The work was jointly supported by the National Natural Science Foundation of China (grant no. 21676026), the National Key R&#38;D Program of China (grant no. 2017YFD0201400), and the China Postdoctoral Science Foundation (grant no. 2017M620643). Works in the UCLA Institute of Advancement (Suzhou) were supported by the internal grants from Jiangsu Province and Suzhou Industrial Park.

1. Construction of the plasmids expressing the rare-codon-rich marker genes
<ol>
	<li>Select a marker gene that contains an appropriate number of the common codons for the targeted amino acid. 
	NOTE: For L-leucine, the kanamycin resistance gene kanR, which contains 29 leucine codons, of which 27 are common codons, is used for the construction of the selection system13. The gfp gene, which contains 17 common codons out of 19 leucine codons, or the purple protein-encoding gene prancerpurple (ppg), which harbors 14 leucine common codons, is used for the screening system (Supplementary Table 1).</li>
	<li>Replace the common codons in the marker genes with the synonymous rare codon. For L-leucine, replace its codons in kanR, gfp, or ppg with the rare codon CTA, generating kanR-RCs, gfp-RC, or ppg-RC, respectively13 (Supplementary Table 1). 
	NOTE: The frequency of the rare codon in the marker genes will affect the stringency of the selection or screening system. In general, increasing the number of rare codons will increase the stringency of the selection or screening system. To achieve the appropriate selection or screening strength, design a series of marker genes that harbor different numbers of rare codons and compare their effects.</li>
	<li>Generate building blocks of the rare-codon-rich marker genes using tools such as GeneDesign14 (http://54.235.254.95/gd/) for gene synthesis. Alternatively, order the marker genes from commercial gene synthesis services.
	<ol>
		<li>On the GeneDesign page, choose Building block design (constant length overlap).</li>
		<li>Paste the sequences of the rare-codon-rich marker genes in the Sequence box.</li>
		<li>Define the overlap length between the assembly oligos; keep in mind that the default 40 bp works fine for most sequences. 
		NOTE: See the online manual for more instructions on the settings of the other parameters.</li>
		<li>Click the Design building blocks button and order the oligonucleotides listed on the page.</li>
	</ol>
	</li>
	<li>Synthesize the rare-codon-modified genes by polymerase chain reaction (PCR)-based accurate synthesis15.</li>
	<li>Ligate the rare-codon-rich kanR-RC to vector pET-28a, gfp-RC to pSB1C3, and ppg-RC to CPB-37-44116. 
	NOTE: The pET-28a and pSB1C3 plasmid maps are available on the SnapGene online plasmid database (Supplementary Table 1); the CPB-37-441 plasmid map is available on the ATUM chromogenic protein website.
	<ol>
		<li>On ice, add the vector and the marker fragments in a molar ratio of 1:1 to 7.5 &#956;L of assembly mix (see Table of Materials) to a total volume of 10 &#956;L. Incubate the sample at 50 &#176;C for 1 h.</li>
		<li>Transform 5 &#956;L of the assembly product into 50 &#956;L of competent cells (see the Table of Materials) at 42 &#176;C for 30 s.</li>
		<li>Recover the cells in SOC (Super optimal broth with catabolite repression) medium at 37 &#176;C for 1 h, plate them on LB (lysogeny broth) agar medium, and incubate them at 37 &#176;C overnight.</li>
		<li>Inoculate the colony into LB medium and incubate at 37 &#176;C for 8 h.</li>
		<li>Isolate the plasmid using a preferred commercial kit.</li>
	</ol>
	</li>
</ol>
2. Optimizing the selection conditions
<ol>
	<li>Make the parent strain used for mutagenesis into competent cells17.</li>
	<li>Transform 50 &#956;L of the competent cells with 1 &#956;L of plasmid that carries the wild-type kanR, and transform another set of the competent cells with the plasmid containing kanR-RC29 with all leucine codon replaced by the rare codon CTA.</li>
	<li>Add 950 &#956;L of SOC medium and incubate the sample in a shaker at 250 rpm at 37 &#176;C for 1 h.</li>
	<li>Plate 100 &#956;L of the cell culture onto the LB agar medium containing 50 &#956;g&#183;mL-1 kanamycin, and incubate it at 37 &#176;C for approximately 8 h until colonies appear.</li>
	<li>Pick the colonies that harbor the wild-type kanR and the leucine rare-codon-rich kanR-RC29 and use each to inoculate 5 mL of fivefold diluted LB medium (0.2x LB) containing 50 &#956;g&#183;mL-1 kanamycin. Incubate the samples in a shaker at 250 rpm at 37 &#176;C. 
	NOTE: The medium is crucial for the selection system. It should contain just enough carbon and nitrogen to allow expression of the antibiotic resistance protein from the wild-type gene rather than from the rare-codon-modified derivatives. In this case, the L-leucine concentration in 1x LB medium is too high to completely inhibit the protein expression from the rare-codon-rich kanR. Thus, a diluted LB medium with a dilution factor such as 5 is used to generate a clear difference in protein expressions from the wild-type and the rare-codon-rich genes.</li>
	<li>Transfer 200 &#956;L of each of the cell cultures to a 96-well plate in triplicate at defined time points (e.g., 8 h, 16 h, and 24 h). Measure the OD600 (optical density at 600 nm) using a plate reader. 
	NOTE: If a decrease in the cell OD600 cannot be detected for strains harboring the rare-codon-rich marker genes in comparison to the OD600 of the strain harboring the wild-type marker genes, try to increase the amount of rare codon in the marker genes or use a more diluted medium.</li>
	<li>Perform the amino acid feeding assay to test if the expressions of the rare-codon-rich marker genes (e.g., kanR-RC29) can be restored by increasing the concentration of the targeted amino acids. 
	NOTE: For the selection of L-leucine overproducers, L-leucine is used for feeding.
	<ol>
		<li>Inoculate the strains harboring the kanR-RC29 marker gene into 5 mL of the 0.2x LB (containing 50 &#956;g&#183;mL-1 kanamycin) with or without the supply of 1 g&#183;L-1 L-leucine. Inoculate another 5 mL of the 0.2x LB with strains that harbor the wild-type kanR as control. Incubate the samples in a shaker at 250 rpm at 37 &#176;C.</li>
		<li>Measure the OD600 for each culture at defined time points (e.g., 15 h, 17 h, 19 h, and 22 h).</li>
	</ol>
	</li>
</ol>
3. Optimizing the screening conditions
<ol>
	<li>Transform 50 &#956;L of the parent strain used for mutagenesis with 1 &#956;L of plasmid (~50 ng&#183;&#956;L-1) that carries the wild-type gfp or the wild-type ppg. Also, transform the parent strain with the rare-codon-rich derivatives of the marker genes.</li>
	<li>Add 950 &#956;L of SOC medium and incubate the sample in a shaker at 250 rpm at 37 &#176;C for 1 h.</li>
	<li>Plate 100 &#956;L of the cell culture onto the LB agar medium containing the appropriate antibiotics (25 &#956;g&#183;mL-1 chloramphenicol for the gfp plasmid carrying a cmR marker, or 50 &#956;g&#183;mL-1 kanamycin for the ppg plasmid carrying a kanR marker) and incubate overnight at 37 &#176;C.</li>
	<li>Pick one colony that harbors the wild-type gfp or ppg and one colony that harbors the corresponding rare-codon-rich derivative, and transfer them to 5 mL of the properly diluted LB medium individually. Incubate the samples in a shaker at 250 rpm at 37 &#176;C. 
	NOTE: For E. coli strains harboring the leucine rare-codon-rich gfp-RC or ppg-RC, the undiluted LB medium (1x LB) can be used to create significant differences in the expressions of the wild-type and the rare-codon-rich marker genes. Also, inducible promoters are used to drive the expressions of the screening marker genes. Begin the induction when the cells enter the exponential phase to achieve better discriminations.</li>
	<li>For fluorescence markers, transfer 200 &#956;L of each of the cell cultures into a 96-well clear-bottomed black plate in triplicate at defined time points (e.g., 2 h, 4 h, and 6 h). Measure the OD600 and the fluorescence and calculate the fluorescence intensity (the ratio of fluorescence to OD600). For chromogenic markers, measure the color development of the cell cultures. 
	NOTE: If lower fluorescence intensities cannot be detected for strains harboring the gfp-RC in comparison to that of the strains harboring the wild-type gfp, or if a lighter color cannot be detected for cells expressing the purple protein from the ppg-RC than from the wild-type gene, try to increase the number of rare codon on the marker genes or use a more diluted medium.</li>
	<li>Perform the feeding assay (see steps 2.7.1 and 2.7.2). Measure the fluorescence intensity or the color development at defined time points (e.g., 12 h, 18 h, and 24 h).</li>
</ol>
4. Identification of the amino acid overproducers
<ol>
	<li>Inoculate 100 &#956;L of the culture of the mutants into 5 mL of LB medium (2% v/v) and incubate it in a shaker at 250 rpm at 37 &#176;C until the values of OD600 reach 0.4.</li>
	<li>Make the mutants into competent cells17.</li>
	<li>Transform 50 &#956;L of the mutant cells with 1 &#956;L of the plasmid (~50 ng&#183;&#956;L-1) carrying the selection marker kanR-RC29 or the screening markers gfp-RC or ppg-RC. Add 950 &#956;L of SOC medium and rotate the sample in a 37 &#176;C shaker for 1 h.</li>
	<li>Select amino acid overproducers.
	<ol>
		<li>Centrifuge the cell culture at 4,000 x g for 5 min, discard the supernatant and add 5 mL of 0.2x LB medium containing 50 &#956;g&#183;mL-1 kanamycin. Incubate the sample in a 37 &#176;C shaker overnight.</li>
		<li>Plate the overnight culture (e.g., 100 &#956;L, depends on the cell density of the culture) onto 0.2x LB agar medium containing 50 &#956;g&#183;mL-1 kanamycin and incubate at 37 &#176;C for 12 h. 
		NOTE: The colonies developed are the candidates of the targeted amino acid overproducers and, in this case, the L-leucine overproducers.</li>
	</ol>
	</li>
	<li>Screen the amino acid overproducers.
	<ol>
		<li>Plate the appropriate number of cells (e.g., 100 &#956;L) harboring the screening marker (as described in step 4.3) onto the LB agar medium containing the appropriate antibiotic (25 &#956;g&#183;mL-1 chloramphenicol for screening with gfp-RC and 50 &#956;g&#183;mL-1 kanamycin for screening with ppg-RC) and incubate at 37 &#176;C for 8 h.</li>
		<li>Inoculate the LB medium containing the appropriate antibiotic (see step 4.5.1) with each single colony from the plate. Incubate the samples in a shaker at 250 rpm at 37 &#176;C.</li>
		<li>Measure the OD600 and the fluorescence and calculate the fluorescence intensity if gfp-RC is used. Measure the color development of the cell cultures if ppg-RC is used. Note that the strains exhibiting a higher fluorescence intensity or a deeper color than that of the parent strain are the candidate amino acid overproducers and, in this case, the L-leucine overproducers. 
		NOTE: The fluorescence-activated cell sorting is also suitable for identifying single-cell high-producers when the rare-codon-rich fluorescent protein genes are used for screening.</li>
	</ol>
	</li>
	<li>Verify the amino acid productivities of the candidate strains.
	<ol>
		<li>Inoculate 5 mL of LB medium with each of the candidate strains and let the cells grow overnight in a shaker at 250 rpm at 37 &#176;C.</li>
		<li>Harvest the cells from 1 mL of the cell culture by centrifugation at 4,000 x g for 2 min. Discard the supernatant and resuspend the pellet with 1 mL of sterile water.</li>
		<li>Inoculate 20 mL of M9 medium containing 4% glucose with 200 &#956;L of the cell suspension and incubate in a 250 mL shaker at 250 rpm at 37 &#176;C for 24 h.</li>
		<li>Centrifuge 1 mL of the culture medium at 4,000 x g for 5 min. Transfer 200 &#956;L of the supernatant to a clean 1.5 mL tube. Prepare L-leucine solutions (HPLC (high-performance liquid chromatography) grade) of 0.01, 0.05, 0.1, 0.5, and 1 g&#183;L-1 as standards.</li>
		<li>Add 100 &#956;L of 1 mM triethylamine and 100 &#956;L of 1 M phenyl isothiocyanate to the supernatant and the standards, mix them gently, and incubate them at room temperature for 1 h18. 
		CAUTION: Triethylamine and phenyl isothiocyanate can cause severe skin burns and eye damage and is harmful if inhaled. Wear gloves and a mask and, if possible, perform this step in a fume hood.</li>
		<li>Add 400 &#956;L of n-hexane to the same tube and vortex it for 10 s. Filter the lower phase containing the amino acid derivatives through a 0.2 &#956;m polytetrafluoroethylene membrane. 
		CAUTION: n-hexane can cause skin irritation. Wear gloves and protective clothing. If it comes in contact with skin, rinse the skin with plenty of water.</li>
		<li>Prepare mobile phase A by mixing 0.1 M sodium acetate (pH 6.5) and acetonitrile in a 99.3:0.7 volumetric ratio. Prepare acetonitrile (80% v/v) as mobile phase B. Filter all mobile phases through 0.2 &#956;m polytetrafluoroethylene membranes. 
		CAUTION: Acetonitrile is harmful if inhaled and can cause skin and eye irritations. Wear gloves and protective clothing and perform this step in a fume hood.</li>
		<li>Run 1 &#956;L of the sample on an ultra-HPLC equipped with a C18 column according to the elution program in Table 1 with a flow rate of 0.42 mL&#183;min-1 and a column temperature of 40 &#176;C. Detect the targeted amino acids at 254 nm with a diode array detector and calculate their concentrations by mapping the peak areas to the standard curve.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>Mobile phase A (%)</td>
			<td>Mobile phase B (%)</td>
		</tr>
		<tr>
			<td>0</td>
			<td>98</td>
			<td>2</td>
		</tr>
		<tr>
			<td>3.5</td>
			<td>70</td>
			<td>30</td>
		</tr>
		<tr>
			<td>7</td>
			<td>43</td>
			<td>57</td>
		</tr>
		<tr>
			<td>7.1</td>
			<td>0</td>
			<td>100</td>
		</tr>
		<tr>
			<td>11</td>
			<td>98</td>
			<td>2</td>
		</tr>
	</tbody>
</table>
Table 1: Elution program for the quantification of amino acids.

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</ol>

The authors have nothing to disclose.

The number of rare codons in the marker genes and the selection or screening medium are critical to inhibit protein expressions from the rare-codon-modified marker genes. If no significant difference can be detected between protein expressions from the wild-type marker genes and their derivatives, increasing the number of rare codons or using a nutrient-limited medium may amplify the differences. However, if the inhibition effect is too strong, the protein expressions may not be recovered even by extra feeding of the corresponding amino acids. In this case, the number of rare codons in the marker genes should be reduced to relieve part of the stress. Another way to fine-tune the selection or screening stringency is to adjust the copy numbers and the expression levels of the rare-codon-rich marker genes. Decreasing the copy number and the expression levels of the marker genes usually leads to stronger differentiations between the amino acid overproducers and the initial strains. Therefore, vectors containing the low copy number replication origins such as p15A or pSC101, as well as weak promoters, should be used. If the marker gene is driven by an inducible promoter, low induction is recommended.
The rare-codon-based strategy for the selection or screening for amino acid overproducers is a reverse adaptation of the commonly used strategy of &#8220;codon optimization&#8221;, which aims at facilitating the expressions of exogenous proteins. In codon optimization, the rare codons on the targeted genes are replaced by the synonymous common ones with respect to the host; thus, the genes from other organisms could be translated much more rapidly into proteins than those exogenous genes with high proportions of rare codons19. Therefore, it is reasonable to assume that the &#8220;reverse optimization&#8221;, which switches the common codons to their synonymous rare ones, should inhibit gene expressions. However, the gene expressions should be restored by enhanced charging of the corresponding rare tRNAs when the targeted amino acids accumulate intracellularly. The incorporation of rare codons increases the threshold of the amino acid concentration in protein expressions, which offers a potential strategy to select or screen for amino acid overproducers when combined with the proper marker genes.
Besides the antibiotic resistance genes, the fluorescent protein genes, and the chromogenic protein genes used in the protocol, various marker genes could be employed to establish the rare-codon-based selection or screening system. For instance, lethal genes such as tolC20 and sacB21 could be used to select amino acid overproducers. In this case, common codons on the genes that belong to the antidote system should be replaced by the synonymous rare codons of the targeted amino acids. Strains that overproduce the targeted amino acids are able to launch the antidote system and, thus, survive the toxic effects induced by the lethal genes.
It should be noted that side effects may occur when using high amounts of amino acids in the feeding assay. This is because some amino acids are toxic to the microorganisms. For instance, a concentration of around 100 mg&#183;L-1 for L-serine is able to inhibit the growth of E. coli22. However, although lower than that of the wild-type gene, we found that feeding up to 2 g&#183;L-1 L-serine could still restore the expressions of antibiotic resistance genes that rich in serine rare codon13. Therefore, the amino acid toxicity, at least for L-serine, would not jeopardize the reliability of the feeding assay. To overcome the potential negative effects of amino acid toxicity on the productivities of the targeted strains, strategies such as random mutagenesis and the enhancement of amino acid exportations23 could be applied. In fact, the rare-codon-based method is suitable for identifying tolerant strains capable of withstanding or overproducing amino acids above the toxic levels. The key mutations that confer amino acid tolerance could be identified and introduced into the targeted strains, which would be the ideal hosts for the constructions of amino acid overproducers.
The rare-codon-based selection or screening system ensures high fidelity. In other words, strains identified by the system are supposed to be the overproducers of the targeted amino acids. However, in some cases, the candidates that survive the antibiotic selection cannot produce higher amounts of the targeted amino acids than the parent strain. This could be attributed to the antibiotic resistance acquired by the strains through mutagenesis and then a loss of the selection plasmid24. As a consequence, strains without enhanced amino acid productivities could survive the antibiotic stress and escape the selection. These false positive strains could be eliminated by inserting another selection marker into the selection plasmid, such as a wild-type gene that confers resistance to another antibiotic. Strains that lost the selection plasmid are less likely to obtain dual resistance to the two antibiotics and will be eliminated during selection.
Mutants identified by the rare-codon-based system should be able to overproduce the targeted amino acids in comparison to the initial strains. However, the amino acid productivities for the selected strains may still be lower than the industrial requirements. This does not suggest a failure of the rare-codon-based strategy as the strain performances are independent of the selection or screening process but depend on factors such as the characteristics of the initial strain, the approach of mutagenesis, the size of the mutation library, and the fermentation conditions. In order to obtain high-production strains, attention should be paid to the strategies of strain engineering, such as by random mutagenesis or through rational design of the amino acid biosynthetic pathways. Combining adaptive laboratory evolution and the rare-codon-based strategy would facilitate obtaining amino acid overproducers.
The methionine and the tryptophan do not have alternative codons among the 20 proteinogenic amino acids. Therefore, this strategy may not be employed directly to these amino acids. One possible solution is to use engineered tRNAs that are able to recognize the stop codons to carry these amino acids. Thus, the corresponding stop codons could be adopted as the artificial rare codons of these amino acids25,26.
One of the biggest shortcomings concerning the conventional analog-based strategy for the selection of amino acid overproducers is the high false positive rate5,27. Strains that go through mutagenesis could easily acquire resistance toward the toxic amino acid analogs, and the tolerance may even be acquired without the aid of mutagens27. These strains could easily escape the selection pressures from the amino acid analogs and, consequently, the selected strains are usually not the true amino acid overproducers which greatly sacrifices the efficiency of the selection process.
In contrast, the rare-codon-based strategy outcompetes the traditional analog-based method by enabling accurate and rapid identifications of amino acid overproducers. To our knowledge, this is the first strategy that adopts the natural law of codon bias. It only relies on a single rare-codon-rich marker gene and, thus, eliminates the use of toxic analogs. The marker genes are generally nontoxic to the host strains, and the protein expressions from rare-codon-rich genes depend primarily on the intracellular concentrations of the corresponding amino acids because of the universal and stringent law of codon bias across all species. This would prevent the strains from escaping the selection pressures. Besides, due to the great diversity of marker genes, the rare-codon-based strategy could offer various choices for both the selection and the screening of amino acid overproducers.
Due to the universal phenomenon of codon bias in all living organisms28, the rare-codon-based selection or screening strategy could theoretically be employed to other microorganisms besides E. coli, especially those with industrial potentials. When changing to a different host, the choice of rare codons used for designing the marker genes should be based on the codon usage frequencies and the abundances of the corresponding tRNAs for the specific host. The medium used for selection or screening should also be optimized accordingly. One example is the commonly used C. glutamicum in amino acid fermentations. A rare-codon-modified kanR gene containing eight arginine rare codons (AGG) has been shown effective in selecting C. glutamicum L-arginine overproducers by a previous study13 (Figure 4b). Explorations of the rare-codon-based strategy should facilitate the constructions and understandings of amino acid overproducers. Besides amino acids, the rare-codon-based strategy could also be employed with isobutanol, 3-methyl-1-butanol, 2-methyl-1-butanol, and other products that share the same biosynthetic pathways with certain amino acids29. Strains identified by marker genes that harbor the rare codons of these amino acids are capable of overproducing the precursor&#160;compounds, which could be channeled to the synthesis of the amino acid derivatives. Therefore, the rare-codon-based strategy could serve as an indirect yet rapid method to reflect the potentials of the strains in accumulating these chemicals either intra- or extracellularly. Key mutations that confer increased amino acid productivities from various overproducers could be identified by deep sequencing and be introduced individually or simultaneously into industrial strains to further improve the amino acid productions.

The current production of amino acids relies heavily on fermentation. However, the titers and yields for most amino acid production strains are below the rising demands of the global amino acid market that is worth billions of dollars1,2. Obtaining high-performance amino acid overproducers are critical for the upgrade of the amino acid industry.
Traditional strategy to identify amino acid overproducers exploits the competitions between amino acids and their analogs in protein synthesis3,4. These analogs are able to charge the tRNAs that recognize the corresponding amino acids and thus inhibit the elongations of the peptide chains, leading to arrested growth or cell death5. One way to resist the analog stresses is to increase the concentrations of intracellular amino acids. The enriched amino acids will outcompete the analogs for the finite tRNAs and ensure the correct synthesis of functional proteins. Therefore, strains that survive the analogs can be selected and are likely the overproducers of the corresponding amino acids.
Although proved successful in selecting overproducers for amino acids such as L-leucine6, the analog-based strategy suffers from severe drawbacks. One major concern is the analog resistance originated from the process of mutagenesis or through spontaneous mutations. Strains with resistance can escape the selection by blocking, exporting, or degrading the analogs5. Another concern is the toxic side effects of the analogs on other cellular processes7. As a consequence, strains that survive the analog selection may not be the amino acid overproducers, while the desired overproducers could be falsely exterminated due to the negative side effects.
Here, a novel strategy based on the law of codon bias is presented in order to achieve accurate and rapid identifications of amino acid overproducers. Most amino acids are encoded by more than one nucleotide&#160;triplet that is favored differently by the host organisms8,9. Some codons are rarely used in the coding sequences and are referred to as the rare codons. Their translations into amino acids rely on the cognate tRNAs that carry the corresponding amino acids. However, the tRNAs that recognize rare codons usually have much lower abundances than the tRNAs of the common codons10,11. Consequently, these rare tRNAs are less likely to capture the free amino acids in the competitions with other isoacceptors, and translations of the rare-codon-rich sequences begin to decelerate or even are terminated when the amounts of amino acids are limited10. The translations could, theoretically, be restored if there is an amino acid surplus after charging the synonymous common tRNAs due to overproductions or extra feedings of the corresponding amino acids12. If the rare-codon-rich gene encodes a selection or screening marker, strains exhibiting the corresponding phenotypes can then be readily identified and are likely the overproducers of the targeted amino acids.
The above strategy is applied to establish a selection and a screening system for the identification of amino acid overproducers. The selection system uses antibiotic resistance genes (e.g., kanR) as markers while the screening system uses the genes encoding fluorescent (e.g., green fluorescent protein [GFP]) or chromogenic (e.g., PrancerPurple) proteins. The marker genes in both systems are modified by replacing defined numbers of the common codons for the targeted amino acid with its synonymous rare alternative. Strains in the mutation library that harbor the rare-codon-rich marker gene are selected or screened under proper conditions, and the overproducers of the targeted amino acids can be readily identified. The workflow begins with the construction of the rare-codon-rich marker gene system, followed by the optimization of the working conditions, and then the identification and verification of the amino acid overproducers. This analog-independent strategy is based on the dogma in protein translation and has been practically verified to enable accurate and rapid identifications of amino acid overproducers. Theoretically, it could be directly employed to amino acids with rare codons and to all microorganisms. In all, the rare-codon-based strategy will serve as an efficient alternative to the conventional analog-based approach when proper analogs for specific amino acids are unavailable, or when a high false positive rate is the major concern. The protocol below uses leucine rare codon to demonstrate this strategy in identifying Escherichia coli L-leucine overproducers.

<table><tbody><tr><td>Acetonitrile</td><td>Thermo</td><td>51101</td><td></td></tr><tr><td>EasyPure HiPure Plasmid MiniPrep Kit</td><td>Transgen</td><td>EM111-01</td><td></td></tr><tr><td>EasyPure Quick Gel Extraction Kit</td><td>Transgen</td><td>EG101-01</td><td></td></tr><tr><td>Gibson assembly master mix</td><td>NEB</td><td>E2611S</td><td></td></tr><tr><td>Isopropyl &amp;beta;-D-1-thiogalactopyranoside</td><td>Solarbio</td><td>I8070</td><td></td></tr><tr><td>L-leucine</td><td>Sigma</td><td>L8000</td><td></td></tr><tr><td>Microplate reader</td><td>Biotek</td><td>Synergy 2</td><td></td></tr><tr><td>n-hexane</td><td>Thermo</td><td>H3061</td><td></td></tr><tr><td>Phenyl isothiocyanate</td><td>Sigma</td><td>P1034</td><td></td></tr><tr><td>PrancerPurple CPB-37-441</td><td>ATUM</td><td>CPB-37-441</td><td></td></tr><tr><td>TransStar FastPfu Fly DNA polymerase</td><td>Transgen</td><td>AP231-01</td><td></td></tr><tr><td>Triethylamine</td><td>Sigma</td><td>T0886</td><td></td></tr><tr><td>Ultra-high performance liquid chromatography</td><td>Agilent</td><td>1290 Infinity II</td><td></td></tr><tr><td>Wild type &lt;em&gt;C. glutamicum&lt;/em&gt;</td><td>ATCC</td><td>13032</td><td></td></tr><tr><td>XL10-Gold&lt;em&gt; E. coli&lt;/em&gt; competent cell</td><td>Agilent</td><td>200314</td><td></td></tr><tr><td>ZORBAX RRHD Eclipse Plus C18 column</td><td>Agilent</td><td>959759-902K</td><td></td></tr></tbody></table>

identifying amino acid overproducers using rare-codon-rich markers

To satisfy the ever-growing market for amino acids, high-performance production strains are needed. The amino acid overproducers are conventionally identified by harnessing the competitions between amino acids and their analogs. However, this analog-based method is of low accuracy, and proper analogs for specific amino acids are limited. Here, we present an alternative strategy that enables an accurate, sensitive, and high-throughput screening of amino acid overproducers using rare-codon-rich markers. This strategy is inspired by the phenomenon of codon usage bias in protein translation, for which codons are categorized into common or rare ones based on their frequencies of occurrence in the coding DNA. The translation of rare codons depends on their corresponding rare transfer RNAs (tRNAs), which cannot be fully charged by the cognate amino acids under starvation. Theoretically, the rare tRNAs can be charged if there is a surplus of the amino acids after charging the synonymous common isoacceptors. Therefore, retarded translations caused by rare codons could be restored by feeding or intracellular overproductions of the corresponding amino acids. Under this assumption, a selection or screening system for identifying amino acid overproducers is established by replacing the common codons of the targeted amino acids with their synonymous rare alternatives in the antibiotic resistance genes or the genes encoding fluorescent or chromogenic proteins. We show that the protein expressions can be greatly hindered by the incorporation of rare codons and that the levels of proteins correlate positively with the amino acid concentrations. Using this system, overproducers of multiple amino acids can be readily screened out from mutation libraries. This rare-codon-based strategy only requires a single modified gene, and the host is less likely to escape the selection than in other methods. It offers an alternative approach for obtaining amino acid overproducers.

For the selection system, a sharp decrease in OD600 for strains harboring the rare-codon-rich antibiotic resistance gene should be observed in comparison to the strain harboring the wild-type antibiotic resistance gene when cultured in a suitable medium (Figure 1a). Under the same conditions, the decrease in cell OD600 becomes more obvious as the number of rare codons in the antibiotic resistance gene increases (Figure 1a). It should be noted that the inhibition of rare codon on protein expressions mostly takes place under starved conditions. Therefore, if the LB medium is not properly diluted, no significant decrease in cell OD600 will be observed for the strain harboring the rare-codon-rich marker gene in comparison to the strain harboring the wild-type gene (Figure 1b). After extra feeding of the corresponding amino acid, the OD600 for the strain harboring the rare-codon-rich antibiotic resistance gene will increase significantly and approach that of the strain harboring the wild-type gene (Figure 1c).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59331/59331fig1.jpg" /> 
Figure 1: Effects of rare codon on the expressions of marker genes used for the selection and the screening systems. (a) The cell OD600 for an E. coli strains harboring the antibiotic resistance gene (kanR) with 6, 16, 26, and 29 leucine rare-codon (RC6, RC16, RC26, and RC29) replacement after 5 h of incubation. (b) The cell OD600 for an E. coli strain harboring the wild-type (WT) and the rare-codon-rich kanR (RC) in 1x, 0.5x, and 0.2x LB media after 5 h of incubation. (c) Effects of feeding L-leucine on the cell growth for E. coli strains harboring the leucine rare-codon-rich kanR gene after 5 h of incubation. The values and error bars represent the mean and the SD (n = 6). The feeding of L-leucine significantly increased the OD600 for cells harboring the rare-codon-rich kanR. The only exception was for the feeding of 2 g&#183;L-1 L-leucine due to a high SD in OD600 for the feeding treatment. (d) Effects of rare-codon and L-leucine feeding on GFP expressions from the wild-type (WT) and the leucine rare-codon-rich (RC) genes after 16 h of incubation. The feeding of 0.5&#8211;2 L-1 L-leucine significantly increased the fluorescence intensity for cells harboring the rare-codon-rich gfp. The values and error bars represent the mean and the SD (n = 3). **P &#60; 0.01, ***P &#60; 0.001 as determined by two-tailed t-test, and only the most significant results were shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59331/59331fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
For the screening system, the fluorescence intensity and the number of fluorescent cells will be significantly lower for the strain that expresses the fluorescent protein from the rare-codon-rich gene than from the wild-type gene (Figure 1d and Figure 2). When using the purple protein, the color developed from the rare-codon-rich ppg should be lighter than that from the wild-type gene when expressed under the same conditions for the same incubation period (Figure 3). Feeding of the corresponding amino acid will restore protein expressions from the rare-codon-rich genes. For strains harboring the rare-codon-rich gfp, the fluorescence intensity (Figure 1d) and the number of fluorescent cells (Figure 2) should increase significantly and approach that of the strains containing the wild-type gfp. When undiluted LB is used, the amino acids in the medium would be sufficient to allow slow expression of the rare-codon-rich ppg even without extra L-leucine feeding, and the expressed purple protein would become visible once the cells are pelleted (Figure 3). However, this does not conceal the fact that gene expression from the rare-codon-rich ppg was dramatically enhanced by feeding of the L-leucine to 2 g&#183;L-1, especially when observed in liquid culture (Figure 3). Therefore, the liquid culture is a better choice for screening based on chromogenic proteins, and the use of diluted LB medium would bring a more significant difference between the phenotypes induced by the wild-type and the rare-codon-rich genes.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59331/59331fig2.jpg" /> 
Figure 2: The number of fluorescent E. coli cells that harbor the wild-type gfp or the leucine rare codon-rich gfp (gfp-RC) after the addition of L-leucine. Cells were cultured in 1x LB medium. Scale bar = 20 &#956;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59331/59331fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59331/59331fig3.jpg" /> 
Figure 3: Color development for cells harboring the wild-type (WT) and the leucine rare-codon-rich (RC) ppg genes that encode a purple protein in 1x LB medium (left panel) and the effect of L-leucine feeding on cell culture color development (right panel). The ppg genes were induced when the cells entered the exponential phase and the images were captured 3 h after the induction. The L-leucine was added to the medium together with the inducer in the feeding assay. The colored circles were generated by picking the colors of the cell cultures and the cell pellets. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59331/59331fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The rare-codon-based strategy is able to identify overproducers of the targeted amino acids from the mutation library, and these mutants should produce higher amounts of the targeted amino acids than the parent strains (Figure 4).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59331/59331fig4.jpg" /> 
Figure 4: Amino acids produced by the wild-type and the mutated strains identified by the rare-codon-based strategy. (a) L-leucine productions of E. coli strains identified from mutation libraries by the kanR-RC29 (EL-1 to EL-5) and the gfp-RC that harbors 29 and 19 leucine rare codons (EL-6 to EL-10), respectively. (b) L-arginine productions of Corynebacterium glutamicum strains selected by the rare-codon-rich kanR, which contained eight arginine rare codons (AGG). The marker gene was introduced into the C. glutamicum mutation libraries derived from the wild-type strain ATCC13032. The selection medium was 0.3x CGIII supplied with 25 &#956;g&#183;mL-1 kanamycin. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59331/59331fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers at JoVE.com

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

This study presents an alternative strategy to the conventional toxic analog-based method in identifying amino acid overproducers by using rare-codon-rich markers to achieve accuracy, sensitivity, and high-throughput simultaneously.

To satisfy the ever-growing market for amino acids, high-performance production strains are needed. The amino acid overproducers are conventionally ...

identifying-amino-acid-overproducers-using-rare-codon-rich-markers

Nanyang Technological University

Research

JoVE Journal

Biochemistry

8.3K Views.  Beijing Institute of Technology. This study presents an alternative strategy to the conventional toxic analog-based method in identifying amino acid overproducers by using rare-codon-rich markers to achieve accuracy, sensitivity, and high-throughput simultaneously.

Video: Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

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Genetics

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Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population &#34;background&#34; variation). This method can identify amino acid residue location &#34;hotspots&#34; of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

Amino acid-level signal-to-noise analysis determines the prevalence of genetic variation at a given amino acid position normalized to background genetic variation of a given population. This allows for identification of variant &#34;hotspots&#34; within a protein sequence (signal) that rises above the frequency of rare variants found in a population (noise).

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

The number of significant genetic associations with common complex traits is constantly increasing. However, most of these associations have not been understood at molecular level. One of the mechanisms mediating the effect of DNA variants on phenotypes is gene expression, which has been shown to be particularly relevant for complex traits1.
This method tests in a cellular context the effect of specific DNA sequences on gene expression. The principle is to measure the relative abundance of transcripts arising from the two alleles of a gene, analysing cells which carry one copy of the DNA sequences associated with disease (the risk variants)2,3. Therefore, the cells used for this method should meet two fundamental genotypic requirements: they have to be heterozygous both for DNA risk variants and for DNA markers, typically coding polymorphisms, which can distinguish transcripts based on their chromosomal origin (Figure 1). DNA risk variants and DNA markers do not need to have the same allele frequency but the phase (haplotypic) relationship of the genetic markers needs to be understood. It is also important to choose cell types which express the gene of interest. This protocol refers specifically to the procedure adopted to extract nucleic acids from fibroblasts but the method is equally applicable to other cells types including primary cells.
DNA and RNA are extracted from the selected cell lines and cDNA is generated. DNA and cDNA are analysed with a primer extension assay, designed to target the coding DNA markers4. The primer extension assay is carried out using the MassARRAY (Sequenom)5 platform according to the manufacturer's specifications. Primer extension products are then analysed by matrix-assisted laser desorption/ionization time of-flight mass spectrometry (MALDI-TOF/MS). Because the selected markers are heterozygous they will generate two peaks on the MS profiles. The area of each peak is proportional to the transcript abundance and can be measured with a function of the MassARRAY Typer software to generate an allelic ratio (allele 1: allele 2) calculation. The allelic ratio obtained for cDNA is normalized using that measured from genomic DNA, where the allelic ratio is expected to be 1:1 to correct for technical artifacts. Markers with a normalised allelic ratio significantly different to 1 indicate that the amount of transcript generated from the two chromosomes in the same cell is different, suggesting that the DNA variants associated with the phenotype have an effect on gene expression. Experimental controls should be used to confirm the results.

Genetic associations often remain unexplained at a functional level. This method aims to assess the effect of phenotype-associated genetic markers on gene expression by analyzing cells heterozygous for transcribed SNPs. The technology allows accurate measurement by MALDI-TOF mass spectrometry to quantify allele-specific primer extension products.

The number of significant genetic associations with common complex traits is constantly increasing. However, most of these associations have not been ...

Biology

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Synthetic biology enables the engineering of proteins with unprecedented properties using the co-translational insertion of non-canonical amino acids. Here, we presented how a spectrally red-shifted variant of a GFP-type fluorophore with novel fluorescence spectroscopic properties, termed &#34;gold&#34; fluorescent protein (GdFP), is produced in E. coli via selective pressure incorporation (SPI).

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Bioengineering

Optimizing Protein Functions Through Yeast Surface Display Techniques

Protein Engineering by Yeast Surface Display

Protein engineering enables the improvement of existing functions of a given protein or the generation of novel functions. One of the most widely used and versatile tools in the protein engineering field is yeast surface display, where a pool of randomized proteins is expressed on the surface of yeast. The linkage of phenotype (e.g., binding of the yeast-displayed protein to the antigen of interest) and genotype (the plasmid encoding for the protein variant) enables selection of this library for desired properties and subsequent sequencing of enriched variants. By combining magnetic bead selection with flow cytometric sorting, protein variants with enhanced binding to a target antigen can be selected and enriched. Notably, in addition to affinity maturation, binding to a target can also be achieved without any initial binding affinity. Here, we provide a step-by-step protocol that covers all essential parts of a yeast surface display selection campaign and gives examples of typical yeast surface display results. We demonstrate that yeast surface display is a broadly applicable and robust method that can be established in any molecular biology laboratory with access to flow cytometry.

This protocol describes the essential steps for conducting yeast surface display selection campaigns to enrich protein variants binding to an antigen of interest.

Protein engineering enables the improvement of existing functions of a given protein or the generation of novel functions. One of the most widely used and ...

Cell-Type Specific Protein Purification and Identification from Complex Tissues Using a Mutant Methionine tRNA Synthetase Mouse Line

Understanding protein homeostasis in vivo is key to knowing how the cells work in both physiological and disease conditions. The present protocol describes in vivo labeling and subsequent purification of newly synthesized proteins using an engineered mouse line to direct protein labeling to specific cellular populations. It is an inducible line by Cre recombinase expression of L274G-Methionine tRNA synthetase (MetRS*), enabling azidonorleucine (ANL) incorporation to the proteins, which otherwise will not occur. Using the method described here, it is possible to purify cell-type-specific proteomes labeled in vivo and detect subtle changes in protein content due to sample complexity reduction.

This protocol describes how to perform cell-type-specific protein labeling with azidonorleucine (ANL) using a mouse line expressing a mutant L274G-Methionine tRNA synthetase (MetRS*) and the necessary steps for labeled cell-type-specific proteins isolation. We outline two possible ANL administration routes in live mice by (1) drinking water and (2) intraperitoneal injections.

Understanding protein homeostasis in vivo is key to knowing how the cells work in both physiological and disease conditions. The present protocol ...

Neuroscience

Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

In recent years, it has become evident that ribosomes not only decode our mRNA but also guide the emergence of the polypeptide chain into the crowded cellular environment. Ribosomes provide the platform for spatially and kinetically controlled binding of membrane-targeting factors, modifying enzymes, and folding chaperones. Even the assembly into high-order oligomeric complexes, as well as protein-protein network formation steps, were recently discovered to be coordinated with synthesis.
Here, we describe Selective Ribosome Profiling, a method developed to capture co-translational interactions in vivo. We will detail the various affinity purification steps required for capturing ribosome-nascent-chain complexes together with co-translational interactors, as well as the mRNA extraction, size exclusion, reverse transcription, deep-sequencing, and big-data analysis steps, required to decipher co-translational interactions in near-codon resolution.

Co-translational interactions play a crucial role in nascent-chain modifications, targeting, folding, and assembly pathways. Here, we describe Selective Ribosome Profiling, a method for in vivo, direct analysis of these interactions in the model eukaryote Saccharomyces cerevisiae.

In recent years, it has become evident that ribosomes not only decode our mRNA but also guide the emergence of the polypeptide chain into the crowded ...

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical for&#160;comprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Summary

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Protein Engineering by Yeast Surface Display

Cell-Type Specific Protein Purification and Identification from Complex Tissues Using a Mutant Methionine tRNA Synthetase Mouse Line

Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

An Integrated Approach for Microprotein Identification and Sequence Analysis