation results that the proposed algorithm (EEDTSA) offers greater energy saving as compared to other existing techniques.
We have proposed a mobile cloud system with edge data center interfacing mobile user
to the cloud system. There are three computing entities (VMs that runs on the top of the host in the data center, VMs that runs on the top of the edge computing devices, and mobile computing devices) used by the mobile user. An energy efficient task allocation in mobile cloud system scheme, i.e., Energy-Efficient Task Allocation in Mobile Cloud System (EETAMCS) is proposed where the selection of appropriate VM for a task with a deadline is explained. Instead of offloading of tasks directly to the cloud data center, in the proposed scheme, the tasks can be offloaded to the edge data center to minimize the energy consumption and execution delay. The result analysis of the proposed algorithm obtained indicates the utilization of edge data centers reduces energy consumption and execution dela
Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network
No full textWorldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data
Evaluation of gene value and heuristic function of alternate plans in multi database system using Genetic Algorithm
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