An ANFIS approach to transmembrane protein prediction

This paper is concerned with transmembrane prediction analysis. Most of novel drug design requires the use of Membrane proteins. Transmembrane protein structure allows pharmaceutical industry to design new drugs based on structural layout. However, laboratory experimental structure determination by X-ray crystallography is difficult to be achieved as the hydrophobic molecules do not crystalize easily. Moreover, the sheer number of proteins demands a computational solution to transmembrane regions identifications. This research therefore presents a novel Adaptive Neural Fuzzy Inference System (ANFIS) approach to predict and analyze of membrane helices in amino acid sequences. The ANFIS technique is implemented to predict membrane helices using sliding window data capturing. The paper uses hydrophobicity and propensity to encode the datasets using the conventional one letter symbol of amino acid residues. The computer simulation results show that the offered ANFIS methodology predicts transmembrane regions with high accuracy for randomly selected proteins

An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down

Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation

This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis

NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Cascading classifier application for topology prediction of transmembrane beta-barrel proteins

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modelling is essential. This paper focuses on the topology prediction of transmembrane beta-barrel proteins. In the field of bioinformatics, many years of research has been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past for the prediction of TMB proteins topology, however the use of cascading classifier has never been fully explored. This research presents a novel approach to TMB topology prediction with the use of a cascading classifier. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. By using the cascading classifier approach the best overall accuracy is 76.3% with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier

CESE-2019

This book is a collation of articles published in the Special Issue "CESE-2019: Applications of Membranes" in the journal Sustainability. It contains a wide variety of topics such as the removal of trace organic contaminants using combined direct contact membrane distillation–UV photolysis; evaluating the feasibility of forward osmosis in diluting reverse osmosis concentrate; tailoring the effects of titanium dioxide (TiO2) and polyvinyl alcohol (PVA) in the separation and antifouling performance of thin-film composite polyvinylidene fluoride (PVDF) membrane; enhancing the antibacterial properties of PVDF membrane by surface modification using TiO2 and silver nanoparticles; and reviews on membrane fouling in membrane bioreactor (MBR) systems and recent advances in the prediction of fouling in MBRs. The book is suitable for postgraduate students and researchers working in the field of membrane applications for treating aqueous solutions

CO2 sequestration by hybrid integrative photosynthesis (CO2-SHIP): A green initiative for multi-product biorefineries

Diversification among organisms has resulted in uniqueness and complexity between them leading to maintenance of continuous energy supply while restoring equilibrium in the environment. Photosynthetic organisms are capable of naturally converting atmospheric CO2 in presence of sunlight and water leading to production of organic molecules whereas artificial photosynthesis yields solar fuels by directly converting light through photovoltaic cells. Therefore, hybrid integration of both photosynthetic mechanisms involving catalytic processes by converting light energy which is an unlimited source of energy leading to the production of fuels and various valuable products, will be an amicable solution for efficient CO2 sequestration. For optimizing photosynthesis various approaches and processes need to be improvised such as light harvesting complexes, reaction centres, carbon fixation and metabolic pathways for enhancing their photosynthetic efficiencies. This review highlights enormous potential and possibility of solar energy utilization leading to a new horizon to the researchers for exploring the CO2 sequestration by hybrid integrative photosynthesis (CO2-SHIP) for a sustainable renewable production of energy components for multi-product biorefineries. Keywords: Artificial leaf, Carbon dioxide, Catalyst, Photosynthesis, Sequestratio

Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies

Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm