Amino Acids - Science topic

I want to add a longer Gly-Ser (GS) linker, around 15–20 amino acids, between two proteins in a fusion construct to improve their interaction. Previously, I used a short GS5 linker (5 amino acids), but it didn’t help. Now I’m trying a longer linker to reduce steric hindrance and give the proteins more flexibility, hoping this will allow them to interact better.

What is the best method to insert this longer GS linker into my existing vector? I'm looking for practical suggestions on how to modify the construct with this new, longer linker.

I isolated an Fmoc protected unnatural amino acid. TLC indicating a single product, however NMR in CDCl3 indicating two products. Is there a chance of rotamers?

Hello,

I would like to inquire whether enriching the Tryptic Soy Broth (TSB) medium with supplements such as yeast extract, meat peptone, amino acids, and vitamins can enhance the growth rate of pathogenic bacteria. Specifically, is it possible to achieve rapid or exponential bacterial growth within less than 12 hours under such conditions?

We are analyzing amino acids using a C18 column (150 mm × 3 µm) with PITC:Triethylamine (1:1) derivatization, but this method is resulting in broad peaks and poor separation of the amino acids mixture.

Hi everyone,

I am planning to perform MD simulations on a GPCR that contains an unstructured ICL3 region (~70 amino acids). I would like to ask for advice regarding N- and C-terminal treatment in this context.

Any insights or references related to CHARMM force field conventions or best practices in GPCR modeling would be greatly appreciated.

Thank you!

I was wondering whether there are any chemical or enzymatic methods to remove a single nucleotide from the end of DNA. Analogous to Edman degradation but instead of a terminal amino acid it removes a single nucleotide.

It could be either 3 or 5 prime end

It is fine if the nucleotide gets chemically modified, the ejected nucleotide is not of any concern.

The use of exonuclease might remove more than one base which can be a problem.

Hi,

I am doing molecular docking using a 3D structure from PDB. This structure is from the rat protein but I would like to make sure the human protein would give the same results. Only two amino-acids are different in the active site between human and rat (ILE instead of VAL in both cases). Is it possible to generate a 3D structure from the rat protein with these two mutations (VAL to ILE) ?

which software should I use ? Autodock?

Thanks for your help.

Julien

The phenomenon of various amino acids (AA) adsorption onto the silica surface is widely studied. Many people simply add silica into a solution of AA, stir it, centrifuge it, and wash the solid with either 50/50 EtOH/water or pure ethanol, dry it and eventually analyze by FTIR (to see the chemical bond shifting if AA is adsorbed) or TGA (to determine the amount of AA adsorbed).

What I don't understand here is the washing procedure. From what I know, AAs adsorb onto silica surface via hydrogen bonding (I don't think the interaction is covalent) and AAs are highly soluble in water, even in the condition of 50/50 ethanol/water. So, if the supernatant is discarded and you wash the solid with 50/50 EtOH/water, wouldn't you actually wash off these AA that are already adsorbed onto the surface?

Back to my main question, I am grinding silica with AA using a ball mill and also want to determine the adsorption of AA onto silica. In my case, because everything is done in the solid state, how can I proceed from this point? Should I just transfer the powder onto a filter and wash with 50/50 ethanol/water and dry them for the FTIR and TGA analysis? Can this sufficiently remove free AAs that are not adsorbed onto the silica while keeping those adsorbed intact?

The cell is the basic unit of the living body. Tissue is the group of cells, and the tissue further develops as different body organs. The Noble prize in 2024 has been given to the research that discovered the reason behind the development of a cell that was initially similar but later emerged as different organs. This research tells us that the mutation at the level of RNA is the basic reason for the development of cells as different organs.

The RNA is the strande of helix, and it is a combination of twenty amino acids. The combination of amino acids prepares a new protein with a new functionality. This definition already shows the difference in functionality. Then what is the new in this reserach.

Because we don't know the details research so we consider it for the reason.

But we know mutation, it is revolutionary changes in the traits while as the evolutionary changes takes place gradually, so the question is why the mutation takes place?

Are changes not visible? but they reflect directly mutational changes.

Dear all,

Hello, I am refining a protein structure obtained from X-ray diffraction.

It is ~ 300 aa long but there are about 7-8 consecutive amino acids in the middle with weak electron density.

I was trying to fit the amino acids with their side chains by lowering the rmsd but it shows high RSRZ value as high as 4~5.

In addition, there are ~ 18 RSRZ outliers which is 6% of total.

So I thought if I remove those consecutive amino acids or their side chains, the statistics would look okay since I fixed all Ramachandran outliers and Rotamer outliers already.

Please let me know if you need more details to figure this problem. I will upload some more.

Thank you in advance!

Traditional codon optimization tools can be complex and cumbersome. At PeptiCloud, we’ve built an easy-to-use, publicly available codon optimization tool that lets you effortlessly optimize sequences. Simply choose a popular lab strain or customize codon usage for each amino acid with a click. Try it out! https://www.pepticloud.com/codon-optimization-analysis

PeptiCloud is a simple platform for sharing and collaborating on genetic sequences. Whether it's peptides, CRISPR designs, or other genetic data, PeptiCloud makes it easy to share sequences so others can access and build upon them. Getting started is straightforward—just create a project and add sequences. By contributing, you can: - Help others in the community - Discover and use shared sequences - Support open collaboration in genetics Upload sequences today and be part of the effort to make genetic research more open and accessible: https://www.pepticloud.com/

Hello,

For a research project, I need to culture Calu-3 cells and place them in a 6-well plate. Unfortunately, I'm having a lot of difficulty with these cells. They grow very slowly, and many detach. I re-culture them at a ratio of 1:2, and they take about two weeks to reach confluence. Every day, I find a lot of dead cells floating in the medium. When I place them in a 6-well plate, they don't attach and systematically die (only a few cells survive). I culture them in DMEM + 20% FCS + 1% sodium pyruvate + 1% nonessential amino acids + 1% pen/strep. To re-culture them, I use a 0.05% trypsin solution, and they take 15 minutes to detach. I have tried several media (DMEM, DMEM-F12, RPMI), several concentrations of FCS (10%, 15%, 20%) and I am now out of ideas. Does anyone who can grow these cells have any advice for me?

Simple question which I could not find the answer of :

If I want to put the TEV cleavage site at the C-ter of my protein of interest, is it recognized both ways ? SQFYLNE and ENLYFQS ?

I would prefer if I'm only left with one additional amino acid at the C-ter of my protein after cleavage instead of 6 :)

Thanks for the answer!

I work with site-directed mutagenesis, modifying amino acids in ion channel sequences using the splice mutagenesis method. The mutated fragments are then ligated into the full-length channel. However, these fragments are quite large, making it challenging to obtain complete channel constructs.

I needed this research article

The drug discovery pipeline is a notoriously arduous, time-consuming, and expensive process. It often takes over a decade and billions of dollars to bring a new drug to market [6]. This challenging landscape has spurred the exploration of computational approaches to accelerate and improve the efficiency of drug development. Machine learning (ML) and, increasingly, quantum machine learning (QML) are emerging as powerful tools, offering the potential to revolutionize various stages of the drug discovery process [10, 19]. This review will explore the current state of the art, highlighting key applications, emerging trends, and future directions in the application of ML and QML to drug discovery, focusing on the generation of novel molecules, prediction of drug-target interactions, drug repurposing, and the mitigation of challenges such as data scarcity and interpretability. This field is rapidly evolving, with new methods and applications constantly emerging, promising to reshape the future of medicine.

Generative Models for Drug Design

One of the most promising applications of ML in drug discovery is the de novo design of drug molecules. Generative models are trained on existing datasets of known drugs and can then create novel molecules with desired properties [4, 19]. These models offer the potential to explore vast chemical spaces and identify promising drug candidates more efficiently than traditional methods [12].

Energy-based generative models, such as the one developed in [1], are designed for target-specific drug discovery. TagMol, the proposed model, generates molecules with binding affinity scores comparable to real molecules. The study also highlights the advantage of using GAT-based models over GCN baselines for faster and better learning. Similarly, [4] proposes the use of various QML techniques, including generative adversarial networks (GANs), to generate small drug molecules.

Variational autoencoders (VAEs) are another popular approach for drug design. These models learn a latent representation of molecular structures and can generate new molecules by sampling from this latent space [11, 18]. However, as highlighted in [11], near-term quantum computers have limitations that hinder the representation learning in high-dimensional spaces. The authors present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The results suggest that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period. A hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery shows a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance [9]. Moreover, the work in [18] built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer, which could fit a state-of-the-art D-Wave quantum annealer and generate novel chemical structures with medicinal chemistry and synthetic accessibility properties.

Conditional diversity networks offer another approach to drug design. These networks can generate potential drug molecules from a prototype, which is especially valuable in drug discovery where researchers often start from a molecule with some of the desired properties [36].

Predicting Drug-Target Interactions

Identifying drug-target interactions (DTIs) is a critical step in drug discovery. Accurate prediction of these interactions can significantly reduce the time and cost associated with identifying lead compounds [5, 15, 16, 27, 28, 29].

Several studies have explored the use of deep learning models for DTI prediction [5, 16, 28, 29, 30]. DrugMAN, developed in [5], integrates heterogeneous information from multiple biological networks using a mutual attention network. This approach allows the model to capture complex relationships between drugs and targets, leading to improved prediction performance. The study in [16] proposes a method for predicting drug-target binding affinity using deep learning models, using a modified GRU and GNN to extract features from the drug-target protein sequences and the drug molecule map, respectively, to obtain their feature vectors. Another approach, presented in [27], incorporates 3D protein structure features for drug target affinity prediction using GraphPrint. The model generates graph representations for protein 3D structures using amino acid residue location coordinates and combines them with drug graph representation and traditional features to jointly learn drug target affinity, demonstrating that 3D protein structure-based features provide information complementary to traditional features. A cross-field information fusion strategy is employed in [28] to acquire local and global protein information, proposing the siamese drug-target interaction SiamDTI prediction method.

Knowledge graphs and knowledge graph embedding (KGE) models have also shown promise in DTI prediction [15, 21]. In [15], a causal intervention-based confidence measure assesses the triplet score to improve the accuracy of the DTI prediction model. The study in [21] proposes an inductive RGCN for learning informative relation embeddings, even in the few-shot learning regime, which can be applied on the drug-repurposing knowledge graph (DRKG) for discovering drugs for Covid-19. Another study [29] proposes a self-attention-based multi-view representation learning approach for modeling drug-target interactions, achieving competitive prediction performance and offering biologically plausible drug-target interaction interpretations. Furthermore, the study in [30] proposes a convolutional neural network for EEG-mediated DTI prediction, which allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs.

Drug Repurposing

Drug repurposing, the process of identifying new uses for existing drugs, offers a faster and more cost-effective approach to drug discovery compared to de novo drug development [3, 13, 20, 21, 24, 31, 37, 39]. By leveraging existing safety and efficacy data, drug repurposing can significantly accelerate the drug development process [13].

Several studies have explored the use of ML and AI for drug repurposing. NeuroCADR, a novel system for drug repurposing, uses a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees [13]. The system identified novel drug candidates for epilepsy. In [20], a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases (KGML-xDTD) is proposed, which can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths. In the context of the COVID-19 pandemic, [37] proposes Dr-COVID, a graph neural network (GNN) based drug repurposing model. The model constructs a four-layered heterogeneous graph to model the complex interactions between drugs, diseases, genes, and anatomies. The study in [31] proposes a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Similarly, [39] develops a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised).

Self-supervised learning can also be applied to drug repurposing to address label sparsity [24]. The study in [24] proposes a multi-task self-supervised learning framework for computational drug repositioning, which tackles label sparsity by learning a better drug representation. The framework uses data augmentation strategies and contrast learning to mine the internal relationships of the original drug features and a multi-input decoding network to improve the reconstruction ability of the autoencoder model.

Addressing Challenges in Drug Discovery with ML/QML

While ML and QML offer significant promise for drug discovery, several challenges need to be addressed to fully realize their potential. These include data scarcity, the need for interpretability, and the development of methods for handling novel compounds.

Data Scarcity and Cold Start Problems

The availability of high-quality, labeled data is often a limiting factor in ML applications, particularly in drug discovery [24, 25]. Many drug discovery tasks, such as predicting drug-target interactions or drug responses, suffer from data scarcity, making it difficult to train robust and accurate models [8, 24].

To address this challenge, several approaches have been developed. Self-supervised learning techniques can be used to learn representations from unlabeled data, which can then be used to improve the performance of supervised models [24, 26]. Transfer learning, where knowledge learned from related tasks is transferred to the target task, can also be effective [8]. For instance, [8] proposes using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task to solve the cold start problem. The representation learned by CCI and PPI tasks can be transferred smoothly to the drug-target interaction task due to the similar nature of the tasks. The study in [25] discusses the performance of classical and quantum classifiers in QSAR prediction and attempts to demonstrate the quantum advantages in the generalization power of the quantum classifier under conditions of limited data availability.

Interpretability

Many ML models, particularly deep learning models, are often considered "black boxes," making it difficult to understand why they make certain predictions [2, 17]. This lack of interpretability can be a major barrier to the adoption of ML in drug discovery, as it can be challenging to trust and validate the predictions made by these models.

Explainable artificial intelligence (XAI) techniques are being developed to address this challenge [17]. XAI methods aim to provide insights into the decision-making process of ML models, making them more transparent and understandable. The study in [17] provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The study in [29] proposes a self-attention-based multi-view representation learning approach for modeling drug-target interactions that offer biologically plausible drug-target interaction interpretations. The KGML-xDTD framework in [20] provides KG-path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications.

Handling Novel Structures

Many ML models struggle to generalize to novel chemical structures or biological targets that are not well-represented in the training data [21, 28]. This is particularly problematic in drug discovery, where researchers are often interested in identifying novel drug candidates or targeting previously unexplored proteins.

Several strategies are being explored to address this challenge. Graph neural networks (GNNs), which are designed to handle graph-structured data, are particularly well-suited for modeling molecular structures [21, 26, 27]. The study in [21] proposes an inductive RGCN for learning informative relation embeddings, even in the few-shot learning regime. The cross-field information fusion strategy in [28] is employed to acquire local and global protein information.

Quantum computing offers the potential to overcome some of the limitations of classical ML, particularly in handling complex data and performing computationally intensive tasks [4, 6, 10, 11]. QML algorithms can potentially accelerate drug discovery by enabling more accurate simulations, faster molecular property predictions, and the efficient exploration of chemical space [4, 6, 10].

Several studies have explored the application of QML to drug discovery [4, 9, 10, 11, 18, 25]. For example, [4] proposes a suite of QML techniques to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules. The study in [10] discusses the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. The study in [11] presents a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules. The study in [18] built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer, which could fit a state-of-the-art D-Wave quantum annealer and generate novel chemical structures with medicinal chemistry and synthetic accessibility properties. The study in [9] introduces a novel hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery. The study in [25] discusses the performance of classical and quantum classifiers in QSAR prediction.

Hybrid quantum-classical approaches, which combine the strengths of both quantum and classical computing, are particularly promising [9, 10, 18]. These approaches can leverage quantum computers for specific tasks, such as molecular simulations, while using classical computers for other aspects of the drug discovery pipeline.

Future Directions

The application of ML and QML to drug discovery is still in its early stages, and significant opportunities remain for future research and development. Several key areas warrant further investigation:

The ongoing convergence of quantum computing and artificial intelligence has the potential to revolutionize the field of drug discovery, leading to faster, cheaper, and more effective treatments for a wide range of diseases [6, 19]. While challenges remain, the rapid pace of innovation in both ML and QML suggests that these technologies will play an increasingly important role in the future of medicine.

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References

I have an amino acid, in which I introduced the thiol using potassium thioacetate, now I want to reduce the thioacetate to thiol and introduce Trityl using trityl chloride, is there a method to selectively deprotect the thioacetate? while preserving the OMe, so I can do the reaction with Trt-Cl later?

thanks!

For example, now the bindingsites is Gly, what amino acids should I mutate into? Is there any priciples?

Did anyone calculate Beclin1-BCL2 complex with all amino acids (not just BH3domain of Beclin1-BCL2) by Gromacs or...? I nead the crystallography file (like pdb...)

Does anyone have the complete chain of amino acids of Beclin1 or the complete chain of amino acids of complex for Beclin1-BCL2 (not just BH3domain of Beclin1-BCL2)? I mean the crystallography file (like pdb...)

I am going to simulate them by Gromacs.

The reaction between amino acids with ninhydrin at room temperature and aqueous solution how long does it takes (I know the time will change due to different concentration) ? How could I increase it? Is their any catalyst which can increase that reaction.? Thank you.

During coupling with there are chances for coupling happening between NH2 of one peptide chain/amino acid chain in a different chain. I have seen in many articles, they use EDC-NHS method for peptide/amino acid modification of the nanoparticle, but the unwanted groups in peptide/amino acids are not protected. So how to avoid the cross-reaction between peptides/amino acids itself which can reduce the efficiency of the modification ?

Hi all,

I am conducting research on screening peptides (20-35 amino acids) with potential for treating dengue fever through protein-peptide docking methods. In the protein database, I found the target protein in both unbound (apo) and complex-bound (holo) forms. Which form of the target protein should I choose for docking? Additionally, the preparation steps for proteins and peptides before docking that I have learned include: adding missing amino acids, removing water, adding hydrogen, and recalculating charges. Are these all the necessary preparation steps before docking? The servers I have chosen to perform docking are: ClusPro, HADDOCK and HawkDock. Should I prepare the input files the same way for all of them? Please advise me or provide relevant references. I am new to this field and facing many difficulties. Please help me. Thank you.

I have to make a project for the university, and i need that sequence.

Cysteine has a hydropathy score that reflects its overall preference for non-polar environments compared to some other polar amino acids, meaning it can be somewhat hydrophobic. Its positive hydropathy index value suggests that it has some hydrophobic qualities, making it more favorable in non-polar environments compared to very polar amino acids.

Hello everyone!

I'm calculating protein dimer structure in CNS-solve v1.21 using distance restraints obtained from solution NMR experiments.

There is an issue during calculation that most structures (not all) in the ensemble have two specific amino acids: one tyrosine and one phenilalanine broken like shown in the picture. The problem reproduces even after I remove all restraints associated with this amino acids.

I tried to review topology file, but did not find anything suspicious about these residues.

I would greatly appreciate if you could give me any hints on how to solve this problem.

hi,

I am expressing protein with Unnatural Amino acid (UAAs) especially negatively charged UAAs. i am trying different -ve UAAs they were easy to incorporate in protein and gave good expression. But the problem is with sTyr (Tyrosine-O-Sulphate).

Previous, research reported that exogenous styr shown low permeability and the other possible reason is to can't compete with endogenous sTyr.

Different strategies were successfully applied for sTyr incorporation for instance

(1) Propeptide gateway (doi: 10.1038/nchembio.2405)

(2) Engineered periplasmic binding protein (PBPs) to accelerate the UAAs transportation (DOI: 10.1021/acssynbio.9b00076).

One more thing, i also used 2% EtOH as mentioned following articles (

this increased the protein expression with very low sTyr mutant yield.

Hello,

Any advice regarding amino acids elution using Sephadex G10 or 15 ? I need advice about an elution dye, I know that ponceau S dye can be eluted almost at the same time, but would this affect the detetction of amino acids by ninhydrin after TlC separation ?

I want to introduce a stop codon but also substitute different amino acids using a site directed mutagenesis.

The AB1 sequence file sent by Macrogen company was reverse sequence only. I can use it instead of forward sequence to deposit in the gene bank and convert it to amino acids and perform analyses on it, such as alignment and phylogenetic tree construction.

I expressed my target protein with heavy water(D2O) for my study, but I didn't know how to calulate the molecular weight of my target protein. Because I don't know which Hydrogen atoms can be repalced by Deuterium atoms in 20 essential amino acids. Could you give me some advice on it?

Palladium seems to coordinated to strong with a N-Boc amino acid in my substrate. What kind of transition metal has minor coordination with N-Boc amino acid? Is there any references?

I am attempting to conjugate PEG to an amino acid at the C-terminus, for the purposes of producing nanoparticles. I have been told that PEG modified with amine groups can be used for this purpose, but I have not been told how I would go about doing so. What method can be used for that purpose?

I'm trying to do a multiple alignment of coding sequences for a protein I'm interested in, using Mega 5.05. Following the advice in Barry Hall's book, I'm trying to align the sequences using MUSCLE, aligning by codons. However, I keep getting an error during the alignment: "Stop codon(s) are found in the translated sequences. Please select a correct Genetic Code or coding frame." If I ignore the error and look at the translated protein sequences from the resulting alignment, I can't find any evidence of unidentified or termination codons within the sequences, just at the end (as I would expect). Am I misinterpreting the error, or if not, is there a way to figure out which sequence is causing the problem so I can decide what to do with it?

I am having trouble expressing a protein and have heard that co-transforming it can improve stability and increase expression, so I am looking to try this experiment. I only need the kinase domain from the original kinase, but it is expressed in low amounts, so I am trying to do this. I'm using a HIS6 tag and plan to utilize the TEV cleavage site to take the tag off afterwards.

If I were to design a construct for this experiment, could it be organized as follows?

pet28a: HIS6 tag and TEV cleavage site at N-Terminal + Kinase domain (1-200)

pet21b: not any tag at N-Terminal + C-terminal domain(201-500)

After this design, can I put both vectors into BL21(DE3)?

Would this result in expression with amino acids from 1-500 with HIS 6 tag and TEV site at N-terminal?

When designing like this, I was wondering if I should also put a linker or something. I'm completely new to this, so any recommendations for papers to read or sites to refer to would be appreciated.

I apologize if my question is too long.

Hello,

I am currently working on making a fusion protein through the tagging of an endogenous gene. I have restricted and ligated fragments such there has appeared a 3 amino acid (Alanine repeats) insertion after the stop codon of my tag protein and before the 3' UTR start of the endogenous protein. This has not resulted in any frame shifts (The endogenous protein, tagged protein and UTRs are in frame) but I am curious to know if these 3 amino acids might affect the folding or expression of the fusion protein since they were not inherently present in the endogenous 3' UTR sequence.

I am trying to eliminate infection from my plates and therefore, gave UV treatment after addition of amino acid (used for better growth). Can you please suggest if UV destroys amino acids or not, specifically the ones mentioned above?

I want to predict the DAR ratio based on the number of cys available for conjugation to Cys residue. I use Ellman's assay to predict the concentration of Cys after TCEP reduction. I gain a molarity ratio based on Ellman's test, would it be possible to indicate the number of available Cys based on this molarity?

Thanks for your comments in advance~

Hi Dear Researchers, Could anyone please provide me with some information about the HPLC column, specifically the “Purospher STAR RP-18 LiChroCART Cartridge”? I would like to know if it is suitable for separating soluble amino acids in water.

Thank you.

Hello guys.

I have faced the problem of being in need of carboxyl-methylated amino acids.

Amino acids I need are not available in the market so I need to synthesize them by myself.

So thus I'm now finding the method for synthesizing.

But what I found was the derivatization of amino acid for GC that can methylate the amine group either, which cannot be used.

And also found the method using acid and dimethylcarbonate but I'm not sure about the amount for that, because the molarity was too small.

So, thanks for reading

Adding substance like amino acids such as Tyrosine or L-tyrosine in E.coli and LB broth, How many concentration we should add for increasing product?

After entering the protein sequence in the provean tool, it is followed by amino acid variation in order to run it and get the score. which are these variations?

I wanted the whole protein structure of beclin-1 protein, but since I could not find he whole protein structure on it's own, I went for the structure of PI3KC3-C1 complex, which included the whole Beclin-1 protein (pdb id: 8sor).

But when I opened the protein in Autodock, I found that certain amino acids are missing from the beclin-1 structure. Please provide me with suggestions on how to tackle this problem.

Thank you.

I have a protein that is 117 amino acids long, with a 21 amino acid transmembrane domain at the C-terminus. I've cloned this protein into the pET28 expression vector in BL21, but am not seeing any protein expression. Do you think the transmembrane domain could be the reason for the lack of expression? What strategies can I try to improve the expression of this transmembrane protein?

## Background

- Protein length: 117 amino acids (from virus)

- Transmembrane domain : C-terminus, length: 21 amino acids

- Expression vector used: pET28

host:BL21

- Current expression status: No detectable protein expression

I need to derivatize amino acids from a plant based beverage sample, what is the best derivatization method and the internal standard to be used while running HPLC?

The call for papers is open for article collection on Advancements in Alternative Proteins for Aquafeed: Enhancing Nutritional Quality and Utilization The Collection is being hosted by the Taylor & Francis journal Cogent Food & Agriculture (Impact Factor 2.0 (2022)

https://think.taylorandfrancis.com/article_collections/cogent-food-agriculture-advancements-in-alternative-proteins-for-aquafeed-enhancing-nutritional-quality-and-utilization/

Focus areas are on: * Investigating the supplementation of agents like exogenous enzymes, prebiotics, and probiotics, either individually or in combination, to enhance nutrient utilization, growth, and overall health of fish fed plant or insect-based diets * Exploring diverse physical, chemical, and biological methods for improving the nutrient quality and utilization of alternative protein sources in aquafeed *Enhancing the nutritional quality and utilization of insects as protein sources in aquafeed, including feeding different substrates * Supplementing limited essential amino acids and conditionally essential amino acids to alternative protein sources in aquafeed. Please contact Marya Baig at marya.baig@tandf.co.uk with any queries and discount codes regarding this Article Collection. Deadline is 30th July 2024

I made an attempt to synthesize an unnatural amino acid containing phenyl group. In the last step of the synthesis I deprotected trityl protected amino group using 1N HCl aq., extracted, collected aq. layer, neutralized it with NaHCO3 aq. till pH roughly 8, evaporated water and used it further in deprotection of methyl ester group using LiOH monohydrate, quenched reaction with water, neutralized the media and purified the residue by reverse column. After purification i can see a strange impurity in the downfield region of the NMR, it looks like a second set of peaks resembling the product (amino acid) peaks but in the upfield region, i did not observe any extra methine and benzylic proton peaks. The amino acid synthesis started from pure enantiomeric starting material. So, I am not sure what could happen during the deprotection step. I would appreciate your help or any suggestions. Thank you. I will attach a proton NMR of the aromatic region where impurity is observed.

I have found many products in the market stating this, but I have not found any published article that describes this phytotoxicity issue with Plum trees that have been sprayed with an amino acid-based product.

Hi,

I saw a picture on a web (https://www.chegg.com/homework-help/questions-and-answers/9-calculated-molecular-weight-native-gfp-denatured-gfp-native-denatured-proteins-differ-mo-q53517323). It show two computational formula for native and denatured GFP protein respectively. But I can not understand the behind mechanism... Is it a trusty information? Or dose anyone know about this and provided some help?

The SnapGene show that EGFP with 239 amino acids and is 26.9 kDa. But the picture showed 28.183 and 31.622 kDa respectively. That's strange....

Thanks,

Best

I have a protein that has a loop of missing 7 amino acids and i want to model them. what are the tools (webservers or programs) that can help me in this task?

Hi all,

I am trying docking protein-protein interaction. Is it necessary that i should remove heteroatoms from the protein structure, since its making main bonds between the amino acids?pls let me know.

Its ending up with the results like non-resideus ACE, clean the structure and apply charmpolar force.

He

My name is Tiphane I started working with kappaphycus extract [Ksap] and I need help with a setback in my research.

Normally we produce the extract with fresh biomass, but due to a problem in the laboratory, we needed to freeze it. Now we are concerned about whether freezing could interfere with the levels of amino acids and, mainly, phytohormones in the extract. I have been searching for two days and have not found anything in the literature about this. Could anybody help us with this question?

Thank you

I recently attempted to replicate the amino acid derivatization procedure outlined by Sobolevsky et al. 2003. While I followed the steps diligently, unfortunately, I did not achieve the expected results. I followed the following steps:

Step 1: Amino acids (Stock 1): 10 mg L-Proline, 16 mg L-Valine, 10.1 mg DL-Phenylalanine, 10 mg L-Tyrosine, and 11 mg DL-Alanine in 10 mL 0.1M HCl. (Vortexed to dissolve the amino acids)

Step 2: Amino acids (Stock 2): 100 µL of Stock 1 was added in 900 µL of 0.1M HCl to get 100 µg/mL working solution.

Step 3: 100 µL of working solution was lyophilized at -20 ºC or air dried at room temperature.

Step 4: Derivatization of the dried residue was carried out using N-tert-butyldimethylsilyl- N-methyltrifluoroacetamide (MTBSTFA) from Merck. 100 µL of HPLC-grade acetonitrile and 100 µL of MTBSTFA were added to the residue and gentle sonication was performed for 30 s (as mentioned in the article). After sonication, the mixture was heated for 30 min. at 70 ºC. Step 5: 1 mL of HPLC-grade ethyl acetate was added to the derivatized product and centrifuged at 10000 rpm for 15 min at room temperature (just to avoid any crystals) and the supernatant was injected into the GC-MS.

I have attached the column details and the result I obtained.

I greatly appreciate your efforts. Thank you.

Hi, I am trying to insert dehydrated amino acids into a lantibiotic structure.

Lantibiotics have serine and threonine residues that are posttranslationally modified and dehydrated to form 2,3-didehydroalanine (Dha) and 2,3-didehydrobutyrine (Dhb) residues.

How can I insert these dehydrated amino acids in place of serine and threonine?

Can someone point us towards analysis software that is able to identify similar short protein sequences within a protein database through use of a set of comparisons (e.g., similar charged or structural) amino acids) and with a base short protein sequence (say a 10aa sequence)?

I am working on AA based protic ionic liquids. How can we confirm that a proton from amino acid has been transferred to cation to form an ionic liquid??

I've been generating and culturing multiple different human iPSCs lines on mito-MEF feeders using standard hiPSC media composed of Advanced DMEM/F12 (Gibco) with 20% KSR, pen/strep, glutamax, 2-mercaptoethanol and daily fresh bFGF supplementation. As advanced dmem/f12 contains non-essential amino acids, I do not further supplement in my culture. So far, so good. I was wondering if anyone has ever tried to reduce the % of KSR in their culture, as Advanced DMEM/F12 already contains in part holo transferrin, insulin, BSA (AlbuMAX II) and most of other essential components that were also provided by KSR itself, and are completely missing in the standard DMEM/F12 basal medium.

Now I am trying to optimize their culture in 15% KSR, but has anyone ever tried even lower concentrations (which would seriously affect the cost of media for routine iPSC culture) of KSR when using Advanced DMEM/F12?

Can it be that in the primeval ocean, thanks to some energy source like sunlight, volcano or lighting, the amino acids randomly unite a long chain (protein?) and some of which were able to increase their length. When a chain was too long it broke, and if the sequences were similar enough to the original, and chain broke in right place, it had the ability to increase its amino acid chain just like the “parent”. Thus those chains which were able to grown the chain with similar sequences than they have and have a weak point in the right places, could “reproduce”. After that they “learn” how to use another chain as resource for themselves then they try to defend it, which later evolves to a singular cell.

Hello, dear researchers

I hope you are well

Is there a tool that gives 3D coordinates for each amino acid?

The protein sequence is in FASTA format

An example sequence is:

>1a81A.txt

SANHLPFFFGNITREEAEDYLVQGGMSDGLYLLRQSRNYLGGFALSVAHGRKAHHYTIERELNGTYAIAGGRTHASPADLCHYHSQESDGLVCLLKKPFNRPQGVQPKTGPFEDLKENLIREYVKQTWNLQGQALEQAIISQKPQLEKLIATTAHEKMPWFHGKISREESEQIVLIGSKTNGKFLIRARDNNGSYALCLLHEGKVLHYRIDKDKTGKLSIPEGKKFDTLWQLVEHYSYKADGLLRVLTVPCQKI

Thank you for your guidance

I'm trying to seprate amino acids using silica gel.When I spray ninhydrin solution on silica gel it get disturbed.Can anyone tell me how to observe separated amino acid on silica gel plate?

I am currently learning about PyMol to utilize in my project. I used PyMol to visualize potential H-bond interactions in specific amino acid residues. However, I have discovered that Arg465 and Ser461 show a distinct interaction, as shown.

Please help identify this interaction.

How can I change the number of amino acids in pdf files?

I need to change amino acid number for docking, molecular dynamics and... .

I use SWISSMODEL but it's not working good for me.

I am curious whether the pKa of a charged side chain on the surface of a protein can be influenced by the charged state of nearby amino acids.

My hypothesis suggests that, in comparing Figure 1A and 1B, 1B would be more stable energetically due to reducing repulsion from the charge. Therefore, I propose that this could lead to an increase in pKa, as illustrated in Figure 2.

Is my hypothesis scientifically valid, and are there any literature references supporting it?

Hello,

I'm trying to inhibit bovine intestinal alkaline phosphatase in a supplied kit reagent. Since the components of the kit are proprietary, I don't know the actual concentration of alkaline phosphatase in the bottle so trial and error with various inhibitors is the only way I can test for inhibition.

Various papers in the literature have described using amino acids (tryptophan, leucine, phenylalanine) for inhibition of this enzyme. But I'm having trouble getting it to work. There are other enzymes in the reagent that I don't want to impact, so amino acids are ideal for this because I don't want to mess with the pH or other conditions requires by the other enzyme in the mix. I'm also having trouble getting high enough concentrations for inhibition when solubility of these amino acids in water is so low.

Has anyone worked with alkaline phosphatase inhibitors that can give me some advice? Alternative inhibitors to try? Concentrations to try? I just need some fresh ideas. Thanks!

If aspartic acid residues are densely packed and the distance between their side chain functional groups is close, I believe that the pKa might upshift to reduce charge repulsion. Is my understanding correct? Additionally, are there any papers that address pKa shifts in amino acids within proteins based on their surrounding environment?

I made a kind of gene knock-in mice, the vector was inserted after the endogenous promoter of this gene. For the vector, it includes part of exons of WT gene and the WT fragment is flanked by two loxP sites following which there is a part of exons containing amino acid mutant(one amino acid is mutated to another). So this gene-KI F/F mouse expresses WT gene and it expresses gene mutant in which case it breeds with Cre mice. Is there any method to detect the gene expression of this mutant? Western blot cannot work because the length of this gene is consistent......

I have a protein structure loaded into VMD, it has multiple chains. I would like to get the number of chains, and the number of amino acids in each chain using a Tcl script. Does anyone know how to do it?

It is a cationic peptide with a length of less than 20 amino acids.

Hello everyone,

Does anyone know If I want to represent a DNA sequence from Snapgene viewer in the manuscript to point the location of a specific amino acid how I can do that?

Thanks

Doing phospho-STAT5 Western on NK92 human cell line. I keep getting two bands, one fainter than the other. Online searches imply that this might be STAT5a and STAT5b, but the difference in amino acids between these two is 12, and the bands look a little too spread out to just be a difference of 12. Does anyone have any insight? The band in the middle of the gel is off target binding.

I want to make schiff base from amino acid and aldehyde but I am not getting any precipitation. I tried using methanol and ethanol as solvent and adding KOH to dissolve the amino acid in the solvent. experimented with different methods like refluxing and magnetic stirring but of no use. The aldehyde always keeps on getting separated out once kept for evaporation. But no precipitation of product observed. How can i get my schiff base to precipitate?

Hello

I'm trying to do amino acid analysis in my rice germ extract. I want to measure lysine and valine content but the GABA content in this particular extract is very high, hence other peaks are hard to see.

I'm pretty new at HPLC, so I was wondering if anyone have any suggestion on how to measure low content amino acid ?

Thank you

Hi, I am working on protein-protein interaction studies, specifically on antibody-antigen interaction. I would like to observe the changes in interaction if there's mutation occurs in the protein. Could anyone suggest a tool that can be used to induce substitution mutation to a targeted amino acid of a 3D protein and tools to validate that the mutation is not a nonsense mutation that produces truncated protein?

I want to repair the missing amino acids of a protein structure and I cannot use Moddeller because there’s more than 20 residues to add, is there another option I can use?