Team:XMU Software

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<div class="team_introduce">XMU-Software-2013 team consists of 11team members, 2 instructors and 3 advisors. This extraordinarily energetic and creative team is tightly connected by trust, collaboration and affection among its members.</div>

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<span class="body_title">Prof. Bai-shan Fang</span></br></br>

<span class="body_text">Dr. Bai-shan Fang is the professor in College of Chemistry &amp; Chemical Engineering at Xiamen University. In the group of biocatalysis and biotransformation, his research mainly focuses on synthetic biology, mining and the transformation of the enzyme, construction of bio-molecular machines, the application of new biocatalysts. His major role of XMU iGEM team is to mentor for all and to enlighten the members. </br> </br></br> </br></br> </br></br></span>

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<span class="body_title">Associate Prof. I-Son Ng</span></br></br>

<span class="body_text">Dr. I-Son Ng is the associate professor in College of Chemistry &amp; Chemical Engineering at Xiamen University. Her research interests are biofuel, engineering of enzyme and protein, zymology, genetic engineering, biochemical separation procedures and proteomics. Her role for the project is to provide suggestions and instruments.</br> </br></br> </br></br> </br></br></span>

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<div class="project_introduce">Our project includes 2 independent software tools-the brick worker and E’NOTE. The former is a software suit for the evaluation and optimization of biobricks, i.e., promoter, RBS, protein coding sequence and terminator. E’NOTE is a web application serveing as an assistant for experiments. It’s useful functions such as experiments recording and experimental template customization make experimental process easier and more enjoyable. To learn more about our project, please visit Project page.</div>

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<a id="project_navi1" class="navi1" href="#_promoter"         onClick="project_promoter();" onmouseover="navi1();" onmouseout="navi11();">Promoter-decoder</a>

<a id="project_navi2" class="navi2" href="#_rbs"               onClick="project_rbs();"       onmouseover="navi2();" onmouseout="navi21();">RBS-decoder</a>

<a id="project_navi3" class="navi3" href="#_terminator"        onClick="project_terminator();"onmouseover="navi3();" onmouseout="navi31();">Terminator</a>

<a id="project_navi4" class="navi4" href="#_protein"           onClick="project_protein();"   onmouseover="navi4();" onmouseout="navi41();">SynoProteiner</a>

<a id="project_navi5" class="navi5" href="#"                   onClick="project_enote();"     onmouseover="navi5();" onmouseout="navi51();">E' Note</a>

<a id="project_navi6" class="navi6" href="javascript:void(0)"                                 onmouseover="navi6();" onmouseout="navi61();">Back To Top</a>

<li><div onClick="project_promoter();"><a href="#">Promoter-decoder</a></div></li>

<li><div ><a href="#">RBS-decoder</a></div></li>

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<div class="body_text">Gene  expression  in  both  prokaryotes  and  eukaryotes  is  frequently  controlled  at  the  level  of  transcription.  This  process  can  be represented  as a cycle  consisting  of  four  major  steps:  (1) promoter binding; (2) RNA  chain  initiation;  (3) RNA  chain elongation; and  (4) termination. Since regulatory controls are exerted at each step, an understanding of the mechanism of each step is of general importance in understanding gene expression.</div>

<div class="body_heading">Introduction</div>

<div class="body_text">Termination, the last step of the transcription cycle, occurs when the RNA polymerase releases the RNA transcript and dissociates from the DNA template. It is important that transcription is imperfectly terminated at some terminator so that the ratio of the amount of the mRNA transcribed from upstream and that from downstream of the terminator is controlled. This regulation is quantified by the termination efficiency (%T),</div>

<div class="body_text">Two mechanisms of transcription termination and two classes of termination signals have been described in bacteria: rho-dependent and rho-independent. </div>

<div class="body_text">Rho-independent (also known as intrinsic) terminators are sequence motifs found in many prokaryotes that cause the transcription of DNA to RNA to stop. These termination signals typically consist of a short, often GC-rich hairpin followed by a sequence enriched in thymine residues.</div>

<div class="body_text">The conventional model of transcriptional termination is that the stem loop causes RNA polymerase to pause and transcription of the poly-A tail causes the RNA: DNA duplex to unwind and dissociate from RNA polymerase.</div>

<div class="body_heading">Algorithm</div>

<div class="body_text">In 2011, iGEM team SUSTC-Shenzhen-B developed a software tool TTEC to predict terminator efficiency. It takes DNA sequences as input and returns the terminator efficiency value.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/c/c6/Xmusoftware_Ter1.jpg"></div>

<div class="body_text">In the algorithm, it takes 3steps to calculate the terminator efficiency:</div>

<div class="body_text">1. Use RNA folding algorithm to predict the secondary structure of terminator and and recognize A tail, stemloop and T tail.</div>

<div class="body_text">3. From the score, we predict the terminator efficiency based on the score-terminator equation.</div>

<div class="body_text">The prediction of secondary and recognition of A tail, stemloop and T tail are achieved by Kings ford scoring system. </div>

<div class="body_heading">Kingsford Scoring System</div>

<div class="body_text">In 2007, Carleton L Kingsford et al described TransTermHP1, a new computational method to rapidly and accurately detect Rho-independent transcription terminators. </div>

<div class="body_text">They put forward an algorithm to predict Rho-independent terminators.The first 15 bases of the potential tail sequence are scored using a function:</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/f/f5/Xmusoftware_Ter_formula1.jpg"></div>

<div class="body_text">where</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/3/35/Xmusoftware_Ter_formula2.jpg"></div>

<div class="body_text">for n=1...15 and =1.</div>

<div class="body_text">The energy of potential hairpin configurations adjacent to a reference position can be found efficiently with a dynamic programming algorithm. The table entry hairpin_score[i,j] gives the cost of the best hairpin structure for which the base of the 5' stem is at nucleotide position i and the base of the 3' stem is at position j. The entry hairpin_score[i,j] can be computed recursively as follows:</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/8/85/Xmusoftware_Ter_formula3.jpg"></div>

<div class="body_text">The function energy(i,j) gives the cost of pairing the nucleotide at i with that at j, and loop_pen(n) gives the cost of a hairpin loop of length n. The hairpin's loop is forced to have a length between 3 and 13 nt, inclusive, by setting loop_pen(n) to a large constant for any n outside that range. The constant 'gap' gives the cost of not pairing a base with some base on the opposite stem and thus introducing a gap on one side of the hairpin stem.</div>

<div class="body_text">Table 1</div>

<div class="body_text">Parameters used to evaluate hairpins</div>

<div class="body_text">Pairing Energy</div>

<div class="body_text">G-C -2.3</div>

<div class="body_text">A-T -0.9</div>

<div class="body_text">G-T 1.3</div>

<div class="body_text">Mismatch 3.5</div>

<div class="body_text">Loop_pen(n) 1•(n - 2)</div>

<div class="body_text">Parameters used to evaluate the energy of a potential hairpin where n is the length of the hairpin loop</div>

<div class="body_text">For an RNA sequence, we number it from 5’ to 3’ . If i &lt; j and nucleotides ri and rj form a base pair,we denote it by i.j. We call base ri’ or base pair i’.j’ is accessible from i.j if i &lt;i’ ( &lt;j’ ) &lt;j and if there is no other base pair k.l so that i &lt;k &lt;i’ ( &lt;j’ ) &lt;l &lt;j. We denote the collection of base and base pair accessible from i.j by L(i,j). Then i.j is the closing base pair. Here “L” means loop.</div>

<div class="body_text">ii. n-loop</div>

<div class="body_text">If the loop contain n – 1 base pairs, we denote it by n-loop. (Because there is a closing base pair, so we denote it by n-loop even though the closing base pair is not included in the loop.)</div>

<div class="body_text">1-loop : Hairpin loop(size of loop shouldn’t be smaller than 3) </div>

<div class="body_text">2-loop : Interior Loop(right strand size and left strand size are both bigger than 0.)</div>

<div class="body_text">Buldge(Size of one strand is bigger than 0 and that of another strand is 0.)Stack(size of the loop is 0.)</div>

<div class="body_title">2. Calculation of the Minimum Free Energy Change of Stemloop Formation4 Assume i.j is the closing base pair of the loop </div>

<div class="body_text">G（i,j）= min { GH ( i , j ) , GS( i , j ) + G ( i + 1 , j – 1 ) , GBI( i , j ) } ;</div>

<div class="body_text">GBI ( i , j ) = min{ gbi( i , j , k , l ) + G( k , l ) } for all 0 &lt; k – i + l – j - 2 &lt; max_size</div>

<div class="body_text">G(i,j) is the minimum free energy change of stemloop formation. GH is the free energy change to form a hairpin loop. GS is the free energy change to form a stack. GBI is to calculate the minimum free energy change of structure containing 2-loop. gbi(i,j,k,l) is the free energy change to form 2-loop.</div>

<div class="body_title">3.Calculation of T Tail Score</div>

<div class="body_text">Here we consider 15 nucleotide in the downstream of stemloop. T tail score nT is calculated as follows :</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/6/62/Xmusoftware_Ter_formula4.jpg"></div>

<div class="body_heading">Background</div>

<div class="body_text">Translational efficiency in Escherichia coli is generally determined at the stage of initiation. There are several principal mRNA sequence elements which can affect the kinetics of ternary initiation complex formation (30 S-mRNA-fMet-tRNA): the initiation codon, the Shine-Dalgarno (SD) sequence, the spacing between those two elements, and other factors.</div>

<div class="body_heading">Introduction</div>

<div class="body_text">The bacterial ribosome binds to particular sequences on an mRNA, primarily the Ribosome Binding Site (RBS) and the start codon (ATG). The RBS base-pairs with an RNA molecule that forms part of the bacterial ribosome (the 16s rRNA), while the start codon base-pairs with the initiator tRNA which is bound to the ribosome. In addition to the sequences of the RBS and the start codon being important, these two sequences need to be positioned approximately 6-7 nucleotides apart so they can both make contact with the appropriate parts of the ribosome complex1.</div>

<div class="body_text">The Shine-Dalgarno Sequence</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/a/a6/Xmusoftware_RBS1.jpg"></div>

<div class="body_text">The RBS sequence logo representing the sequences of 149 RBS from E. coli. The height of each letter represents the frequency of the base at that location. From Tom Schneider, "A Gallery of Sequence Logos".</div>

<div class="body_text">The end of the 16s rRNA that is free to bind with the mRNA includes the sequence 5′–ACCUCC–3′. The complementary sequence, 5′–GGAGGU–3′, named the Shine-Dalgarno sequence, can be found in whole or in part in many bacterial mRNA. Very roughly speaking, ribosome binding sites with purine-rich sequences (A's and G's close to the Shine-Dalgarno sequence will lead to high rates of translation initiation whereas sequences that are very different from the Shine-Dalgarno sequence will lead to low or negligible translation rates. The sequence is named after the researchers who discovered the high frequency of the sequence upstream of bacterial coding sequences . You can see how common the sequence is by looking at the RBS sequence logo on the right (where the height of a letter indicates the frequency of the letter at that location).</div>

<div class="body_heading">Algorithms</div>

<div class="body_text">As we know, the RBS strength is greatly influenced by the SD sequence, where the 16s RNA of the ribosom binds to, so the strength can be determined by the free binding energy between the SD sequence and the 16s RNA. So we designed a program calculating the free binding energy but the results show that the correlation between the free energy and the strength of RBS is rather weak (R2=0.5517). So we decide to find other algorithms for better accuracy.</div>

<div class="body_text">Inspired by the strength prediction algorithms used in promoter part, in which the similarity to the sigma factors’ PWM is interlocking with the binding affinity between the protein and DNA sequence. We obtained the Position Frequency Matrix of SD sequence of E.coli and use the PMW method (illustrated in detail in the promoter part) to calculate the similarity between the RBS sequence and the Position frequency sequence, what is different from the promoter is that, the spacer length between the SD sequence and the startcodon and the start codon itself both act as constraints in locating the SD sequence, which is confined to 3-16 bp and ATG/TTG/GTG. And similar to the prediction of promoter strength, the spacer length between the SD sequence also contribute to the RBS strength, the weight of the influence of the spacer on strength is determined by our experiments in which we either mutated the SD sequence in a RBS sequence or change the spacer length to figure out the relative influence of these two factors 2.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/f/ff/Xmusoftware_RBS2.jpg"></div>

<div class="body_text">We use the RBS sequence listed on the iGEM registry with experimentally determined relative strength, and the correlation between the RBS strength predicted by our software and the actual relative strength is strong, with a determination coefficient value 0.8039.</div>

<div class="body_photo"><img src="https://static.igem.org/mediawiki/igem.org/a/a2/Xmusoftware_RBS3.jpg"></div>

<div class="body_text">Due to scarcity of experimental data, the relative weight of the SD sequence and the spacer length used currently is roughly determined which may undermine the accuracy of RBS strength prediction. For further improvement of our program, we’ll try to obtain more reliable experimental data to accurately determine the weight used in our algorithm and hopefully elevate the accuracy of RBS-decoder.</div>

<div class="body_text">In the next version of RBS-decoder, the secondary structure of the RBS sequence will be shown on the software and we’ll also include the other species’ SD sequence data in order to analyze predict the strength of RBS.</div>

<div class="body_heading">Reference</div>

<div class="body_references">1. Ma, J.; Campbell, A.; Karlin, S., Correlations between Shine-Dalgarno sequences and gene features such as predicted expression levels and operon structures. <i>Journal of bacteriology</i> 2002,<i> 184 (20),</i> 5733-5745.</div>

<div class="body_references">2. Noguchi, H.; Taniguchi, T.; Itoh, T., MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes.<i> DNA research </i>2008,<i> 15 (6), </i>387-396.</div>

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<div class="body_text">fi,B, frequency of nucleotide B to occur at the position i of the matrix (B ∈{A, T, G, C})</div>

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<div class="hp_s2h1">Doodles of Synthetic Biology</div>