Optimization methods for side-chain positioning and macromolecular docking

This dissertation proposes new optimization algorithms targeting protein-protein docking which is an important class of problems in computational structural biology. The ultimate goal of docking methods is to predict the 3-dimensional structure of a stable protein-protein complex. We study two specific problems encountered in predictive docking of proteins. The first problem is Side-Chain Positioning (SCP), a central component of homology modeling and computational protein docking methods. We formulate SCP as a Maximum Weighted Independent Set (MWIS) problem on an appropriately constructed graph. Our formulation also considers the significant special structure of proteins that SCP exhibits for docking. We develop an approximate algorithm that solves a relaxation of MWIS and employ randomized estimation heuristics to obtain high-quality feasible solutions to the problem. The algorithm is fully distributed and can be implemented on multi-processor architectures. Our computational results on a benchmark set of protein complexes show that the accuracy of our approximate MWIS-based algorithm predictions is comparable with the results achieved by a state-of-the-art method that finds an exact solution to SCP. The second problem we target in this work is protein docking refinement. We propose two different methods to solve the refinement problem. The first approach is based on a Monte Carlo-Minimization (MCM) search to optimize rigid-body and side-chain conformations for binding. In particular, we study the impact of optimally positioning the side-chains in the interface region between two proteins in the process of binding. We report computational results showing that incorporating side-chain flexibility in docking provides substantial improvement in the quality of docked predictions compared to the rigid-body approaches. Further, we demonstrate that the inclusion of unbound side-chain conformers in the side-chain search introduces significant improvement in the performance of the docking refinement protocols. In the second approach, we propose a novel stochastic optimization algorithm based on Subspace Semi-Definite programming-based Underestimation (SSDU), which aims to solve protein docking and protein structure prediction. SSDU is based on underestimating the binding energy function in a permissive subspace of the space of rigid-body motions. We apply Principal Component Analysis (PCA) to determine the permissive subspace and reduce the dimensionality of the conformational search space. We consider the general class of convex polynomial underestimators, and formulate the problem of finding such underestimators as a Semi-Definite Programming (SDP) problem. Using these underestimators, we perform a biased sampling in the vicinity of the conformational regions where the energy function is at its global minimum. Moreover, we develop an exploration procedure based on density-based clustering to detect the near-native regions even when there are many local minima residing far from each other. We also incorporate a Model Selection procedure into SSDU to pick a predictive conformation. Testing our algorithm over a benchmark of protein complexes indicates that SSDU substantially improves the quality of docking refinement compared with existing methods

Investigation and Modeling of the Dip Coating for Dispersions with Near Wall Effects

The key to controlling the quality of liquid dispersion coated films lies in the particle distribution in the flow field. The control of particle dispersion in the film can also empower the optimization of the process operation. Therefore, a numerical simulation of a solid-liquid suspension in the dip coating (free withdrawal) process using the finite element method for the fluid flow has been developed. The neutrally buoyant suspension is considered as a Newtonian fluid with a concentration-dependent viscosity. A continuum constitutive equation is employed based on the diffusive flux model in twodimensional flow. The main purpose of this study is used to assess the shear-induced migration phenomenon in free-surface of concentrated suspension and the effects of the coating bath walls near the substrate to explore the particle distribution in the flow field. Other parameters studied include particle concentration, particle radius, and withdrawal speed. The simulation results show a highly nonuniform distribution of particles in the coating film and recirculation regions. The suspension flow model predicts regions of low and high particle concentration compared to the average concentration. Higher concentration region presents at the middle and outer part of the coating film, varied depending on the parameter. Lower concentration region presents at the moving substrate region. Certainly, the nonuniform shear flow and the shape of the interface induces particle migration in concentrated suspension in the dip coating process

LTE and Wi-Fi Coexistence in Unlicensed Spectrum with Application to Smart Grid: A Review

Long Term Evolution (LTE) is expanding its utilization in unlicensed band by deploying LTE Unlicensed (LTEU) and Licensed Assisted Access LTE (LTE-LAA) technology. Smart Grid can take the advantages of unlicensed bands for achieving two-way communication between smart meters and utility data centers by using LTE-U/LTE-LAA. However, both schemes must co-exist with the incumbent Wi-Fi system. In this paper, several co-existence schemes of Wi-Fi and LTE technology is comprehensively reviewed. The challenges of deploying LTE and Wi-Fi in the same band are clearly addressed based on the papers reviewed. Solution procedures and techniques to resolve the challenging issues are discussed in a short manner. The performance of various network architectures such as listenbefore- talk (LBT) based LTE, carrier sense multiple access with collision avoidance (CSMA/CA) based Wi-Fi is briefly compared. Finally, an attempt is made to implement these proposed LTEWi- Fi models in smart grid technology.Comment: submitted in 2018 IEEE PES T&

Multiple sclerosis Lesion Detection via Machine Learning Algorithm based on converting 3D to 2D MRI images

In the twenty first century, there have been various scientific discoveries which have helped in addressing some of the fundamental health issues. Specifically, the discovery of machines which are able to assess the internal conditions of individuals has been a significant boost in the medical field. This paper or case study is the continuation of a previous research which aimed to create artificial models using support vector machines (SVM) to classify MS and normal brain MRI images, analyze the effectiveness of these models and their potential to use them in Multiple Sclerosis (MS) diagnosis. In the previous study presented at the Cognitive InfoCommunication (CogInfoCom 2019) conference, we intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. The previous paper concluded that SVM is a potential method which can be involved during MS diagnosis, however, in order to confirm this statement more research and other potentially effective methods should be included in the research and need to be tested. First, this study continues the research of SVM used for classification and Cellular Learning Automata (CLA), then it expands the research to other method such as Artificial Neural Networks (ANN) and k-Nearest Neighbor (k-NN) and then compares the results of these

Dual-Band RFID Tag Antenna Based on the Hilbert-Curve Fractal for HF and UHF Applications

A novel single-radiator card-type tag is proposed which is constructed using a series Hilbert-curve loop and matched stub for high frequency (HF)/ultra high frequency (UHF) dual-band radio frequency identification (RFID) positioning applications. This is achieved by merging the series Hilbert-curve for implementing the HF coil antenna, and square loop structure for implementing the UHF antenna to form a single RFID tag radiator. The RFID tag has directivity of 1.75 dBi at 25 MHz, 2.65 dBi at 785 MHz, 2.82 MHz at 835 MHz and 2.75 dBi at 925 MHz. The tag exhibits circular polarisation with -3 dB axial-ratio bandwidth of 14, 480, 605 and 455 MHz at 25, 785, 835 and 925 MHz, respectively. The radiation characteristics of the RFID tag is quasi-omnidirectional in its two orthogonal planes. Impedance matching circuits for the HF/UHF dual-band RFID tag are designed for optimal power transfer with the microchip. The resulting dual-band tag is highly compact in size and possesses good overall performance which makes it suitable for diverse applications

Do Multiple Sclerosis and Neuromyelitis Optica Patients Have a Lower Chance of Developing Neurological Complications of COVID-19, Compared to Healthy People? The Role of ACE2

As COVID-19 spreads all around the world, it indicates various side effects and complications. Currently, we know that, this disease can affect other organs like brain. The growing number of neurological complications from this disease suggests that, the coronavirus is a neurotropic virus, and this neurotropicity has been attributed to the expression and presence of receptors of angiotensin-converting enzyme 2 (ACE2) in central nervous system (CNS). Unlike ACE itself, ACE2 converts angiotensin 2 to 1, and is present in lung alveolar epithelial cells. In this regard, the coronavirus is likely to use ACE2 as a receptor to enter and infect human cells. The virus causes disease in some other areas such as pancreas and colon with the same mechanism as that of ACE2 receptor. Moreover, the high presence of the corresponding receptor in the CNS has increased the likelihood of neurological involvement in this virus. The binding of the virus to this receptor (Figure 1), which is present in different areas of the brain such as the glial cells, neurons and astrocytes spreads the virus to the CNS and this induces a variety of neurological symptoms. One of the most important areas of the brain that causes high expressions of ACE and ACE2, angiotensinogen, and angiotensin II secretion in the CNS, is perivascular astrocytes. Neuromyelitis optica spectrum disorder (NMOSD) is an astrocytopathy in which a high rate of astrocyte destruction occurs. Some studies have also shown that, these perivascular astrocytes are largely eliminated in multiple sclerosis (MS), especially at chronic stages. This destruction could justify the studies, which have demonstrated the low levels of ACE2 in the cerebrospinal fluid of these patients. Matsushita et al. revealed that, angiotensin II, ACE, and ACE2 levels were lower in the cerebrospinal fluid of the patients with seropositive NMOSD compared to healthy individuals. Accordingly, the same was true for ACE2 levels in MS patients. Another study confirmed the low level of ACE2 concentration in the cerebrospinal fluid of the patients with MS. The destruction of astrocytes and low level of ACE2 concentration could theoretically predict the ACE2 receptor deficiency which might reduce the chance of entering the virus into the CNS, and consequently, decrease the neurological complications. This may suggest that, neurological complications are less likely to occur in the patients with NMOSD and MS in case of developing COVID-19. However, as with all diseases, it is not possible to simply predict the lower degree of neurological complications in these patients on the basis of one factor such as a lower expression of ACE2 in these patients. Thereafter, further investigations are required to shed light on how MS and NMOSD patients develop infectious diseases related to the CNS