All work described on this wiki or on our parts registry pages was done by iGEM Team XMU Software 2013. We managed to finish the whole project, from planning, financing to the complete dry and wet lab work by ourselves. Nevertheless we could not have done all this work without the help, advice and guidance of several people. Therefore, special thanks to the following people:

1. Team XMU China, the wet lab team of our university, gave us a hand to construction work.

2. Tina Zhang, the advisor of team XMU China, helped us in experiments, especially in site-specific mutagenesis and PCR.

3. Prof. Zhiliang Ji, College of Life Science, provided us with valuable guidance in choosing the project.

4. Prof. Shoufa Han, provided us with many instruments such as ELISA reader to test the fluorescence.

5. Qiang Kou, the previous team leader of SYSU-Software team, gives us lots of help.

6. Wenjun Rao, a shy boy from XMU Software College, build the beautiful interfaces for Brick Worker.

Abstract

In a promoter sequences, the sigma factor binding site and other transcription factor binding site affect the strength of binding significantly. For annotating promoters, some software was developed which mostly focused on the prediction of other transcription factors or one particular type of sigma factors but failed to analyze the promoter with both sigma factors and other transcription factors. ^1-2 To solute this problem, a module of our software was designed which can analyze and evaluate promoters.

Our software use PWM method to calculate the similarity between promoter sequencess and the position frequency matrix of transcription factor binding sites (TFBS) to locate the TFBS as well as to predict the relative strength of the promoter. Promoter-Decoder overshadows its counterparts with all-round analysis and the prediction of promoter strength. It enables users to figure out promoter types, predict promoter strength, changeit by mutating the key sites and even change the property of certain promoter by adding new TFBS to the promoter sequences.

Background

Sigma Factors

Bacteria encode several thousands of different proteins, which are necessary for normal cell functions or for adaptation to environmental changes.³ These proteins are not required at the same time or in the same amount. Regulation of gene expression therefore enables the cell to control the production of proteins needed for its life cycle or for adaptation to extracellular changes. The various steps during transcription and translation are therefore subject to different regulatory mechanisms.⁴

The most prominent step in gene regulation is the initiation of transcription in which the DNA-dependent RNA polymerase (RNAP) is the key enzyme. The RNAP or the RNAP core enzyme is the catalytic machinery for the synthesis of RNA from a DNA template. However, RNAP cannot initiate transcription by itself. Initiation of transcription requires an additional polypeptide known as a sigma-factor.⁵ Sigma-factors are a family of relatively small proteins that can associate in a reversible way with the RNAP core enzyme. Together, the sigma-factor and the RNAP core enzyme form an initiation-specific enzyme, the RNAP holoenzyme.

The sigma-factor directs RNA polymerase to a specific class of promoter sequencess. Most bacterial species synthesize several different sigma-factors that recognize different consensus sequencess.⁶

This variety in sigma-factors provides bacteria with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to altered environmental or developmental signals.

The frequency at which the RNAP holoenzyme initiates transcription, also known as the strength of a promoter, is influenced by the promoter sequences and the conformation of the DNA in the promoter region. The sigma-factors recognize two conserved sequencess in the promoter region, known as the promoter consensus sequences. Sigma-factors or fragments of sigma-factors bind specifically to promoter DNA sequences and by specific base pair and amino acid substitutions in the promoter consensus sequencess or sigma factors. Most bacterial species synthesize several different sigma-factors which direct the RNAP holoenzyme to distinct classes of promoters with a different consensus sequences. This variety in sigma-factors provides the bacterium with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to specific environmental stimuli.

The identification of bacterial promoters is an essential step in the elucidation of gene regulation.⁷

As a general rule, the more complex the life-cycle and environmental niche of a bacterium, the greater the number of sigma factors with corresponding promoter types. Typically however, the most common promoter type is that which regulates the housekeeping genes and the corresponding major sigma-factor is shared by all bacteria (sigma 70 in the well studied E. coli, and its homologues in other species). The binding site for the sigma70-family of promoters is defined by two consensus hexamers, TTGACA and TATAAT, located at approximately −35 and −10, respectively relative to the transcript start site (TSS) and spaced 15–21 base pairs (bp) apart2. RNA polymerase core enzyme associates with the major sigma-factor to form the holoenzyme which in turn binds to its cognate promoters to initiate transcription.

In prokaryotes, the minimum requirement for RNA polymerase binding is recognition of the promoter by the sigma factor. In general, prokaryotic RNA polymerases can interchange a number of sigma factors which bind and initiate different groups of genes.³

Transcription Factors

Sigma factors are essential for the transcription initiation in E. coli.¹⁰

In addition, promoter strengths are not determined purely by the binding of the sigma factor. Other transcription factors can bind specific sequencess surrounding or overlapping the promoter to either activate or repress transcription.⁴ The mechanism is transcriptional activators and repressors contribute to and detract from the accessibility of DNA by the RNA polymerase. ¹²

These transcription-regulating nuclear proteins bind to specific binding sites in the regulatory regions (e.g. promoters, enhancers) of the genes thus providing their activation or repression.

Computational methods of predicting TF binding sites in DNA are very important for understanding the molecular mechanisms of gene regulation.

The binding sites of the same transcription factor show a significant sequences conservation, which is often summarized as a short (5–20 bases long) common pattern called a transcription factor binding site (TFBS) or binding consensus. Our software aims to figure out the possible TFBS in promoters and precisely locate the TFBS so that the user may know the exact sites that play a role in regulating the transcription.

In prokaryotes (lower organisms without nuclei), there are fewer TFs, their motifs tend to be relatively long and the strength of regulation for a particular gene often depends on how closely a particular site matches the consensus for the motif. The more mismatches to the consensus in a binding site, the less often the TF will bind and therefore the less control it will exert on the target gene. So our software will calculate the similarity between the possible TFBS in the promoter and the standard motifs so the user will know to which extent the transcription factor will control the promoter transcription

Primer Design

To facilitate the design of PCR primers of various promoters, we've developed an additional function, namely, primer design in this part of our program. After inputting the promoter sequences, the software will figure out the most suitable primers based on the theory of Thomas Kämpke¹, Markus Kieninger, and Michael Mecklenburg.¹³

Data Source

RegulonDB

Genes and operons that are under control of the same TF are members of that TF's regulon. Although methods for the prediction of regulons have been substantially improved, they are still far from perfect.

Comparative genomics tools can be used to predict regulons in bacterial genomes but the procedure can lead to incorrect regulon calling. Despite this drawback, several regulon databases are available that are based on comparative genomics methods and lack experimental evidence.

Probably the extended and accurate databases of regulons for E. coli are RegulonDB which provides the data source for our program.

Algorithm

Experimental results show that these are the strongest promoters that have been characterized in vitro so far and confirm the hypothesis that the consensus promoter sequences is "best". To calculate the similarity between the promoter sequences and the best sequences, we implement the PWM method⁶ in our program.

PWM (Position Weight Matrix)

Molecular techniques for the identification of promoters are both costly and time consuming, hence in silico methods are an attractive and well explored alternative. The most common in silico method to identify sigma 70 promoters uses position weight matrices (PWMs) and depends on the relative conservation of the transcription factor binding site (TFBS, or motifs ).

The algorithm can be divided into two parts regarding to the difference between the motifs of sigma factors and other transcription factors.

Part 1: the recognition of other transcription factors.⁷

Other transcription factors are proteins that can bind to a specific DNA sequences (motifs) and regulate the promoter's transcription. To recognize these possible motifs in a given promoter sequences, we calculate the Matrix Similarity Score (MSS) of every possible sites in the promoter sequences using the position frequency matrix of 86 transcription factors published by RegulonDB. The algorithm reports only those matches of a matrix that have got MSS higher than the settled threshold. And MSS for a subsequences x of the length L is calculated in following steps:

f_i,Bi , frequency of nucleotide B to occur at the position i of the matrix (B ∈{A, T, G, C})

f _i^min , frequency of the nucleotide which is rarest in position i in the matrix

f _i^max , highest frequency in position i.

The information vector

describes the conservation of the positions i in a matrix. Multiplication of the frequencies with the information vector leads to a higher acceptance of mismatches in less conserved regions, whereas mismatches in highly conserved regions are very much discouraged. This leads to a better performance in recognition of TF binding sites if compared with methods that do not use the information vector.

To determine the best threshold of the motif finding algorithm, we test various threshold values and analyze the true negative and false positive rate of each threshold value. The ideal threshold is supposed to have both the least true negative and false positive rates.

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Table 1 The threshold setting data

Threshold

0.5977

0.598

0.69

0.7