Sharif Digital Repository

Folded protein experience several conformational substates. Each substate reperesents a minimum in the free energy surface of the system. Since Myoglobin is the smallest protein that can reversibly bind to gas molecules and, it can be used as prototype for more complex systems.IR pecrtum of CO inside of myolgobin shows that distinct conformational substates can occur. It was shown that Histidine residue inside heme pocket plays an essential rolle in structural properties of substates. In this study, we used molecular dynamice simulation together with umbrella sampling method to obtain free energy profile of MbCO system. The configuration of distal Histidine (His64), CO and Heme is studied... 

The use of super-swelling polymers is steadily increasing and the applications in industry are continuing to grow. With the authorization of the superabsorbents in food packaging by the Food and Drug Administration recently, demand may soon take off in the market. The worldwide production of SAPs is more than one million tons per year, representing a multibillion-dollar market. However, the increase in prices of petroleum products in recent years may be a drawback for these acrylic-based materials. Thus, there is now a need to develop natural-based super-swelling hydrogels which are more economical and environment friendly. In addition, the super-swelling gels are promising novel functions... 

Milk is One of the most important source s of protein in the world. during the process of cheese the most of these protein remain in whey. There are different methods to separate these protein from whey. The one of advanced method is ultrafiltartion, this method is based on a permeable membranes, with different pore size. There are many research works in this field. In this project we have tried to study the effect ofpressure, temperature, concentration and resistance time on membrane porosity. In order to design the experiment and analyze the results tagochi L 16 methods is used. The PVC membranes were prepaid by gas anti solvent method, experiment showed that changes of temperature and... 

Phospholipid membranes and vesicles play important roles in the cellular functioning, otein signaling and material transport inside cells. Protein-embedded vesiclesare also used for targeted drug delivery. In this thesis, we use molecular dynamicsmethods and study (i) the formation of vesicles from flat lipid bilayers (ii) the mechanicalproperties of vesicles under compressive forces (iii) the shape variations ofvesicles with and without transmembrane proteins (iv) protein clustering.We grow our vesicles from lipid bilayers, which may contain proteins with differentconcentrations. We start with a random initial distribution of proteins that allowsus to monitor the clustering and... 

In this thesis, proteinnanoparticle interactions have been studied using Molecular Dynamics simulation. sT fN, is the studied protein in this work. It is one of the most important proteins in the blood fluid. In this work, nanoparticle has been modeled as a surface; because the radius of gyration of sT fN is less than 3nm, so when it is near an enough big nanoparticle (100200 nm), nanoparticle seems as a surface from the view of protein. The surface of nanoparticle consists of sodium ions. Surface of nanoparticle, has been modeled using two types of surface:flat surface and nonflat surface. Protein interaction with surface, has been studied in two different initial orientations with respect... 

Proteins are one of the most important bio-molecules. they play an important role in every aspect of living organisms. The information for synthesizing these important molecules is coded in the DNA of that organism. After the coded data is read and transcribed by some other bio-molecules that are proteins themselves, the protein is made and starts it's biological function in different organs. For this reason, investigating properties of proteins helps us to better understand how life works. Understanding the dynamics of proteins, which are some polymers with special energy landscape properties, has attracted the attention of physicists in the 20th century. Proteins are chains of amino... 

Diabetes has become the most prevalent disease all among the world. One of the complications of this disease is diabetic nephropathy. Diabetic nephropathy occurs when the kidney cells encounter high glucose concentration for long periods. Kidney cells use glucose as their energy supply. However, the excess glucose in the diabetics triggers specific gene pathways, gradually leading to diabetic nephropathy. As a result, proteins’ levels and the activity of transcription factors change during the disease progression. Diabetic nephropathy is characterized by excessive deposition of the proteins comprising the extracellular matrix in the mesangium, basement membrane of the glomerulus, and in the... 

In Hemodialysis, membrane dialyser and commonly hollow fibers are used to treat patients who suffer from end stage renal failure. When blood flows into membrane, it loses uremic toxins such as urea and creatinine. On the other hand, some of the vital proteins of blood pass through the membrane or they are adsorbed on the membrane surface. The more hydrophilicity of the membrane, the less adsorption of proteins is. In this study, to achieve high hydrophilicity and low adsorption of proteins on the polysulfone membrane surface, the presence of hydrophilic nano-particles SBA-15 in membrane composition is investigated. The nano-particles with mass percentages 0.25%, 0.5 % and 1 % to one mass of... 

Membrane bioreactors are increasingly being used for treatment of domestic and industrial effluents. A major operational drawback of this system is membrane fouling attributed to soluble microbial products (SMPs). In this study sampling were taken from microbial products on the surface and within the membrane pores and mixed liquir for mass characterization of protein content. Water-washing was used for surface and reversible portion of pore samples. Chemical wash with urea was used for further cleansing of SMPs within the pores. All samples were concentrate by precipitation with ammonium sulfate salt prior to analysis. Total protein content of samples was measured by lowry method. SDS-PAGE... 

This study aims to investigate the influence of important operational parameters on membrane fouling with the approach of fouling layers and to model these influences in the membrane bioreactor (MBR) for municipal wastewater treatment. At the first phase, 11 operational scenarios were executed using a pilot setup for identifying the system behavior and the optimal scenario. Protein and carbohydrate as the fouling components (SMP) and the transmembrane pressure (TMP) as the fouling criteria were measured in the tests. By studying the influence of operational parameters (sludge retention time (SRT), aeration rate and filtration mode), some useful results obtained for the next phase, including... 

Surface phenomena and dynamic interfacial properties plays a significant role in multiphase gas-liquid & liquid-liquid processes applied in different industrial applications. However, current laboratory equipment and represented methods for dynamic and unsteady state condition caused by motivation of interface of two phases (like rising bubble), in purpose of qualitative investigation of interphase properties are not satisfying enough. Also, common modeling methods are represented with lots of modified assumptions which are not validated properly with experimental results. In current thesis, with development of laboratory tools and applying the “rising bubble method” for investigating the... 

New biological discussions in quantum mechanics that has grown considerably in the past decade. Meanwhile, considering some of interesting problems in biology and chemistry, the use of quantum approach superposition principle and tunneling effect explain some of the phenomena of life more than several. Nowadays, regarding quantum description, provided to explain how we recognize different odors, what the mechanism photosynthesis is, and how bind navigation occurs. Furthermore, the new field of quantum biology sheds light in underestanding the issue of heritance and genetics inforamtion. In this thesis, after reviewing some interesting topics of quantum biology, the data transfer from... 

Protein-ligand binding affinity is extremely important for finding new candidates in drug discovery and computational biochemistry. One of the physical characteristics for protein ligand interactions has been dissociation constant (KD) which can be obtain experimentally. However, there have been tremendous efforts to predict KD using modeling and computational approaches for protein-ligand interactions. In this project, we have exploited Convolutional Neural Network (CNN) model based on KDeep design, PDBBind version 2016 refined set training data, and examining it with KDeep core set test data. In order to modify KDeep,instead of 24 rotations (0, 90, 180 and 270 degrees in selection of two... 

Protein, as the functional unit of the cell, plays a vital role in its biological function. With the advent of advanced sequencing techniques in recent years and the consequent exponential growth of the number of protein sequences extracted from diverse biological samples, their analysis, comparison, and classification have faced a considerable challenge. Existing methods for comparing proteins divide into two categories: methods based on alignment and alignment-free. Although alignment-based methods are among the most accurate methods, they face inherent limitations such as poor analysis of protein groups with low sequence similarity, time complexity, computational complexity, and memory... 

Drug-target binding affinity prediction is one of the most important and vital part of drug discovery. The computational methods to predict binfing affinity is a standing challenge in drug discovery. State-of-the-art models are usually based on supervised machine learning with known label information. It is expensive and time-consuming to collect labeled data. This thesis proposes a semi-supervised model based on convolutional GAN (Generative adversarial networks). The model consists of two Gans and Two CNN blocks for feature extraction and fully connected layers for prediction. Gan can learn protein and drug features from unlabeled data. We evaluate the performance of our method using four...