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Tyrosine

Tyrosine is a nonessential amino acid that plays a crucial role in various physiological processes.
It serves as a precursor for the synthesis of important neurotransmitters and hormones, including dopamine, epinephrine, and thyroid hormones.
Tyrosine is involved in melanin production, skin pigmentation, and the regulation of metabolism.
Dietary sources of tyrosine include protein-rich foods such as meat, dairy, eggs, and legumes.
Proper tyrosine levels are essential for maintaining cognitive function, mood, and overall health.
Researchers studying tyrosine's impact on the body can leverage PubCompare.ai's AI-driven platform to easily locate, comapre, and identify the best protocols and products for their experiments, enhancing the reproducibility and accuracy of their tyrosine-related studies.

Most cited protocols related to «Tyrosine»

Generation of Diverse Amino Acid Conformations

Cited 74 times

To maximize transferability of the parameters, multidimensional structure scans were employed to generate conformational diversity. For smaller side chains, grid scans in dihedral space were used to generate side chain variety, including both α and β backbone conformations for each side chain rotamer. Grid scans were generated for Val in one dimension, as it only has χ1, at an interval of 10°. Grids were generated for Asp⁻, Asn, Cys, Phe, His (δ-, ɛ-, and doubly-protonated), Ile, Leu, Ser, Thr, and Trp in two dimensions, as they have χ1 and χ2, at intervals of 20°, yielding 324 structures per amino acid.
We were unable to exhaustively explore side chain conformational space side chains with more than two rotatable bonds. Tyrosine has 3 rotatable χ bonds, but dihedral space is reduced as 180° rotation of either the phenol (χ2) or of the hydroxyl produce the same effect when accounting for symmetry of the ring. We therefore fully scanned each tyrosine dihedral when the other two were at a stable rotamer defined as any instance of that value in the rotamer library for this amino acid, rounded to the nearest 10° and limiting χ2 to (−90°, 90°] to account for symmetry. Stable rotamers for the hydroxyl, not in the rotamer library, were inferred from the QM energy profiles discussed above. Stable rotamers were 180° or ±60° for χ1, ±30° or 90° for χ2, and 0° or 180° for the hydroxyl. Conformations were generated using a full scan for each dihedral (at 20° increments), repeated for every combination of stable rotamer values for the other two dihedrals. As protonated aspartate has nearly the same dihedrals as Tyr (χ1, χ2 and hydroxyl), it was scanned in the same manner, but without χ2 restriction because aspartate does not have the same symmetry properties.
Cysteine presents a special case, as it can form disulfide bonds that bridge two amino acids. In addition to developing parameters for reduced Cys (no disulfide), a pair of Cys dipeptides with a disulfide bond was employed to scan the S-S energy profile. However, a disulfide between Cys_A and Cys_B has a total of five dihedrals: χ1_A, χ2_A, χ_SS, χ2_B, and χ1_B. As full sampling across five dihedrals is clearly intractable, conformation space was reduced by applying the same χ1 / χ2 values to both dipeptides. Using this symmetry, a two-dimensional scan was performed for all χ1 / χ2 combinations using 20° spacing; this scan was repeated with χ_SS restrained to 180°, ±60°, or ±90° (five 2D scans). Separately, the χ_SS profile was scanned with 20° spacing using χ1 of 180° or ±60° and χ2 of 180° or ±60° (nine 1D scans total). As with the other amino acids, the entire procedure was repeated with the backbone in α and β conformations; here, both dipeptides adopted the same backbone conformation.
The remaining side chains, Arg⁺, Gln, Glu (protonated), Glu⁻,Lys⁺, and Met, have at least three side chain dihedrals (Table S1). Rather than performing a grid search, MD simulations were used to generate diverse conformations of these side chains. Each dipeptide was simulated twice, with α or β backbone restraints, for 100 ns each. To overcome kinetic traps, these simulations were performed at 500 K and the dielectric was set to 4r. Next, a diverse subset was generated by mapping each conformation to a multidimensional grid spaced 10° in each χ. The five lowest energy conformations at each grid point were saved. From each simulation grid, five hundred structures were randomly selected (comparable to the number generated by the grid procedure described above for Tyr). Because the longer, more flexible side chains of these amino acids can adopt conformations with strong interactions between backbone and side chain, conformations where we suspected the in vacuo MM description may produce fitting artifacts were excluded, using electrostatic and distance cutoffs defined in the Supporting Information.

Maier J.A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K.E, & Simmerling C. (2015). ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of chemical theory and computation, 11(8), 3696-3713.

Publication 2015

Amino Acids Aspartate Dipeptides Disulfides DNA Library Electrostatics Hydroxyl Radical Kinetics Phenol Radionuclide Imaging Tyrosine Vertebral Column

Local Distance Difference Test (lDDT) for Protein Structure Evaluation

Cited 69 times

lDDT measures how well the environment in a reference structure is reproduced in a protein model. It is computed over all pairs of atoms in the reference structure at a distance closer than a predefined threshold R_o (called inclusion radius), and not belonging to the same residue. These atom pairs define a set of local distances L. A distance is considered preserved in the model M if it is, within a certain tolerance threshold, the same as the corresponding distance in . If one or both the atoms defining a distance in the set are not present in M, the distance is considered non-preserved. For a given threshold, the fraction of preserved distances is calculated. The final lDDT score is the average of four fractions computed using the thresholds 0.5 Å, 1 Å, 2 Å and 4 Å, the same ones used to compute the GDT-HA score (Battey et al., 2007 (link)). For partially symmetric residues, where the naming of chemically equivalent atoms can be ambiguous (glutamic acid, aspartic acid, valine, tyrosine, leucine, phenylalaine and arginine), two lDDTs, one for each of the two possible naming schemes, are computed using all non-ambiguous atoms in M in the reference. The naming convention giving the higher score in each case is used for the calculation of the final structure-wide lDDT score.
The lDDT score can be computed using all atoms in the prediction (the default choice), but also using only distances between Cα atoms, or between backbone atoms. Interactions between adjacent residues can be excluded by specifying a minimum sequence separation parameter. Unless explicitly specified, the calculation of the lDDT scores for all experiments described in this article has been performed using default parameters, i.e. R_o = 15 Å, using all atoms at zero sequence separation.

Mariani V., Biasini M., Barbato A, & Schwede T. (2013). lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics, 29(21), 2722-2728.

Publication 2013

Arginine Aspartic Acid Conferences Glutamic Acid Immune Tolerance Leucine Radius Staphylococcal Protein A Tyrosine Valine Vertebral Column

SMURF: Fungal Genome Mining for Secondary Metabolites

Cited 57 times

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Open the protocol to access the free full text link

Publication 2010

Acyltransferase Amino Acids Anabolism brevianamide F CSF2RB protein, human Dehydrogenase, Aminoadipate-Semialdehyde Dimethylallyltranstransferase Enzymes Genes Genes, Fungal Genome Genome, Fungal Hybrids Lyngbya Toxins Nitric Oxide Synthase non-ribosomal peptide synthase Proteins Protein Subunits SET Domain short chain trans-2-enoyl-CoA reductase Synthase, Polyketide tryptophan dimethylallyltransferase Tyrosine Vertebral Column

Computational Prediction of Phosphorylation Sites

Cited 34 times

The PhosphoBase (6 (link)) consists of 1883 experimentally verified phosphorylation sites within 597 protein entries. The number of serine, threonine and tyrosine sites is 984, 246 and 653, respectively. Swiss-Prot (7 (link)) (release 45 of October 2004) maintains 163 500 protein entries, of which 3614 have phosphorylation annotation. Among these entries, the number of serine, threonine and tyrosine sites was 1005, 281 and 321, respectively. Generally, the serine, threonine and tyrosine, which are not annotated as phosphorylation residues, within the experimentally validated phosphorylated proteins, are selected as negative sets, i.e. the non-phosphorylated sites. Therefore, two negative (non-phosphorylated) datasets were obtained from the PhosphoBase and Swiss-Prot based on the phosphorylation annotation. Because of the absence of good negative dataset exists for non-phosphorylated sites, the residues that had not been previously annotated as phosphorylated in phosphorylation annotated proteins were chosen as a reflection of more general non-phosphorylated sites. Supplementary Table S1 summarizes the statistics of kinase-specific phosphorylated sites used for learning models in the proposed application. This work confirms the existence of two major protein kinases phosphorylating either at serine/threonine residues or at tyrosine residues.
Figure 1 depicts a flowchart of the proposed method. Phosphorylated sites were first extracted as positive sets; non-phosphorylated sites were extracted as negative sets, and the catalytic kinase annotations were obtained from PhosphoBase and Swiss-Prot. The positive sets were then categorized by catalytic kinases. Alternatively, in larger positive groups, the sequences of the phosphorylated sites can be clustered into subgroups by maximal dependence decomposition (MDD) (8 (link)). The MDD was first applied in nucleotides and is a recursive process to divide a sequence set into tree-like subgroups based on the positional dependency of the sequences. Here, we applied the MDD to group protein phosphorylation substrates into subgroups. As the example given in Figure 1, 232 phosphorylation serine substrates are grouped into subgroups. When applying MDD to cluster the sequences of a positive set, a parameter, i.e. the minimum-cluster-size, should be set. If the size of a subgroup is less than the minimum-cluster-size, the subgroup is terminated to be divided. The MDD process terminates until all the subgroup sizes are less than the minimum-cluster-size.
Thereupon, the concept of the profile HMM was adopted to learn computational models from positive sets of phosphorylation sites. To evaluate the learned models, k-fold cross-validation and leave-one-out cross-validation were performed on them. After evaluating the models, the model with highest accuracy in each dataset was chosen.
For each kinase-specific positive set of the phosphorylated sites, the best performed model is selected and used to identify the phosphorylation sites within the input protein sequences by HMMsearch (9 (link)). To search the hits of a model, HMMER returns both a HMMER bit score and an expectation value (E-value). The HMMER bit score is used as the criterion to define a HMM match. We select the HMMER score as the criterion to define a HMM match. A search of a model with the HMMER score greater than the threshold t is defined as a positive prediction, i.e. a HMM recognizes a phosphorylation site. The threshold t of each model is decided by maximizing the accuracy measure during a variety of cross-validations with the HMM bit score value range from 0 to −10. For example, Supplementary Figure S1 depicts the optimization of the threshold of the HMM bit scores in the S_PKA model. The threshold of the S_PKA model is set to −4.5 to maximize the accuracy measure of the model.
When considering a MDD-clustered dataset, for example, MDD-clustered PKA catalytic serine (S_PKA), the HMMs are trained separately from the subgroups of the phosphorylated sites resulted by MDD. Each model is used to search in the given protein sequences for the phosphorylated sites. A positive prediction of a model group is defined by at least one of the models that makes a positive prediction, whereas a negative prediction is defined as all the models that make negative predictions.

Huang H.D., Lee T.Y., Tzeng S.W, & Horng J.T. (2005). KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Research, 33(Web Server issue), W226-W229.

Publication 2005

Amino Acid Sequence Base Sequence Catalysis Exhaling Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Nucleotides Phosphorylation Phosphotransferases Protein Kinases Proteins Reflex Serine Threonine Trees Tyrosine

Tandem Mass Tag Proteomics of Yeast

Cited 26 times

BY4741 wild-type yeast were grown in yeast extract peptone dextrose media to mid-log phase (OD₆₀₀ = 0.6). Proteins were chemically extracted with YPer (Thermo Scientific Pierce; Rockford, IL), and digested with Promega sequencing-grade modified trypsin (Madison, WI) at a 1:50 enzyme:substrate ratio at 37 °C overnight and quenched by acidification with TFA. Peptides were desalted and labeled with Thermo Scientific Pierce TMTsixplex (Rockford, IL; lot number KD130680A), with intermittent mixing at room temperature, and quenched following an hour of incubation. Peptides labeled with tags of nominal m/z 126 through 131 were mixed in ratios of 1: 5: 2: 1.5: 1: 3, respectively.
Peptides were separated on a Waters nanoACQUITY UPLC (Milford, MA) with a self-packed 9 cm precolumn (75 μm i.d.) and a 30 cm analytical column (50 μm i.d.), both packed with Alltech Alltima 5 μm C18 particles (Deerfield, IL) [33 (link)]. The peptides were eluted with a gradient of 5 to 30% acetonitrile over two hours at a flow rate of 300 nL/min. The eluent was analyzed with LC–MS/MS on a Thermo Scientific LTQ Orbitrap Velos mass spectrometer (San Jose, CA/Bremen, Germany). The instrument method was 165 minutes and consisted of a 30,000 resolving power MS¹ survey scan followed by data-dependent top-10 higher-energy collision dissociation (HCD) MS² at 7,500 resolving power, all detected in the orbitrap. Precursor charges states that were unknown or +1 were excluded, and dynamic exclusion was enabled after one fragmentation event for 45 seconds.
This dataset was searched against the Saccharomyces Genome Database [36 (link)] (http://www.yeastgenome.org/; January 5, 2010 release; “all” file including verified, uncharacterized, and dubious open reading frames, and pseudogenes). Full trypsin enzymatic specificity was required, allowing up to three missed cleavages. Carbamidomethylation of cysteines (+57 Da) and TMT 6-plex on peptide N-termini and lysines (+229 Da) were specified as fixed modifications, while oxidation of methionines (+16 Da) and TMT 6-plex (+229 Da) on tyrosines were specified as variable modifications. An average mass tolerance of ±5 Da was used for precursors, while a monoisotopic mass tolerance of ±0.01 Da was used for products. For TPP analysis, the data was searched with Sequest (version 27 from the University of Washington) or OMSSA (2.1.9) using either a ±5 Da average precursor mass tolerance or a ±10 ppm monoisotopic precursor mass tolerance and a monoisotopic fragment bin size of 0.01 Da (Sequest) or a monoisotopic product mass tolerance of ±0.01 Da (OMSSA). Results were filtered with PeptideProphet, iProphet and ProteinProphet from TPP 4.3 rev 1. The accurate mass, non-parametric model, and decoy estimation options were used in PeptideProphet. The TPP component Libra was used for isobaric label quantitation.

Wenger C.D., Phanstiel D.H., Lee M.V., Bailey D.J, & Coon J.J. (2011). COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA. Proteomics, 11(6), 1064-1074.

Publication 2011

acetonitrile Cysteine Cytokinesis Enzymes Genome Glucose Immune Tolerance Lysine Methionine Neoplasm Metastasis Open Reading Frames Peptides Peptones Promega Proteins Pseudogenes Radionuclide Imaging Saccharomyces Saccharomyces cerevisiae Tandem Mass Spectrometry Trypsin Tyrosine

Most recents protocols related to «Tyrosine»

Microbial Seed Treatments for Enhanced Crop Performance

Not available on PMC !

Example 2

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

A. Seed Treatment with Isolated Microbe

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

B. Seed Treatment with Microbial Consortia

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×10⁵cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.

Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.

Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:

- Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
- Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
- Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
- Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
- Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.

Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.

TABLE 15

StrainShootRoot

Microbe sp.IDCropAssayIOC (%)IOC (%)

Bosea thiooxidans123EfficacyEfficacy

overall100%100%

Bosea thiooxidans54522WheatEarly vigor - insol P30-40 —

Bosea thiooxidans54522RyegrassEarly vigor50-60 50-60

Bosea thiooxidans54522RyegrassEarly vigor - moderate P0-100-10

Duganella violaceinigra111EfficacyEfficacy

overall100%100%

Duganella violaceinigra66361TomatoEarly vigor0-100-10

Duganella violaceinigra66361TomatoEarly vigor30-40 40-50

Duganella violaceinigra66361TomatoEarly vigor20-30 20-30

Herbaspirillum huttiense222Efficacy—

overall100%

Herbaspirillum huttiense54487WheatEarly vigor - insol P30-40 —

Herbaspirillum huttiense60507MaizeEarly vigor - salt stress0-100-10

Janthinobacterium sp.222Efficacy—

Overall100%

Janthinobacterium sp.54456WheatEarly vigor - insol P30-40 —

Janthinobacterium sp.54456WheatEarly vigor - insol P0-10—

Janthinobacterium sp.63491RyegrassEarly vigor - drought0-100-10

stress

Massilia niastensis112EfficacyEfficacy

overall80%80%

Massilia niastensis55184WheatEarly vigor - salt stress0-1020-30

Massilia niastensis55184WinterEarly vigor - cold stress0-1010-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress20-30 20-30

wheat

Massilia niastensis55184WinterEarly vigor - cold stress10-20 10-20

wheat

Massilia niastensis55184WinterEarly vigor - cold stress<0<0

wheat

Novosphingobium rosa211EfficacyEfficacy

overall100%100%

Novosphingobium rosa65589MaizeEarly vigor - cold stress0-100-10

Novosphingobium rosa65619MaizeEarly vigor - cold stress0-100-10

Paenibacillus amylolyticus111EfficacyEfficacy

overall100%100%

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Paenibacillus amylolyticus66316TomatoEarly vigor10-20 10-20

Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10

Pantoea agglomerans323EfficacyEfficacy

33%50%

Pantoea agglomerans54499WheatEarly vigor - insol P40-50 —

Pantoea agglomerans57547MaizeEarly vigor - low N<00-10

Pantoea vagans55529MaizeEarly vigor<0<0

(formerly P. agglomerans)

Polaromonas ginsengisoli111EfficacyEfficacy

66%100%

Polaromonas ginsengisoli66373TomatoEarly vigor0-100-10

Polaromonas ginsengisoli66373TomatoEarly vigor20-30 30-40

Polaromonas ginsengisoli66373TomatoEarly vigor<010-20

Pseudomonas fluorescens122Efficacy—

100%

Pseudomonas fluorescens54480WheatEarly vigor - insol P>100 —

Pseudomonas fluorescens56530MaizeEarly vigor - moderate N0-10—

Rahnella aquatilis334EfficacyEfficacy

80%63%

Rahnella aquatilis56532MaizeEarly vigor - moderate N10-20 —

Rahnella aquatilis56532MaizeEarly vigor - moderate N0-100-10

Rahnella aquatilis56532WheatEarly vigor - cold stress0-1010-20

Rahnella aquatilis56532WheatEarly vigor - cold stress<00-10

Rahnella aquatilis56532WheatEarly vigor - cold stress10-20 <0

Rahnella aquatilis57157RyegrassEarly vigor<0—

Rahnella aquatilis57157MaizeEarly vigor - low N0-100-10

Rahnella aquatilis57157MaizeEarly vigor - low N0-10<0

Rahnella aquatilis58013MaizeEarly vigor0-1010-20

Rahnella aquatilis58013MaizeEarly vigor - low N0-10<0

Rhodococcus erythropolis313Efficacy—

66%

Rhodococcus erythropolis54093MaizeEarly vigor - low N40-50 —

Rhodococcus erythropolis54299MaizeEarly vigor - insol P>100 —

Rhodococcus erythropolis54299MaizeEarly vigor<0<0

Stenotrophomonas chelatiphaga611EfficacyEfficacy

60%60%

Stenotrophomonas chelatiphaga54952MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga47207MaizeEarly vigor<0 0

Stenotrophomonas chelatiphaga64212MaizeEarly vigor0-1010-20

Stenotrophomonas chelatiphaga64208MaizeEarly vigor0-100-10

Stenotrophomonas chelatiphaga58264MaizeEarly vigor<0<0

Stenotrophomonas maltophilia612EfficacyEfficacy

43%66%

Stenotrophomonas maltophilia54073MaizeEarly vigor - low N50-60 —

Stenotrophomonas maltophilia54073MaizeEarly vigor<00-10

Stenotrophomonas maltophilia56181MaizeEarly vigor0-10<0

Stenotrophomonas maltophilia54999MaizeEarly vigor0-100-10

Stenotrophomonas maltophilia54850MaizeEarly vigor 00-10

Stenotrophomonas maltophilia54841MaizeEarly vigor<00-10

Stenotrophomonas maltophilia46856MaizeEarly vigor<0<0

Stenotrophomonas rhizophila811EfficacyEfficacy

12.5%37.5%

Stenotrophomonas rhizophila50839MaizeEarly vigor<0<0

Stenotrophomonas rhizophila48183MaizeEarly vigor<0<0

Stenotrophomonas rhizophila45125MaizeEarly vigor<0<0

Stenotrophomonas rhizophila46120MaizeEarly vigor<00-10

Stenotrophomonas rhizophila46012MaizeEarly vigor<0<0

Stenotrophomonas rhizophila51718MaizeEarly vigor0-100-10

Stenotrophomonas rhizophila66478MaizeEarly vigor<0<0

Stenotrophomonas rhizophila65303MaizeEarly vigor<00-10

Stenotrophomonas terrae221EfficacyEfficacy

50%50%

Stenotrophomonas terrae68741MaizeEarly vigor<0<0

Stenotrophomonas terrae68599MaizeEarly vigor<00-10

Stenotrophomonas terrae68599Capsicum *Early vigor20-30 20-30

Stenotrophomonas terrae68741Capsicum *Early vigor10-20 20-30

The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.

US11871752B2. Agriculturally beneficial microbes, microbial compositions, and consortia (2024-01-16). BIOCONSORTIA, INC. [US]. Inventors: Peter Wigley [NZ], Susan Turner [US], Caroline George [NZ], Thomas Williams [US], Kelly Roberts [US], Graham Hymus [US], Kelvin Lau [NZ].

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Patent 2024

Ammonia Asparagine Aspartic Acid Biological Assay Bosea thiooxidans Calcium Phosphates Capsicum Cells Chlorophyll Cold Shock Stress Cold Temperature Crop, Avian Dietary Fiber DNA Replication Droughts Drought Tolerance Embryophyta Environment, Controlled Farmers Fertilization Glutamic Acid Glutamine Glycine Growth Disorders Herbaspirillum Herbaspirillum huttiense Leucine Lolium Lycopersicon esculentum Lysine Maize Massilia niastensis Methionine Microbial Consortia Nitrates Nitrites Nitrogen Novosphingobium rosa Paenibacillus Paenibacillus amylolyticus Pantoea agglomerans Pantoea vagans Phenotype Phosphates Photosynthesis Plant Development Plant Embryos Plant Leaves Plant Proteins Plant Roots Plants Polaromonas ginsengisoli Pseudoduganella violaceinigra Pseudomonas Pseudomonas fluorescens Rahnella Rahnella aquatilis Retention (Psychology) Rhodococcus erythropolis Rosa Salt Stress Sodium Chloride Sodium Chloride, Dietary Stenotrophomonas chelatiphaga Stenotrophomonas maltophilia Stenotrophomonas rhizophila Stenotrophomonas terrae Sterility, Reproductive Strains Technique, Dilution Threonine Triticum aestivum Tryptophan Tyrosine Vegetables Zea mays

Evolved TpH Background Strain for 5HTP Production

Not available on PMC !

Example 3

Effectiveness of Newly Evolved TpH Background Strain Using Schistosoma mansoni TpH

One of the 7 evolved high 5HTP-producers was selected to further evaluate if the mutations identified were only specifically beneficial to hsTpH2 or could be widely applicable to others. The chosen evolved strain was first cured to lose the evolution plasmid (e.g. the hsTpH gene) and this was immediately followed by re-introducing the E. coli tyrA gene. Upon restoration of the strain's tyrosine auxotrophy, the resulting strain was transformed with pHM2, which is identical to pHM1 used in the earlier evolution study except that the hsTpH gene was replaced with a Schistosoma mansoni TpH gene (SEQ ID NO:9). The 5HTP production of the resulting strain was compared to a wild-type strain carrying pHM2 in the presence of 100 mg/l tryptophan. Results showed the wild-type transformants could only produce ˜0.05 mg/l 5HTP while the newly evolved background strain transformants accumulated >20 mg/l. These production results demonstrated that the mutations acquired in the evolved background strain were not only beneficial to hsTpH but also to other TpHs; possibly applicable also to other aromatic amino acid hydroxylases (e.g. tyrosine hydroxylase).

US11866749B2. Optimized microbial cells for production of melatonin and other compounds (2024-01-09). Danmarks Tekniske Universitet [DK]. Inventors: Hao Luo [DK], Jochen Förster [DK].

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Patent 2024

5-Hydroxytryptophan Aromatic Amino Acids Biological Evolution Cells Escherichia coli Genes Melatonin Mixed Function Oxygenases Mutation Plasmids Schistosoma mansoni Strains Tryptophan Tyrosine Tyrosine 3-Monooxygenase

Biosynthesis of Bisbenzylisoquinoline Alkaloids in Engineered Yeast

Not available on PMC !

Example 5

FIG. 16 illustrates (A) a biosynthetic scheme for conversion of L-tyrosine to bisBlAs and (B) yeast strains engineered to biosynthesize bisBlAs, in accordance with embodiments of the invention. In particular, FIG. 16 illustrates (A) a pathway that is used to produce bisBlAs berbamunine and guattegaumerine. FIG. 16 provides the use of the enzymes ARO9, aromatic aminotransferase; ARO10, phenylpyruvate decarboxlase; TyrH, tyrosine hydroxylase; DODC, DOPA decarboxylase; NCS, norcoclaurine synthase; 6OMT, 6-O-methyltransferase; CNMT, coclaurine N-methyltransferase; CYP80A1, cytochrome P450 80A1; CPR, cytochrome P450 NADPH reductase. Of the metabolites provided in FIG. 16, 4-HPA, 4-HPP, and L-tyrosine are naturally synthesized in yeast. Other metabolites that are shown in FIG. 16 are not naturally produced in yeast.

In examples of the invention, a bisBIA-producing yeast strain, that produces bisBlAs such as those generated using the pathway illustrated in (A), is engineered by integration of a single construct into locus YDR514C. Additionally, FIG. 16 provides (B) example yeast strains engineered to synthesize bisBlAs. Ps6OMT, PsCNMT, PsCPR, and BsCYP80A1 were integrated into the yeast genome at a single locus (YDR514C). Each enzyme was expressed from a constitutive promoter. The arrangement and orientation of gene expression cassettes is indicated by arrows in the schematic. These strains convert (R)- and (S)-norcoclaurine to coclaurine and then to N-methylcoclaurine. In one example, the strains may then conjugate one molecule of (R)—N-methylcoclaurine and one molecule of (S)—N-methylcoclaurine to form berbamunine. In another example, the strains may conjugate two molecules of (R)—N-methylcoclaurine to form guattegaumerine. In another example, the strains may conjugate one molecule of (R)—N-methylcoclaurine and one molecule of (S)-coclaurine to form 2′-norberbamunine. In another embodiment, the strain may be engineered to supply the precursors (R)- and (S)-norcoclaurine from L-tyrosine, as provided in FIG. 5.

The construct includes expression cassettes for P. somniferum enzymes 6OMT and CNMT expressed as their native plant nucleotide sequences. A third enzyme from P. somniferum, CPR, is codon optimized for expression in yeast. The PsCPR supports the activity of a fourth enzyme, Berberis stolonifera CYP80A1, also codon optimized for expression in yeast. The expression cassettes each include unique yeast constitutive promoters and terminators. Finally, the integration construct includes a LEU2 selection marker flanked by loxP sites for excision by Cre recombinase.

A yeast strain expressing Ps6OMT, PsCNMT, BsCYP80A1, and PsCPR is cultured in selective medium for 16 hours at 30° C. with shaking. Cells are harvested by centrifugation and resuspended in 400 μL breaking buffer (100 mM Tris-HCl, pH 7.0, 10% glycerol, 14 mM 2-mercaptoethanol, protease inhibitor cocktail). Cells are physically disrupted by the addition of glass beads and vortexing. The liquid is removed and the following substrates and cofactors are added to start the reaction: 1 mM (R,S)-norcoclaurine, 10 mM S-adenosyl methionine, 25 mM NADPH. The crude cell lysate is incubated at 30° C. for 4 hours and then quenched by the 1:1 addition of ethanol acidified with 0.1% acetic acid. The reaction is centrifuged and the supernatant analyzed by liquid chromatography mass spectrometry (LC-MS) to detect bisBlA products berbamunine, guattegaumerine, and 2′-norberbamunine by their retention and mass/charge.

US11859225B2. Methods of producing epimerases and benzylisoquinoline alkaloids (2024-01-02). The Board of Trustees of the Leland Stanford Junior University [US]. Inventors: Christina D. Smolke [US], Stephanie Galanie [US], Isis Trenchard [US], Catherine Thodey [US], Yanran Li [US].

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Patent 2024

2-Mercaptoethanol 3-phenylpyruvate Acetic Acid Allopurinol Anabolism Barberry Base Sequence berbamunine Buffers Cells Centrifugation coclaurine Codon Cre recombinase Culture Media Cytochrome P450 Dopa Decarboxylase enzyme activity Enzymes Ethanol Gene Expression Genome Glycerin guatteguamerine higenamine Liquid Chromatography Mass Spectrometry Methyltransferase NADP NADPH-Ferrihemoprotein Reductase norcoclaurine synthase Plants Protease Inhibitors Retention (Psychology) S-adenosyl-L-methionine coclaurine N-methyltransferase S-Adenosylmethionine Saccharomyces cerevisiae Strains Transaminases Tromethamine Tyrosine Tyrosine 3-Monooxygenase

HPLC-ESI-MS/MS Proteomic Analysis of Mouse

HPLC-ESI-MS/MS was performed in positive ion mode on a Thermo Fisher Scientific Orbitrap Fusion Lumos tribrid mass spectrometer fitted with an EASY-Spray Source as previously described (Parker et al., 2019 (link)). NanoLC was performed using a Thermo Fisher Scientific UltiMate 3000 RSLCnano System with an EASY Spray C18 LC column (Thermo Fisher Scientific, 50 cm × 75 μm inner diameter, packed with PepMap RSLC C18 material, 2 μm, cat. # ES803); loading phase for 15 min; mobile phase, linear gradient of 1–47% ACN in 0.1% FA for 106 min, followed by a step to 95% ACN in 0.1% FA over 5 min, hold 10 min, and then a step to 1% ACN in 0.1% FA over 1 min and a final hold for 19 min (total run 156 min); Buffer A = 100% H2O in 0.1% FA; Buffer B = 80% ACN in 0.1% FA; flow rate, 250–300 nl/min. All solvents were liquid chromatography mass spectrometry grade. Spectra were acquired using XCalibur, version 2.1.0 (Thermo Fisher Scientific). A “top 15” data-dependent MS/MS analysis was performed (acquisition of a full scan spectrum followed by collision-induced dissociation mass spectra of the 15 most abundant ions in the survey scan). Dynamic exclusion was enabled with a repeat count of 1, a repeat duration of 30 s, an exclusion list size of 500, and an exclusion duration of 40 s. Tandem mass spectra were extracted from Xcalibur ‘RAW’ files and charge states were assigned using the ProteoWizard 2.1.x msConvert script using the default parameters. The fragment mass spectra were searched against the 2016 Mus musculus SwissProt database (16838 entries) using Mascot (Matrix Science; version 2.4) using the default probability cut-off score. The search variables that were used were: 10 ppm mass tolerance for precursor ion masses and 0.5 Da for product ion masses; digestion with trypsin; a maximum of two missed tryptic cleavages; variable modifications of oxidation of methionine and phosphorylation of serine, threonine, and tyrosine. Cross-correlation of Mascot search results with X! Tandem was accomplished with Scaffold (version Scaffold_4.8.7; Proteome Software). Probability assessment of peptide assignments and protein identifications were made using Scaffold. Only peptides with ≥95% probability were considered.

Parker S.S., Ly K.T., Grant A.D., Sweetland J., Wang A.M., Parker J.D., Roman M.R., Saboda K., Roe D.J., Padi M., Wolgemuth C.W., Langlais P, & Mouneimne G. (2023). EVL and MIM/MTSS1 regulate actin cytoskeletal remodeling to promote dendritic filopodia in neurons. The Journal of Cell Biology, 222(5), e202106081.

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Publication 2023

Buffers Cytokinesis Digestion High-Performance Liquid Chromatographies Immune Tolerance Liquid Chromatography Mass Spectrometry Methionine Mice, House Peptides Phosphorylation Proteins Proteome Radionuclide Imaging Serine Solvents Tandem Mass Spectrometry Threonine Trypsin Tyrosine

Azocasein Proteolytic Activity Assay

The proteolytic activity was assessed by mixing 1 mL of culture supernatant and 1 mL of 1% (w/v) azocasein solution dissolved in 0.2 M Tris–HCl buffer of pH 7.0. The enzyme–substrate reaction was allowed to proceed for 30 min at 37 °C and was ended through the addition of 2 mL of 10% (w/v) trichloroacetic acid solution. Thence, incubation for 60 min in a crushed ice bath. The amount of soluble degradation proteins (C) was measured (mg/mL) following this calculation; C (mg/mL) = 1.55 OD₂₈₀ − 0.76 OD₂₆₀. One unit (1 U) of proteolytic enzyme activity was equivalent to one microgram of released l-tyrosine per milliliter per minute of the reaction at the standard conditions of experimentations.

Kotb E., El-Nogoumy B.A., Alqahtani H.A., Ahmed A.A., Al-shwyeh H.A., Algarudi S.M, & Almahasheer H. (2023). A putative cytotoxic serine protease from Salmonella typhimurium UcB5 recovered from undercooked burger. Scientific Reports, 13, 3926.

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Publication 2023

azocasein Bath enzyme activity Enzymes Peptide Hydrolases Proteolysis Trichloroacetic Acid Tromethamine Tyrosine

Top products related to «Tyrosine»

L tyrosine by Merck Group

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L-tyrosine is a naturally occurring amino acid that is used in various laboratory applications. It serves as a precursor in the synthesis of important biomolecules, including neurotransmitters and melanin. L-tyrosine is a white crystalline powder and is soluble in water and other polar solvents.

Tyrosine by Merck Group

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Tyrosine is a laboratory reagent used in biochemical and analytical applications. It is a non-essential amino acid that plays a role in the production of important neurotransmitters and hormones. Tyrosine can be used as a substrate or standard in various in vitro assays and analytical procedures.

Proteome discoverer by Thermo Fisher Scientific

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Proteome Discoverer is a software solution for the analysis of mass spectrometry-based proteomic data. It provides a comprehensive platform for the identification, quantification, and characterization of proteins from complex biological samples.

Mushroom tyrosinase by Merck Group

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Mushroom tyrosinase is a laboratory enzyme derived from mushrooms. It catalyzes the conversion of tyrosine to melanin, a pigment. The core function of mushroom tyrosinase is to facilitate this biochemical reaction in a controlled laboratory setting.

L phenylalanine by Merck Group

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L-phenylalanine is an essential amino acid that serves as a fundamental building block for proteins. It is a commonly used laboratory reagent in various applications, including biochemical research and analysis.

Phenylalanine by Merck Group

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Phenylalanine is an amino acid that is used as a laboratory reagent. It is a colorless and odorless crystalline solid. Phenylalanine is a naturally occurring essential amino acid that is required for protein synthesis.

L tryptophan by Merck Group

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L-tryptophan is an amino acid that serves as a precursor for the synthesis of serotonin, melatonin, and niacin. It is commonly used in various research and laboratory applications.

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L-DOPA is a laboratory product manufactured by Merck Group. It is a chemical compound used as a precursor in the synthesis of various pharmaceutical and research-related substances. The core function of L-DOPA is to serve as a starting material or intermediate in chemical reactions and processes. No further details or interpretations are provided.

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Mascot is a versatile lab equipment designed for efficient sample preparation and analysis. It features a compact and durable construction, enabling reliable performance in various laboratory settings.

Glycine by Merck Group

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Glycine is a colorless, crystalline amino acid that is used as a raw material in the production of various pharmaceutical and chemical products. It serves as a key component in buffer solutions and is commonly employed in the preparation of cell culture media and various biological assays.

What are the different forms or variations of tyrosine that I should be aware of?

Tyrosine exists in several forms, including L-tyrosine, which is the naturally occurring and biologically active form, and D-tyrosine, which is the mirror-image form that is less common. Additionally, there are various tyrosine supplements available, such as N-acetyl-L-tyrosine and DL-phenylalanine, which can have slightly different properties and effects. It's important to be aware of these variations when selecting the appropriate form of tyrosine for your specific research needs.

How can tyrosine be used to support cognitive function and mental health?

Tyrosine is a precursor for the neurotransmitters dopamine, norepinephrine, and epinephrine, which play crucial roles in cognitive function, mood regulation, and stress response. Studies suggest that supplemental tyrosine may help improve cognitive performance, especially under conditions of acute stress or sleep deprivation. Tyrosine may also have a positive impact on mood and can be beneficial for individuals experiencing depression or anxiety. However, the specific effects can vary, so it's important to carefully design your experiments to understand the optimal tyrosine dosage and application for your research objectives.

What are some common challenges or considerations when working with tyrosine in research?

One key consideration when working with tyrosine is ensuring optimal absorption and bioavailability. Factors such as the timing of administration, the presence of other amino acids, and individual differences in metabolism can all influence the efficacy of tyrosine supplementation. Additionally, there can be individual variability in the response to tyrosine, so it's important to carefully monitor and measure outcomes in your research. PubCompare.ai can assist by helping you identify the most effective tyrosine protocols and prodicts from the literature, allowing you to optimize your experimental design and improve the reproducibility of your findings.

How can PubCompare.ai help researchers working with tyrosine?

PubCompare.ai's AI-driven platform can be a valuable tool for researchers studying the effects of tyrosine. The platform allows you to efficiently screen the protocol literature, leveraging artificial intelligence to pinpoint critical insights that can inform your experimental design. By comparing the effectiveness of different tyrosine protocols and products, PubCompare.ai can help you identify the most optimal approach for your specific research goals, enhancing the reproducibility and accuracy of your tyrosine-related studies. This can be particularly helpful when navigating the nuances of tyrosine supplementation and addressing the common challenges associated with this amino acid.

Are there any unique or special applications of tyrosine that I should be aware of?

In addition to its well-known roles in cognitive function and mood regulation, tyrosine has some more specialized applications in research. For example, tyrosine is involved in the production of melanin, the pigment responsible for skin color. As such, tyrosine supplementation has been studied for its potential effects on skin pigmentation and photoprotection. Tyrosine is also a precursor for thyroid hormones, so it may be relevant for research on thyroid function and metabolism. By leveraging PubCompare.ai's AI-driven platform, you can more easily identify and compare the most effective tyrosine protocols for these specialized applications, optimizing your experimental design and outcomes.

More about "Tyrosine"

Tyrosine, a non-essential amino acid, plays a crucial role in various physiological processes.
It serves as a precursor for the synthesis of important neurotransmitters and hormones, including dopamine, epinephrine, and thyroid hormones.
Tyrosine is also involved in melanin production, skin pigmentation, and the regulation of metabolism.
Dietary sources of tyrosine include protein-rich foods such as meat, dairy, eggs, and legumes.
Proper tyrosine levels are essential for maintaining cognitive function, mood, and overall health.
Researchers studying the impact of tyrosine on the body can leverage PubCompare.ai's AI-driven platform to easily locate, compare, and identify the best protocols and products for their experiments, enhancing the reproducibility and accuracy of their tyrosine-related studies.
This includes exploring related terms like L-tyrosine, Proteome Discoverer, Mushroom tyrosinase, L-phenylalanine, Phenylalanine, L-tryptophan, L-DOPA, and Mascot, which are all relevant to tyrosine research.
PubCompare.ai's intelligent comparisons can help researchers identify the optimal tyrosine protocols and products for their specific needs, leading to more accurate and reproducible results.
With the power of PubCompare.ai, researchers can take their tyrosine studies to new heights and advance our understanding of this crucial amino acid and its impact on the human body.