The Central Dogma Of Molecular Biology Biology Essay

The molecule we know today as deoxyribonucleic acid was initially seen in 1869 by Swiss biologist Friedrich Miescher, who stumbled upon a substance which was resistant to protein digestion. At that time he referred to the molecule as ``nuclein'' \citePray2008. Though Miescher remained in obscurity, Russian biochemist Phoebus Levene extended work with it and in 1919 uncovered the three major components of a nucleotide: phosphate, sugars, and foundation. He mentioned that the sweets element was ribose for RNA and deoxyribose for DNA, and he suggested that nucleotides were made up of a chain of nucleic acids \citeLevene1919. He was basically accurate, and in 1950 Erwin Chargaff, after reading a paper by Oswald Avery in which Avery recognized the gene as the machine of hereditary material \citeAvery1944, attempt to discover whether the deoxyribonucleic acid molecule differed among types. He found that although, as opposed to Levene's proposal that nucleotides are always repeated in the same order, nucleotides appear in different orders in different microorganisms, these molecules preserved certain characteristics. This led him to develop a set of guidelines (known as ``Chargaff's Guidelines'') where he declares that the total number of purines (Adenine and Guanine) and the total quantity of pyrimidines (Cytosine and Thymine) are nearly always equal within an organism's genetic materials. In 1952 Rosalind Franklin and Maurice Wilkins used X-ray crystallography to fully capture the first image of the molecule's shape, and in 1953 Wayne Watson and Francis Crick finally proposed the three dimensional model for DNA \citeWatson1953. The four main tenants with their discovery still carry true today: 1) DNA is a double-stranded helix, 2) nearly all these helices are right-handed, 3) the helices are anti-parallel, and 4) the DNA base pairs within the helix are joined by hydrogen bonding, and the bases can hydrogen connection with other molecules such as protein.

The Central Dogma of Molecular Biology, first suggested by Francis Crick \citeCrick1958, explains the directional operations of transformation from DNA to RNA and from RNA to protein. This gene expression process starts off with DNA, a double-stranded molecule comprising base-paired nucleic acids adenine (A), cytosine (C), guanine (G), and thymine (T) over a sugar-phosphate backbone. This hereditary material acts as the \emphinformation storage for life, a dictionary of kinds that provides all the necessary tools for an organism to generate the components of itself. During the process of \emphtranscription, the DNA molecule is employed to make messenger RNA (mRNA), which posesses \emphspecific instance of the DNA instructions to the machinery that will make protein. Protein are synthesized during \emphtranslation using the mRNA molecule as helpful information. Gene manifestation is a deterministic process during which each molecule is produced using the merchandise of the prior step. The outcome is a transformation from the genetic code into an operating unit that can be used to perform the task of the cell. As you can imagine, this process must be managed by an organism to make effective use of resources, respond to environmental changes, and differentiate cells within the body. Gene regulation, as it may also be called, occurs by any means stages along the way from DNA to protein.

Regulation falls into four categories: 1) epigenetic (methylation of DNA or necessary protein, acetylation), 2) transcriptional (will involve protein called transcription factors), 3) post-transcriptional (sequestration of RNA, substitute splicing of mRNA, microRNA (miRNA) and small interfering RNA (siRNA)), and 4) post-translational modification (phosphorylation, acetylation, methylation, ubiquitination, etc. of health proteins products). Epigenetic regulation of DNA entails a reversible, heritable change that will not alter the collection itself. DNA methylation occurs on the nucleic acid cytosine. Arginine and lysine are the most commonly methylated proteins. When proteins called \emphhistones contain certain methylated residues, these proteins can repress or switch on gene expression. Often this occurs on the transcriptional level, and therefore avoids the cell from processing messenger RNA (mRNA), the precursor to proteins. Proteins tend to be known as the workhorse of the cell and are in charge of from catalyzing chemical substance reactions to providing the building blocks for skeletal muscles. Some protein, called \emphtranscription factors, help to up- or down-regulate gene manifestation levels. These proteins can act alone or together with other transcription factors and bind to DNA bases near gene coding areas.

This is an over-all schema for gene appearance. DNA is a double-stranded molecule comprising base-paired nucleic acids A, C, G, and T on the sugar-phosphate backbone and can be used as information storage. mRNA is made during transcription and carries a specific instance of the DNA instructions to the equipment that will make the protein. Protein are synthesized during translation using the information in mRNA as a template. This is a deterministic process where each molecule is made using the product of the prior step. mRNA requires a 5' cap and a 3' poly(A) tail in order to be exported out of the nucleus. The cap is critical for popularity by the ribosome and cover from enzymes called RNases that will breakdown the molecule. The poly(A) tail and the proteins bound to it aid in safeguarding mRNA from degradation by other enzymes called exonucleases.

What can be gained by studying gene regulation? Generally, it allows us to understand how an organism evolves and produces, both on an area scale \citeChoe2006, Wilson2008, and on a more global network level. You will discover, however, more specific reasons to investigate this process more closely. Failing in gene regulation has been shown to be a main factor in disease \citeStranger2007. Additionally, learning how to interrupt gene regulation can lead to the introduction of drugs to deal with bacteria and trojans \citeMcCauley2008. A clearer knowledge of this process in microorganisms may lead to possible solutions to the challenge of antimicrobial amount of resistance \citeCourvalin2005.

There are two major factors that motivate the studies herein. Firstly, the size and quality of biological data sets has increased drastically within the last several years. This is due to high-throughput experimental techniques and technology, both which have provided large amounts of interaction data, along with X-ray crystallography and nuclear magnetic resonance (NMR) tests which have given us the solved three-dimensional composition of proteins. Secondly, machine learning has become an extremely popular tool in bioinformatics research because it allows for more acoustics gene and health proteins annotation without relying entirely on collection similarity. In case a collection of characteristics which separate between two classes of proteins can be assembled, function can be forecasted.

In this work we target mainly on regulation at the transcriptional level and the components which play a commanding role in this operation. So-called nucleic acid-binding (NA-binding) proteins, which include transcription factors, are involved in this and a great many other cellular procedures. Disruption or malfunction of transcriptional legislation may cause disease. We identify these protein from representative data models such as many categories of proteins. Additionally, to be able to comprehend the actual mechanisms, we anticipate the specific residues involved with nucleic acid binding using machine learning algorithms. Id of these residues can provide sensible assistance in the efficient annotation of NA-binding proteins. These predictions can also be used to expedite mutagenesis tests, guiding researchers to the right binding residues in these proteins.

Toward the ultimate goal of attaining a deeper knowledge of how nucleic acid-binding proteins facilitate the rules of gene expression within the cell, the study described here focuses on three particular areas of this problem. We get started by examining the nucleic acid-binding protein themselves, both on the proteins and residue levels. Next, we convert our attention toward necessary protein binding sites on DNA molecules and a particular type of modification of DNA that make a difference health proteins binding. We then have a global perspective and study real human molecular networks in the framework of disease, focusing on regulatory and protein-protein connection networks. We verify the amount of partnership interactions between transcription factors and how it scales with the amount of target genes controlled. In several model microorganisms, we find that the syndication of the amount of partners vs. the amount of target genes appears to follow an exponential saturation curve. We also find our generative transcriptional network model employs a similar distribution in this evaluation. We show that cancers- and other disease-related genes preferentially occupy particular positions in conserved motifs and discover that more ubiquitously expressed disease genes have more disease organizations. We also forecast disease genes in the protein-protein interaction network with 79\% area under the ROC curve (AUC) using ADTree, which identifies important qualities for prediction such as degree and disease neighbor percentage. Finally, we make a co-occurrence matrix for 1854 diseases predicated on shared gene uniqueness and find both previously known and probably undiscovered disease interactions.

The goal for this project is to predict nucleic acid-binding on both protein and residue levels using machine learning. Both sequence- and structure-based features are used to tell apart nucleic acid-binding protein from non-binding proteins, and nucleic acid-binding residues from non-binding residues. A novel software of a costing algorithm is utilized for residue-level binding prediction to be able to accomplish high, balanced correctness when working with imbalanced data collections.

During the past few decades, the quantity of biological data designed for analysis has grown exponentially. Additionally huge amount of information comes the challenge to seem sensible of it all. One subject of immediate matter to us as humans is health and disease. Why do we get suffering, and how? Where do our bodies fail on a molecular level in order for this to occur? How are diseases related to one another, and do they have got similar settings of action? These questions will demand many researchers from multiple disciplines to answer, but where do we start? We take a bioinformatics methodology and examine disease genes in a network framework. In this chapter we analyze individuals disease and its own relationship to two molecular networks. First, we find conserved motifs in the individual transcription factor network and identify the location of disease- and cancer-related genes within these buildings. We find that both tumor and disease genes take up certain positions more often. Next, we study the individuals protein-protein discussion (PPI) network as it pertains to disease. We find that people are able to anticipate disease genes with 79\% AUC using ADTree with 10 topological features. Also, we find that a mixture of several network characteristics including level centrality and disease neighbor percentage help separate between these two classes. Furthermore, an alternating decision tree (ADTree) classifier allows us to see which combinations of highly predictive attributes contribute most to protein-disease classification. Finally, we build a matrix of diseases based on shared genes. Instead of using the raw count of genes, we use a \emphuniqueness score for every single disease gene that pertains to the amount of diseases with which a gene is engaged. We show several interesting types of disease relationships for which there is some clinical information and some for which the information is lacking. We believe this matrix will be useful to find relationships between diseases with very different phenotypes, or for those disease relationships which might not exactly be obvious. It might also be helpful in determining new potential medication targets through drug repositioning.

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