Introduction Artificial neural networks are a class of computational structures (Lesk, 2013) made up of several processing elements, called artificial neurons that are connected and organized in layers (Larder et al., 2007). They are capable of generating models for the detection of non linear functions(..). Their algorithms are extensively applied in biology and medicine to solve complex problems, more specifically for prediction or classification of solutions or to refine methodological aspects. (Florence and Balasubramanie, 2010). Human immunodeficiency virus (HIV) is a retrovirus that can lead to acquired immunodeficiency syndrome (AIDS). (Kim et al., 2010). It is a disease in which the body immune system weakens progressively, …show more content…
Artificial neural networks have been used extensively as a complimentary bioinformatics tool to make approximations of the cleavage site activity and specificity. First uses of ANNs to solve the problem The aim of first research study was to develop a classification model that, given a sequence of eight amino acids, could discriminate between sequences which are either cleavable or uncleavable by the HIV- 1 protease. (Kim et al., 2010). The neural learning algorithms used most frequently was back-propagation neural networks (BPNNs) (Thomson et al., 2003) because it performs well on prediction problems. (Sibanda and Pretorius, 2012). When BBNN was used for the prediction of the HIV-1 protease cleavage site, it gave a prediction accuracy 92%(Thomson et al., 2010) However, one of the major disadvantages of using ANNs to analyse biological data referred to the impossibility of most ANNs of recognizing non- numerical features like amino acids. Hence an encoding process to model the amino acids was preferable. (Thomson et al., 2003) The advantage of the Bio basic functional neural networks The peculiarity of this algorithm relates to its ability to recognise amino acids directly. Thus, avoiding the use of 20 binary bits to represent each amino acid is advantageous. (2003) The prediction accuracy of BBFNN was proved in a research study using 362 HIV protease sequences, where 114 were with cleavage sites and
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Dr. N.A.S states that one of the antiretrovirals blocks translation of RNA into the proteins required to make new viruses. Some of the current antiretrovirals include reverse transcriptase, fusion and entry, protease, and integrase inhibitors;6,10 however there is not an inhibitor that blocks translation of rna into proteins on the market. Targeting inhibitors specific to HIV has made ARVs increasingly effective and less harmful to humans.
A protein has multiple existing structures, these are the primary, secondary, tertiary and quaternary structures which occur progressively. A protein is essentially a sequence of amino acids which are bonded adjacently, and interact with one another in various ways depending on the R group that the amino acid contains. There are 20 different amino acids which are able to be arranged in any given order, thus giving rise to a potential 2.433x1018 (4.s.f) different combinations, and therefore interactions between the various amino acids.
Since we have already known the amino sequence of the protein in previous step, we can narrow down the targeting ubiquitin ligase based on existing research data such as papers, NCBI data.
The basic building blocks of proteins are amino acids, the biuret reaction tests for protein. A solution of sodium hydroxide is added to a sample then a few drops of copper sulphate solution, if positive – the solution will turn mauve. There are 20 different amino acids and they can be joined in any order. Therefore there can be many different functions. A protein consists of one or more polypeptide chains (a polypeptide chain being multiple amino acids joined together via condensation, producing a peptide bond). Different proteins have different shapes as the shapes are determined by the sequence of amino acids.
I propose to use computational techniques to determine which drug treatment for epilepsy is best by comparing the structure of each drug with the active site of the affected enzymes. The purpose of this computational query is to determine which drug facilitates the best interaction with the active site of the enzymes and prevents epileptic seizures. By visualizing the structure of the molecules and active sites of the enzymes with the use of Spartan’14, the 2D and 3D shape how these molecules will interact with the active sites of the enzymes can be determined. The drug that is considered to be the best will be characterized as having the highest affinity for the active site of the enzyme, which can be determined by the finding the drug with the molecular shape that is closest in shape to the active site of the enzyme according to the lock and key model of enzyme interactions. This information will allow doctors to have more knowledge on which drugs are better to prescribe to epileptic patients, but it will also be useful to drug developers so that they can design drugs with similar structures in order to treat epilepsy.
With an algorithm we can locate the motif much faster to analyze it. Once the motif is found,
For the second part of the experiment, one had to use the knowledge learn from viewing protein molecules in FirstGlance in Jmol to analyze the protein PDB ID: 4EEY. The analysis of this protein was done using the RSCB protein data bank (PDB) at (http://www.rcsb.org/pdb/home/home.do).2
It seems these days that the building blocks of proteins, affectionately known as "amino acids", are tiny little gold nuggets that bestow superhuman powers upon anyone lucky enough to stumble upon them in a sports gel, capsule, fizzy drink or cocktail. After all, these little guys are starting to get put by nutrition supplement manufacturers into just about everything, from your engineered pre-workout snack, to your during workout beverage, to your post-workout smoothie mix.
Polyphen2.0 (Polymorphism Phenotyping) a multi-sequence alignment server predicts the functional impact of an amino acid substitution. The prediction is a straightforward empirical rule which is automatically applied to the structural, sequence, and phylogenetic information, and readily characterizes the amino acid substitution (Ramensky et al. 2002). To determine the effect of variants on the protein secondary structure, inter chain contents, functional sites, and binding sites, Polyphen2.0 utilizes PDB (Protein Data Bank), DSSP (Dictionary of secondary structures in protein), and three-dimensional structure databases (Ramensky et al. 2002). We submitted the query in the form of the protein sequence with the mutational position of two amino
In most instances, protein molecules are usually embedded from hundreds to thousands of amino acids. A repertoire of twenty different amino acids, joined in any possible sequence allows the existence of an inconceivably large number of proteins that is infinite in nature.
When analyzing the secondary structure predictions of the query sequence from the five prediction programs, the majority of the programs seemed to detect the presence of one long, uninterrupted helix on the N-terminal side of the pilE protein. However, some programs, such as PORTER (Pollastri et al., 2005) and SSpro (Magnan et al., 2014) indicated the presence of two helices interrupted by a small gap of random coils. While these results appear to contradict each other at first, upon further inspection, this can be explained by the fact that when two helices are very close to each other, some algorithms may erroneously pick it up as 1 long helix since secondary prediction programs are not 100% accurate. Thus, in the consensus secondary
Cytotoxic (CD8+) T cell epitopes as MHC class I ligand were predicted using NetCTL 1.2 , a server based on neural network architecture, for each of the 12 HLA class I super types defined by Lund et al. in  (A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58, B62). Previously selected FASTA sequence of the polymerase protein was submitted to that server with the parameter of threshold level set at 0.5 to have sensitivity and specificity of 0.89 and 0.94 respectively. This would be helpful for finding more epitopes effectively. An integrated value of transporter of antigenic peptide (TAP) transport efficiency, MHC-I binding and proteosomal cleavage efficiency