Influence of metal oxide catalyst on co-pyrolysis of biomass and COVID-19 waste

The disposal of waste generated by the COVID-19 pandemic is still a challenge to the government in most countries. The present study shines its light on the catalytic effect of metal oxide on converting COVID-19 waste i.e. used face masks into valuable products through co-pyrolysis. The co-pyrolysis trial was carried out for a mixture of waste face mask (WFM) and Moringa oleifera (MO) biomass at a constant temperature of 450°C for 15 min of resident time. This investigation focuses on studying the catalytic effect of calcium oxide (CaO) on the by-products of the pyrolysis process. From the FT-IR studies, it is observed that the CaO catalyst assisted to reduce oxygen as well as sulphur and carboxylic acids in the bio-oil due to its strong basic nature. The FE-SEM images suggest the increase in porous structure with catalytic pyrolysis (CP) char compared to non-catalytic pyrolysis (NCP) char. The catalytic activity of CaO increased the alcoholic content with a reduction in aldehydes and ketones in the bio-oil. The addition of WFM to the biomass with CaO catalyst pyrolysis (CP) delivered a higher oil yield of 52% compared to non-catalytic pyrolysis (NCP).

Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber-physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass-spring-damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

Disturbance rejection for multi-weighted complex dynamical networks with actuator saturation and deception attacks via hybrid-triggered mechanism

In this work, we address hybrid-driven-based robust synchronization problem for multi-weighted complex dynamical networks with actuator saturation and deception attacks. The hybrid-triggered mechanism, which combines a switch between the event-triggered scheme and the time-triggered scheme, is often used to reduce the data transmission and the alleviate network burden. Further, the equivalent-input-disturbance technique is applied to eliminate the unknown disturbance effect of the addressed system. Moreover, a memory controller is designed under actuator saturation to ensure that the resultant augmented system is asymptotically synchronized even in the presence of deception attacks. Finally, three numerical examples are given to show the validity of the obtained theoretical results.

Nanotechnology- A ray of hope for heavy metals removal

Environmental effects of heavy metal pollution are considered as a widespread problem throughout the world, as it jeopardizes human health and also reduces the sustainability of a cleaner environment. Removal of such noxious pollutants from wastewater is pivotal because it provides a propitious solution for a cleaner environment and water scarcity. Adsorption treatment plays a significant role in water remediation due to its potent treatment and low cost of adsorbents. In the last two decades, researchers have been highly focused on the modification of adsorption treatment by functionalized and surface-modified nanomaterials which has spurred intense research. The characteristics of nano adsorbents attract global scientists as it is also economically viable. This review shines its light on the functionalized nanomaterials application for heavy metals removal from wastewater and also highlights the importance of regeneration of nanomaterials in the view of visualizing the economic aspects along with a cleaner environment. The review also focused on the proper disposal of nanomaterials with crucial issues that persist in the adsorption process and also emphasize future research modification at a large-scale application in industries.

Design of robust tracking and disturbance attenuation control for stochastic control systems

The focus of current research is to address the problem of robust output tracking, input delay compensation and disturbance attenuation performance for a family of stochastic systems by implementing the improved-equivalent-input-disturbance (IEID) estimator and the extended Smith predictor (ESP) technique. By integrating the observer and IEID-estimator together with ESP, a new closed-loop configuration is presented. Then, Lyapunov based mean-square asymptotic stability criterion is obtained. According to attained stability criterion, an IEID and ESP based-controller is designed, which ultimately guarantees the exact output tracking. Simulation studies of numerical examples are offered to expose the authenticity of IEID and ESP-based controller. Further, the proposed outcomes in comparison with existing results are presented to demonstrate the efficaciousness of the established control procedure.

Composite fault-tolerant and anti-disturbance control for switched fuzzy stochastic systems

This paper investigates the issue of fault-tolerant and anti-disturbance attenuation for a two-dimensional modified repetitive control system (2D MRCS) which is described by switched fuzzy systems with multiple disturbances. In particular, the multiple disturbances contain an exogenous disturbance and standard Wiener noise. Specifically, a generalized extended state observer (GESO) is incorporated with the 2D MRCS to estimate both fault and exogenous multiple disturbances so that the disturbances and faults can be attenuated in the control input. Further, the improved 2D MRCS relaxes the stability condition and provides an enhanced tracking performance. Based on the Lyapunov function approach, pole placement technique and average dwell time approach, the stability criteria for the considered system is developed in terms of linear matrix inequality (LMI). Then an algorithm for designing a GESO-based 2D MRC design is developed based on the obtained LMIs. Further, the results developed are validated in the simulation section through three numerical examples.

An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction

Deep learning models demonstrate superior performance in image classification problems. COVID-19 image classification is developed using single deep learning models. In this paper, an efficient hardware architecture based on an ensemble deep learning model is built to identify the COVID-19 using chest X-ray (CXR) records. Five deep learning models namely ResNet, fitness, IRCNN (Inception Recurrent Convolutional Neural Network), effectiveness, and Fitnet are ensembled for fine-tuning and enhancing the performance of the COVID-19 identification; these models are chosen as they individually perform better in other applications. Experimental analysis shows that the accuracy, precision, recall, and F1 for COVID-19 detection are 0.99,0.98,0.98, and 0.98 respectively. An application-specific hardware architecture incorporates the pipeline, parallel processing, reusability of computational resources by carefully exploiting the data flow and resource availability. The processing element (PE) and the CNN architecture are modeled using Verilog, simulated, and synthesized using cadence with Taiwan Semiconductor Manufacturing Co Ltd (TSMC) 90 nm tech file. The simulated results show a 40% reduction in the latency and number of clock cycles. The computations and power consumptions are minimized by designing the PE as a data-aware unit. Thus, the proposed architecture is best suited for Covid-19 prediction and diagnosis.

Liquid hot water as sustainable biomass pretreatment technique for bioenergy production: A review

In recent years, lignocellulosic biomass has emerged as one of the most versatile energy sources among the research community for the production of biofuels and value-added chemicals. However, biomass pretreatment plays an important role in reducing the recalcitrant properties of lignocellulose, leading to superior quality of target products in bioenergy production. Among existing pretreatment techniques, liquid hot water (LHW) pretreatment has several outstanding advantages compared to others including minimum formation of monomeric sugars, significant removal of hemicellulose, and positive environmental impacts; however, several constraints of LHW pretreatment should be clarified. This contribution aims to provide a comprehensive analysis of reaction mechanism, reactor characteristics, influencing factors, techno-economic aspects, challenges, and prospects for LHW-based biomass pretreatment. Generally, LHW pretreatment could be widely employed in bioenergy processing from biomass, but circular economy-based advanced pretreatment techniques should be further studied in the future to achieve maximum efficiency, and minimum cost and drawbacks.

Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks

This paper investigates the robust synchronization problem for a class of master-slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) estimation technique is applied to compensate for the effects of unknown uncertainty and disturbances in the system output. In addition, to reduce the burden of the communication channel in the addressed MSNNs and improve the utilization of bandwidth an event-triggered control protocol is developed to obtain the synchronization of MSNNs. In particular, event-triggering conditions are verified periodically at every sampling instant in both sensors and actuators to avoid the Zeno behavior in the networks. By designing an appropriate low-pass filter in the EID estimator block, the accuracy of disturbance estimation performance is improved. Moreover, by concatenating the synchronization error, observer, and filter states as a single state vector, an augmented system is formulated. Then the tangible delay-dependent stability condition for that augmented system is established by employing the Lyapunov stability theory and reciprocally convex approach. Based on the feasible solutions of the derived stability conditions, the event-triggering parameters, controller, and observer gains are co-designed. Finally, two toy examples are given to illustrate the established theoretical findings.

Disturbance rejection in fuzzy systems based on two dimensional modified repetitive-control

This paper concerns with the issues of designing an improved-equivalent-input-disturbance (IEID) based robust two dimensional modified repetitive control (2D MRC) for a class of fuzzy systems in the presence of aperiodic disturbances. Specifically, IEID-estimator is implemented to the 2D MRC systems that estimates all types of disturbances and compensates them for assuring robust stability. In particular, the proposed 2D MRC system has two different type of behaviours such as continuous control and discrete learning independently. To obtain gains of the observer and the controller, an adequate set of robust stability conditions is derived in the form of a linear-matrix-inequalities. Finally, simulation results for three numerical examples are provided to depict the efficacy of the proposed control technique.

CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

MPTherm: database for membrane protein thermodynamics for understanding folding and stability

The functions of membrane proteins (MPs) are attributed to their structure and stability. Factors influencing the stability of MPs differ from globular proteins due to the presence of membrane spanning regions. Thermodynamic data of MPs aid to understand the relationship among their structure, stability and function. Although a wealth of experimental data on thermodynamics of MPs are reported in the literature, there is no database available explicitly for MPs. In this work, we have developed a database for MP thermodynamics, MPTherm, which contains more than 7000 thermodynamic data from about 320 MPs. Each entry contains protein sequence and structural information, membrane topology, experimental conditions, thermodynamic parameters such as melting temperature, free energy, enthalpy etc. and literature information. MPTherm assists users to retrieve the data by using different search and display options. We have also provided the sequence and structure visualization as well as cross-links to UniProt and PDB databases. MPTherm database is freely available at http://www.iitm.ac.in/bioinfo/mptherm/. It is implemented in HTML, PHP, MySQL and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Opera. MPTherm would serve as an effective resource for understanding the stability of MPs, development of prediction tools and identifying drug targets for diseases associated with MPs.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Atrial fibrillation and preexcitation - A licence to kill

Atrial fibrillation becomes a potentially lethal arrhythmia in the presence of preexcitation because the rapid ventricular activation can result in ventricular fibrillation. Fortunately, radiofrequency ablation is an effective treatment for these patients. Specific points of interest regarding this association are the mechanism of increased incidence of atrial fibrillation and the current management of patients presenting in atrial fibrillation. These are discussed in this editorial.

Synchronization of complex dynamical networks with random coupling delay and actuator faults

This paper addresses the issue of passivity-based synchronization problem for a family of Markovian jump neutral complex dynamical networks (NCDNs) with coupling delay and actuator faults. Also, by considering the effect of random fluctuation in complex dynamical network systems, the occurrence of coupling delay are taken in terms of a stochastic distribution, which obeys the Bernoulli distribution. To handle the fault effects in actuators of proposed complex network systems, an actuator fault model is considered. The main objective of this paper is to develop a robust state feedback controller such that for all possible actuator failures and random coupling delays, all nodes of the proposed Markovian jump NCDNs is globally asymptotically synchronized to the reference node in mean square sense and guarantee the output strict passivity performance. By developing a suitable Lyapunov-Krasovskii functional and utilizing the Wirtinger-based integral inequality, the required a set of sufficient conditions for the synchronization of proposed system is established in form of linear matrix inequalities. Finally, three numerical examples including a 3-dimensional Lorenz chaotic model are provided to demonstrate the correctness and superiority of the proposed control scheme.

Estimation and disturbance rejection performance for fractional order fuzzy systems

This paper gives attention to the issues of output tracking and disturbance rejection performance for a class of fractional order Takagi-Sugeno fuzzy systems in the presence of time-varying delay and unknown external disturbances. More specifically, a new configuration of a fractional order modified repetitive controller that incorporates an improved equivalent-input-disturbance estimator and gain fluctuations in its design is proposed to perform disturbance rejection for the addressed system. By introducing a continuous frequency distributed equivalent model and using the Lyapunov-Krasovskii stability theory, a new set of sufficient conditions ensuring robust asymptotic stability of the resulting closed-loop system is obtained in the framework of linear matrix inequalities. Finally, a numerical example is presented to validate the developed theoretical results, where it is shown that the obtained conditions could force the considered system output to exactly track the given any kind of reference signal by compensating the unknown external disturbance.

Design of observer-based non-fragile load frequency control for power systems with electric vehicles

This paper establishes an observer-based finite-time non-fragile load frequency control design using electric vehicles for power systems with modeling uncertainties and external disturbances. A state space representation of the addressed power systems together with dynamic interactions of electric vehicles is formulated. A full-order observer-based non-fragile controller is designed to ensure finite-time boundedness and satisfactory finite-time H_∞ performance of the considered system. By constructing an augmented Lyapunov-Krasovskii functional and employing Wirtinger-based integral inequality, the required conditions are obtained in terms of linear matrix inequalities. The desired non-fragile load frequency control law is presented via the observer-based feedback approach. Simulations are given to show the effectiveness of the proposed control scheme.

Finite-time decentralized non-fragile dissipative control for large-scale systems against actuator saturation

This paper employs linear matrix inequality-based optimization algorithm to establish finite-time boundedness and dissipativeness for a class of large-scale systems in the presence of actuator faults and actuator saturation. In addition, for the proposed system, a novel time-varying actuator fault model is incorporated in controller design, which is more general than the conventional actuator fault models. Specifically, by constructing a suitable Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived, which ensures the finite-time boundedness with dissipativity of the considered large-scale systems. The main intention of this paper is to design a novel decentralized fault-tolerant controller to compensate simultaneously the actuator faults, actuator saturations and nonlinear interconnections. Finally, an example and its simulation study are presented to verify the effectiveness and potential of the proposed control design technique.

Consensus of uncertain multi-agent systems with input delay and disturbances

In this paper, the problem of robust consensus for multi-agent systems affected by external disturbances is discussed. A novel consensus control is developed by using a feedback controller based on disturbance rejection and Smith predictor scheme. Specifically, the disturbance rejection performance of the uncertain multi-agent systems is improved according to the estimation of equivalent-input-disturbance and the effect of time delay in the control system is reduced via Smith predictor scheme by shifting the delay outside the feedback loop. Furthermore, by combining Lyapunov theory, matrix inequality techniques and properties of Kronecker product, a robust feedback controller for each agent is designed such that the desired consensus of the uncertain multi-agent systems affected by external disturbances can be ensured. Finally, to illustrate the validity of the designed control scheme, two numerical examples with simulation results are provided.

Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer

Background:

Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects.

Objective:

In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer.

Methods:

Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs.

Results:

The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges.

Conclusion:

Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks

This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator (FUDE) based feedback control strategy is proposed to not only synchronize the considered FCDNs but also guaranteeing the precise rejection of unmodelled system uncertainty and external disturbance. Especially, in FUDE-based approach, model uncertainties and external disturbance are integrated as a lumped disturbance and it does not require a completely known system model or a disturbance model. On the other hand, the design algorithm for the proposed control strategy is based on the state-space framework, rather than frequency-based design methodologies in the literature, which helps for predominant comprehension of the inner system behaviour. Also, by the temperance of Lyapunov stability theory and fractional calculus, a set of adequate conditions in the linear matrix inequality framework is obtained, which guarantees the robust synchronization of the closed-loop system. Furthermore, an iterative optimization algorithm is proposed to improve control robustness against the external disturbance and model uncertainties. Finally, two numerical illustrations including financial network model, where the influence of adjustment of macro-economic policies in the entire financial system are given to exhibit the rightness and important features of the acquired theoretical results.

Dissipativity-based non-fragile sampled-data control design of interval type-2 fuzzy systems subject to random delays

This paper investigates the β-dissipativity-based reliable non-fragile sampled-data control problem for a class of interval type-2 (IT2) fuzzy systems. In particular, it is allowed to have randomly occurring time-varying delays in the controller design, which are modeled by Bernoulli distributed white noise sequences. Precisely, the IT2 fuzzy model and the non-fragile sampled-data controller are formulated by considering the mismatched membership functions. By constructing an appropriate Lyapunov-Krasovskii functional, a set of delay-dependent conditions is derived to guarantee that the closed-loop IT2 fuzzy system is strictly <Q,S,R>-β-dissipative. Moreover, the gain matrices of feedback reliable non-fragile sampled-data controller are derived in terms of linear matrix inequalities (LMIs), which can be solved by using existing LMI solvers. Two numerical examples are eventually given to illustrate the applicability and effectiveness of the proposed controller design technique.

Finite-time consensus of Markov jumping multi-agent systems with time-varying actuator faults and input saturation

This paper gives attention to the issue of finite-time leader-following consensus of nonlinear discrete-time multi-agent systems with Markov jump parameters. A robust fault-tolerant control protocol that takes the effect of time-varying actuator faults and actuator saturation into account is considered for the addressed system. The main purpose of the paper is to design a fault-tolerant controller such that the leader-following consensus of the addressed system is achieved over a prescribed finite-time interval. By using the Lyapunov functional approach, Abel's lemma and some properties of Kronecker product, a sufficient condition for the existence of fault-tolerant state feedback controller for the addressed system is presented and an explicit parameterization of such a controller is obtained. Eventually, a numerical example along with its simulation results is exploited to reflect the applicability of the proposed design method, wherein the robust performance of controller is exhibited despite the presence of actuator saturation and time-varying actuator faults.

Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer

Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.

Reuse of pacemakers, defibrillators and cardiac resynchronisation devices

OBJECTIVE

Access to pacemakers remains poor among many patients in low/middle-income countries. Reuse of explanted pacemakers is a possible solution, but is still not widespread because of concerns regarding outcomes, especially infection. Our objective was to study early outcomes with implants using reused devices and compare them with those with implants using new devices.

METHODS

We studied all patients who underwent implantation of a new or reused pacemaker, cardiac resynchronisation therapy (CRT) device or implantable cardioverter defibrillator (ICD) in the last 5 years at a single institution. We analysed outcomes related to infection, device malfunction and device-related death within 6 months after initial implantation.

RESULTS

During the study period, 887 patients underwent device implant, including 127 CRT devices or ICDs. Of these, 260 devices (29.3%) were reused and the others were new. At 6 months, there were three device-related infections in implants using a new device. There were no infections among patients receiving a reused device. There were no device malfunctions or device-related deaths in either group.

CONCLUSIONS

We found no difference in rate of infection or device malfunction among patients getting a reused device as compared with those with a new device. This study reinforces the safety of reusing devices for implant including CRT and ICDs.

Passivity of memristor-based BAM neural networks with different memductance and uncertain delays

This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets

Somatic mutations developed with missense, silent, insertions and deletions have varying effects on the resulting protein and are one of the important reasons for cancer development. In this study, we have systematically analysed the effect of these mutations at protein level in 41 different cancer types from COSMIC database on different perspectives: (i) Preference of residues at the mutant positions, (ii) probability of substitutions, (iii) influence of neighbouring residues in driver and passenger mutations, (iv) distribution of driver and passenger mutations around hotspot site in five typical genes and (v) distribution of silent and missense substitutions. We observed that R→H substitution is dominant in drivers followed by R→Q and R→C whereas E→K has the highest preference in passenger mutations. A set of 17 mutations including R→Y, W→A and V→R are specific to driver mutations and 31 preferred substitutions are observed only in passenger mutations. These frequencies of driver mutations vary across different cancer types and are selective to specific tissues. Further, driver missense mutations are mainly surrounded with silent driver mutations whereas the passenger missense mutations are surrounded with silent passenger mutations. This study reveals the variation of mutations at protein level in different cancer types and their preferences in cancer genes and provides new insights for understanding cancer mutations and drug development.

Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer

Cancer is one of the most life-threatening diseases and mutations in several genes are the vital cause in tumorigenesis. Protein kinases play essential roles in cancer progression and specifically, epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this work, we have developed a method to classify single amino acid polymorphisms (SAPs) in EGFR into disease-causing (driver) and neutral (passenger) mutations using both sequence and structure based features of the mutation site by machine learning approaches. We compiled a set of 222 features and selected a set of 21 properties utilizing feature selection methods, for maximizing the prediction performance. In a set of 540 mutants, we obtained an overall classification accuracy of 67.8% with 10 fold cross validation using support vector machines. Further, the mutations have been grouped into four sets based on secondary structure and accessible surface area, which enhanced the overall classification accuracy to 80.2%, 81.9%, 77.9% and 75.1% for helix, strand, coil-buried and coil-exposed mutants, respectively. The method was tested with a blind dataset of 60 mutations, which showed an average accuracy of 85.4%. These accuracy levels are superior to other methods available in the literature for EGFR mutants, with an increase of more than 30%. Moreover, we have screened all possible single amino acid polymorphisms (SAPs) in EGFR and suggested the probable driver and passenger mutations, which would help in the development of mutation specific drugs for cancer treatment.

Prediction of protein disorder on amino acid substitutions

Intrinsically disordered regions of proteins are known to have many functional roles in cell signaling and regulatory pathways. The altered expression of these proteins due to mutations is associated with various diseases. Currently, most of the available methods focus on predicting the disordered proteins or the disordered regions in a protein. On the other hand, methods developed for predicting protein disorder on mutation showed a poor performance with a maximum accuracy of 70%. Hence, in this work, we have developed a novel method to classify the disorder-related amino acid substitutions using amino acid properties, substitution matrices, and the effect of neighboring residues that showed an accuracy of 90.0% with a sensitivity and specificity of 94.9 and 80.6%, respectively, in 10-fold cross-validation. The method was evaluated with a test set of 20% data using 10 iterations, which showed an average accuracy of 88.9%. Furthermore, we systematically analyzed the features responsible for the better performance of our method and observed that neighboring residues play an important role in defining the disorder of a given residue in a protein sequence. We have developed a prediction server to identify disorder-related mutations, and it is available at http://www.iitm.ac.in/bioinfo/DIM_Pred/.