#inhibitor

[amber-tortoiseshell]

so i saw this cat on pinterest, the caption claimed it was an amber tabby, but a commenter (who apparently runs a cattery?) said it was a smoke because "amber tabbies don't have black noses"

but those stripes look way too defined to be smoke to me, and i've seen other sources claim that a black nose is a defining feature of amber tabbies? so what color is that cat

also i found another tabby with a black nose but this one doesn't look like any ambers i've seen do you know what it is by chance

The first one is a solid (aa) amber, not a tabby. Its name is Kingsley, and here's the original picture on instagram. (Amber is like red, solids are very strongly ghostmarked, and the black nose is a defining feature of solid ambers.) I don't think it's a smoke, the colors are consistent with regular amber, and amber smokes are usually softer and cooler toned.

Jussi Sølv von der Lusshardt, Galaxy vom Ritterclan

Ironically your second cat is actually an amber smoke, Krusmons Golden Gate. Probably photographed young while the color is still in development.

Amber tabbies, like my Galadriel, have pink noses surrounded with yellow and white fur:

Here is a poor picture I took of her half-brother, Trygg, and her mother, Viva:

This is the difference between a tabby and a solid amber.

[identifying-cat-phenotypes]

please identify my son. his name is Applewood Smoked Bacon and he has every disease

this beautiful boy looks like a black smoke with ghost striping! such a pretty cat!

[whats-in-a-sentence]

In other work, the Brimble group recently accomplished more efficient synthesis of gamma-rubromycin (figure 21.8), a potent inhibitor of human telomerase.

"Chemistry" 2e - Blackman, A., Bottle, S., Schmid, S., Mocerino, M., Wille, U.

[deadfileinthecabinet]

It’s also known as HVM-5. Known to enhance human abilities, but the first injection usually causes seizures, cardiac arrest and sometimes, death.

[phenotyping-cats]

Black silver classic tabby tortoiseshell bicolor

Stray cat breaks into Lynx’s enclosure at zoo

(Source)

[medchemexpressinhibitor-blog]

AI Deep Learning Accelerates Drug Development

Deep learning is a subset of artificial intelligence (AI) that mimics the neural networks of the human brain to learn from large amounts of data, enabling machines to solve complex problems. Deep learning technology has made significant progress in the biomedical field. Researchers have developed a series of application based on deep learning for disease diagnosis, protein design, and medical image recognition. The pharmaceutical industry is also beginning to recognize the importance of deep learning technology, hoping to leverage it to accelerate drug development and reduce costs.

1. Application of Deep Learning in Drug Development

Previous studies have demonstrated that deep learning technology offers significant advantages in several key areas of drug development, including optimization of chemical synthesis routes, ADME-Tox prediction, target identification and validation and generation of novel molecules.

Figure 1. A broad overview of drug development and the place of virtual screening in this process[1].

1.1 Virtual Screening: Protein-Ligand Affinity

Deep learning can learn and identify potential binding patterns by comparing known protein-small molecule binding instances. During the training process, the deep learning models continuously optimize their parameters to enhance the accuracy and reliability of their predictions.

Yelena Guttman et al. developed a CYP3A4 inhibitor prediction model based on DeepChem framework. They created a KNIME workflow for data curation and employed the DeepChem module in Maestro to build a categorical classifier. This classifier was then used to virtually screen approximately 68,900 compounds from the FooDB database, leading to the successful identification of two new CYP3A4 inhibitors[2].

Figure 2. Prediction of CYP3A4 Inhibitors Based on DeepChem[2].

A workflow in KNIME analytics platform 4.0.314 was created to prepare and analyze the virtual screening.

1.2 ADME-Tox Prediction

Poor pharmacokinetic properties as well as toxicity issues are considered the main reasons for terminating the development process for drug candidates. Thus, there is an increasing need for robust screening methods to provide early information on absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) properties of compounds. Many studies have shown by leveraging these extensive ADME datasets, deep learning models can automatically identify and extract complex relationships between compound features and their corresponding ADMET properties. These trained models can then be used to predict the ADME properties of new compounds, thereby accelerating the process of drug discovery and development.

Liu et al. utilized directed message passing neural networks (D-MPNN, Chemprop) to predict the Nrf2 dietary-derived agonists and safety of compounds in the FooDB database. They successfully identified Nicotiflorin, a drug that exhibits both agonistic activity of Nrf2 and safety, which was validated in vitro and in vivo[3].

Figure 3. Using Deep-Learning Model D-MPNN to Assess Drug Safety[3].

1.3 Optimize Chemical Synthesis Routes

In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Deep learning is increasingly being applied to chemical synthesis, enabling the automatic identification and extraction of features and patterns from large datasets. This capability enhances the prediction of the efficiency and selectivity of new synthesis routes, significantly accelerating drug development and production.

Li et al. introduced a novel reaction representation, GraphRXN, for reaction prediction. G

Figure 4. A deep-learning graph framework, GraphRXN, was proposed to be capable of learning reaction features and predicting reactivity[4].

2. Drug Screening Based on Deep Learning

The application of deep learning in the field of virtual screening primarily involves using neural networks to predict the activity or properties of compounds, thereby identifying potential candidate drugs or materials in a virtual environment. Commonly used deep learning models include Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN) and Transformer models.

 CNNs excel at identifying patterns and features in structured data, such as chemical structures represented as images or graphs. Recent studies have demonstrated their effectiveness in predicting drug-drug interactions and assessing molecular properties by analyzing chemical substructures and other relevant features.

 GNNs are designed to work directly with graph-structured data, making them particularly suitable for representing molecular structures where atoms are nodes and bonds are edges. They have shown remarkable performance in drug discovery by capturing the complex relationships between molecules and their properties.

 RNNs are designed to handle sequential data, making them particularly effective for tasks where context from previous inputs is essential.

 GANs consist of two neural networks—a generator and a discriminator—that work against each other to create new data instances.

 Transformers have gained popularity for their ability to handle sequential data and capture long-range dependencies, making them suitable for tasks like natural language processing and time-series analysis.

In summary, deep learning is revolutionizing drug development by enhancing efficiency, accuracy, and cost-effectiveness across multiple stages of the process. As technology continues to evolve, its integration into pharmaceutical research is likely to deepen, paving the way for innovative therapeutic solutions.

Product Recommendation

Virtual Screening

MedChemExpress (MCE) provides high quality virtual screening service that enables researchers to identify most promising candidates. Based on the laws of quantum and molecular physics, our virtual screening services can achieve highly accurate results. Our optimized virtual screening protocol can reduce the size of chemical library to be screened experimentally, increase the likelihood to find innovative hits in a faster and less expensive manner, and mitigate the risk of failure in the lead optimization process.

50K Diversity Library

MCE 50K Diversity Library consists of 50,000 lead-like compounds with multiple characteristics such as calculated good solubility (-3.2 < logP < 5), oral bioavailability (RotB <= 10), drug transportability (PSA < 120). These compounds were selected by dissimilarity search with an average Tanimoto Coefficient of 0.52. There are 36,857 unique scaffolds and each scaffold 1 to 7 compounds. What’s more, compounds with the same scaffold have as many functional groups as possible, which make abundant chemical spaces.

MegaUni 10M Virtual Diversity Library

With MCE's 40,662 BBs, covering around 273 reaction types, more than 40 million molecules were generated. Compounds which comply with Ro5 criteria were selected. Inappropriate chemical structures, such as PAINS motifs and synthetically difficult accessible, were removed. Based on Morgan Fingerprint, molecular clustering analysis was carried out, and molecules close to each clustering center were extracted to form this drug-like and synthesizable diversity library. These selected molecules have 805,822 unique Bemis-Murcko Scaffolds (BMS) with diversified chemical space. This library is highly recommended for AI-based lead discovery, ultra-large virtual screening and novel lead discovery.

MegaUni 50K Virtual Diversity Library

MegaUni 50K Virtual Diversity Library consists of 50,000 novel, synthetically accessible, lead-like compounds. With MCE's 40,662 Building Blocks, covering around 273 reaction types, more than 40 million molecules were generated. Based on Morgan Fingerprint and Tanimoto Coefficient, molecular clustering analysis was carried out, and molecules closest to each clustering center were extracted to form a drug-like and synthesizable diversity library. The selected 50,000 drug-like molecules have 46,744 unique Bemis-Murcko Scaffolds (BMS), each containing only 1-3 compounds. This diverse library is highly recommended for virtual screening and novel lead discovery.

References

[1] Rifaioglu AS, et al.Brief Bioinform. 2019 Sep ;20(5):1878-1912.

[2] Guttman Y, et al.J Agric Food Chem. 2022 Mar ;70(8):2752-2761.

[3] Liu S, et al.J Agric Food Chem. 2023 May ;71(21):8038-8049.

[4] Li B, et al.J Cheminform. 2023 Aug;15(1):72.

[5] Segler MHS, et al. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar ;555(7698):604-610.

[amber-tortoiseshell]

Is there a significant phenotypic difference between red silver tabbies and red smokes? Does the inhibitor present the same way it does in a red self as it does in a red tabby because they’re both visually tabby? 

The difference is the same as between red tabbies and red 'selfs': usually but not always dark chin, mostly.

I pulled these two from the internet, so can't be sure, but the first is probably a red silver tabby (A_) and the second is probably a red smoke (aa).

[identifying-cat-phenotypes]

maybe not the right place to ask, but do you know why Cricket has these grey patches that come and go? otherwise he appears solid black

SMOKE ALERT!!!!!!!!

smoke cars have a white undercoat that looks grey from certain angles!!! this awesome beautiful kitty is a black smoke self!

[phenotyping-cats]

Black silver rosetted tabby

[whats-in-a-sentence]

Australine was isolated from the seeds of the Australian tree Castanospermum australe (figure 19.17) and is also an inhibitor of glycoprocessing enzymes.

"Chemistry" 2e - Blackman, A., Bottle, S., Schmid, S., Mocerino, M., Wille, U.

[dtpnews]

Eine Malaria-Droge der nächsten Generation: Neue epigenetische Inhibitor tötet den tödlichsten Parasit ab

Epigenetische Inhibitoren: Eine vielversprechende neue Strategie für die Antimalaria -Behandlung? Eine kürzlich durchgeführte Studie entdeckt einen Genregulationsinhibitor, der den Malaria -Parasiten selektiv beseitigt. Ein multinationales Forschungsteam unter der Leitung von Professor Markus Meißner von LMU München und Professor Gernot Länder von der University of Regensburg hat bedeutende…

[phenotyping-cats]

Black silver mackerel tabby tortoiseshell bicolor

[identifying-cat-phenotypes]

This 1cm fella seems to be a black smoke! What a pleasant fella!

DISCLAIMER! This is a hobbyist's identification, please send an ask or reply with any corrections or thoughts on this cat!

[medchemexpressinhibitor-blog]

Recombinant Protein Reconstitution Techniques: Key Steps to Enhance Experimental Efficiency

Reconstitution and Storage

1 Centrifuge the tube before opening

During shipment, the protein may adhere to the wall or cap of the vial. Before opening the vial, please centrifuge at 10,000-12,000 rpm for 30 seconds to gather the protein at the bottom of the vial. If a high-speed centrifuge is not available, please centrifuge at 3,000-3,500 rpm for 5 mins.

2 After centrifugation, add the reconstitution buffer to the lyophilized protein powder and mix gently by pipetting. Resuspend in the reconstitution buffer to recommended concentration (no less than 100 μg/mL).

Note: Vigorous vortexing should be avoided as it can cause protein foaming and denaturation, thereby affecting the protein activity.

3 Once reconstituted, recombinant proteins can be stored no more than a week at 2-8°C.

For experiments with a short cycle (no more than 7 days), the recombinant protein solution can be directly added to the culture system and used up within a week. If the experimental concentration is lower than the reconstituted concentration, dilution can be done with a solution containing carrier proteins.

4 For long-term storage, the protein solution should be diluted further with carrier proteins (0.1% BSA, 5% HAS, 10% FBS or 5% trehalose), and then aliquot and stored at -20°C to -80°C.

It is not recommended to freeze the reconstituted product directly at -20°C to -80°C. Some recombinant proteins may stick to the plastic tube wall easily, which results in a lower concentration of protein in the solution and ultimately reduces its activity. Carrier proteins can prevent products from sticking to the tube wall by pre-blocking

the protein binding site. Therefore, for long-term storage, cytokines or proteins should be further diluted with the solution containing carrier proteins before making aliquots and freezing.

Note: Avoid repeated freeze/thaw cycles. Each freeze/thaw cycle will cause denaturation or conformational changes in some proteins, thereby reducing the binding ability of antibodies and accelerating protein degradation.

Cytokines and Growth Factors

Cytokines are a large class of low molecular weight proteins, peptides, or glycoproteins that are secreted by various types of immune cells such as macrophages and lymphocytes, as well as other cell types such as endothelial cells. They play an important role in regulating cell growth, differentiation, and activation and are involved in many aspects of the innate and adaptive immune response[1][2][3]. Growth factors are soluble signaling molecules that stimulate various cellular processes during development and tissue healing, including cell proliferation, migration, differentiation, and multicellular morphogenesis[4][5]. The terms "growth factors" and "cytokines" are often used interchangeably[5][6].Human IGF-I (HY-P7018) The purity of human IGF-I is greater than 95% as analyzed by SDS-PAGE under reducing (R) conditions.

[amber-tortoiseshell]

i thought the inhibitor removed pheomelanin so how does it interact with orange like what is going on like chemically on a red silver tabby???

Unfortunately we just don't know that yet. We understand what agouti (ASIP) and what orange (Arhgap36) does, but inhibitor is still a mystery.

source, source

Maybe the inhibitor effect is somehow linked to high asip levels, and the gene only inhibits the light agouti bands. Who knows.

[identifying-cat-phenotypes]

The cat i made with this! Shes a chocolate silver lynx point with white!

go check the picrew out! it includes golden tabbies (which i dont see too often in cat genetics related content) and im excited to see everyone's kitties! Reblog with a kitty you made :3

HI... I've made another extremely self indulgent picrew

I made the majority of this in one night and polished it for another two days, this thing was built purely in a cat genetics autism fueled need to see if it was possible to make

it's not a perfect visual representation of all possible cat colours, there's only so much I can do within picrew's simple image layering, but I've done my best to make most colours look close to real life or at least nice looking

there's instructions included if you want to use it the way I built it to be used, but they're completely optional and you're welcome to just mess around with it with no rules if you prefer!

-> https://picrew.me/image_maker/2446358