Xantho biotechnology

Xantho Biotechnology is a biotech company that specializes in the development of novel therapeutics for a range of diseases. The company is based in California and was founded in 2017 by a team of experienced researchers and entrepreneurs.

Xantho Biotechnology’s research focuses on the discovery and development of small molecules that can modulate the activity of key proteins involved in disease pathways. The company’s drug discovery platform uses a combination of computational modeling, high-throughput screening, and in vitro and in vivo testing to identify promising drug candidates.

One of the main areas of focus for Xantho Biotechnology is the development of new treatments for cancer. The company is developing a pipeline of small molecule drugs that target specific signaling pathways involved in cancer growth and metastasis. These drugs have the potential to be more effective and have fewer side effects than traditional chemotherapy drugs.

In addition to cancer research, Xantho Biotechnology is also involved in the development of therapeutics for other diseases, such as neurodegenerative diseases and autoimmune disorders. The company is developing small molecule drugs that target specific protein-protein interactions that are involved in the development and progression of these diseases.

Xantho Biotechnology has established partnerships with a number of academic institutions and biotech companies to advance its research and development efforts. For example, the company has partnered with the University of California, San Francisco to develop new treatments for liver cancer, and with the biotech company Atomwise to use artificial intelligence to accelerate drug discovery.

Overall, Xantho Biotechnology is a promising biotech company that is focused on the development of novel therapeutics for a range of diseases. With its strong research and development capabilities, and its partnerships with leading academic and industry partners, the company is well-positioned to make important contributions to the biotech industry in the coming years.

 

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Xutai biotechnology co. limited

Xutai Biotechnology Co. Limited is a biotech company based in China that focuses on the research, development, and production of biological products, including vaccines and diagnostics. The company was founded in 2012 and has since grown to become a leading biotech company in China.

Xutai Biotechnology’s main areas of focus include the development and production of vaccines for infectious diseases such as COVID-19, hepatitis B, and influenza. The company’s vaccines are produced using advanced biotechnology techniques and are designed to be safe, effective, and affordable.

In addition to vaccine development, Xutai Biotechnology also develops and produces diagnostic products for a range of diseases. The company’s diagnostic products include test kits for infectious diseases, such as COVID-19, as well as tumor markers and other diagnostic assays.

Xutai Biotechnology has established partnerships with a number of academic institutions and biotech companies to advance its research and development efforts. For example, the company has partnered with the China Academy of Medical Sciences to develop a COVID-19 vaccine, and with the biotech company Cansino Biologics to develop a hepatitis B vaccine.

Xutai Biotechnology has received numerous awards and accolades for its work in the biotech industry. In 2020, the company was recognized as one of the Top 100 Chinese Pharmaceutical Companies by the China National Pharmaceutical Industry Information Center. The company was also listed on the Shanghai Stock Exchange in 2020, which has provided additional funding for its research and development efforts.

Overall, Xutai Biotechnology is a leading biotech company in China that is focused on the development and production of vaccines and diagnostics for a range of diseases. With its strong research and development capabilities, and its partnerships with leading academic and industry partners, the company is well-positioned to make important contributions to the biotech industry in China and beyond.

 

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Xpand biotechnology

Xpand Biotechnology is a biotech company that specializes in the development of novel therapies for a range of diseases. The company is based in Israel and was founded in 2018 by a team of experienced biotech entrepreneurs and researchers.

Xpand Biotechnology’s research focuses on the discovery and development of small molecule drugs that can modulate the activity of key proteins involved in disease pathways. The company’s drug discovery platform uses a combination of computational modeling, high-throughput screening, and in vitro and in vivo testing to identify promising drug candidates.

One of the main areas of focus for Xpand Biotechnology is the development of new treatments for cancer. The company is developing a pipeline of small molecule drugs that target specific signaling pathways involved in cancer growth and metastasis. These drugs have the potential to be more effective and have fewer side effects than traditional chemotherapy drugs.

In addition to cancer research, Xpand Biotechnology is also involved in the development of therapeutics for other diseases, such as autoimmune disorders and viral infections. The company is developing small molecule drugs that target specific protein-protein interactions that are involved in the development and progression of these diseases.

Xpand Biotechnology has established partnerships with a number of academic institutions and biotech companies to advance its research and development efforts. For example, the company has partnered with the Weizmann Institute of Science to develop new treatments for multiple sclerosis, and with the biotech company Enlivex Therapeutics to develop therapies for sepsis and cytokine storm syndrome.

Overall, Xpand Biotechnology is a promising biotech company that is focused on the development of novel therapies for a range of diseases. With its strong research and development capabilities, and its partnerships with leading academic and industry partners, the company is well-positioned to make important contributions to the biotech industry in the coming years.

 

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Do antibodies have epitopes? Immunoinformatics definition: Short fast 1 minute answer!

Articles about Immunoinformatics Free Article immunoinformatics short article

Immunoinformatics as a new integrated field of science concentrate on the finding epitope, antigens and pathogenesis mechanism.

An epitope, also known as antigenic determinant, is the part of an antigen that is recognized by the immune system, specifically by antibodies, B cells, or T cells. The epitope is the specific piece of the antigen to which an antibody binds.

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Why is Immunoinformatics important? Short fast 1 minute answer!

Immunoinformatics is a field of Bioinformatics science concentrating on the molecules in the immunology system.

Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology

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SeRenDIP-CE : Epitope Prediction Tool

New immunoinformatics tools On the sequence-side by Dr Anton Feenstra presented that is a new solution called SeRenDIP-CE, a random forest method for predicting epitopes from sequence

This method is based on deriving a range of features from the antigen amino-acid sequence (172 features across a sliding window of 9 amino acids, incorporating MSA-derived information) and training a random forest using those features to predict epitopes on a set of antigens collected from our very own Structural Antibody Database (SAbDab).

Interestingly, SeRenDIP-CE made use of a transfer learning approach of sorts, by combining in their train set data both from antibody-antigen interactions and from general hetero-dimer protein-protein interactions (PPIs) used for their previous SeRenDIP model (I say transfer learning of sorts, because the model is trained once on the full combined dataset, rather than updating the model with the more specific antigen dataset after training on the hetero-dimer dataset). The authors reported this training procedure to achieve considerably better results than training on either just the antigen dataset or just the hetero-dimer dataset.

This has some interesting implications for general development of machine learning methods for antibody-antigen interactions, as it implies that, despite the dissimilarity of binding modes in antibody-antigen interactions compared to general PPIs, antibody machine learning methods can benefit from dataset augmentation from such PPI datasets.

 

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What is Immunoinformatics ?

Today, New integrated scientific fields, change our vision to future and mechanism of life, And immunoinformatics as a new field of science. Big data relevant to immunology researches has been accumulated due to sequencing of genomes of the human and other model organisms for understanding mechanism of life and protecting life from threats.. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records on different researches and platforms. This accumulation of the information is like a gold mine for researchers looking for mechanisms of immune structure and function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics.

Immunoinformatics, otherwise known as computational immunology, is the integrated science between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses.

This science have a review on classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

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Epitope 3D: Epitope prediction tool

This software Presented by “Bruna Moreira da Silva”, Epitope3D is an epitope prediction tool using graph-based signatures, developed at the “University of Melbourne“, which is an example of a structure-based approach to epitope prediction. It was developed and trained using a curated, non-redundant set of 200 antigen structures with marked epitopes.

Individual residues, labeled according to whether they belong to the epitope, are characterized using graph-based signatures of the neighborhood of each residue, representing geometry and chemical composition of the environment, making it a similar approach to recent attempts to utilize geometric features in protein model assessment and property prediction.

The resulting signatures are used as input to a Adaboost classifier, which is tested against a set of 45 held-out antigens. In a comparison study against several other epitope prediction tools, Epitope3D boosted impressive classification performance. It will be interesting to see further evaluation of this tool.

 

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Coronavirus Antibody Database: Increasingly growth report

In May 2020, Group of students from Oxford university released the Coronavirus Antibody Database (‘CoV-AbDab’) to Manage and capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, They had found around 300 relevant antibody sequences and a handful of solved crystal strTuctures, most of which were characterized shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain

10 months later (2nd March 2021), They have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These data points originate from 100s of independent studies reported in either the academic literature or patent filings.

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Short review to OPIG Antibody Suite v2.0

OPIG is one of useful tools available for analyzing Antibody data and documented in research has expanded its range of tools for antibody/BCR analysis. Here is an updated summary of the OPIG antibody databases and immunoinformatics tools for 2021

NB: Several of our databases/tools [SAbDab, Thera-SAbDab, ABodyBuilder, PEARS, FREAD, Sphinx, ANARCI, Antibody iPatch, EpiPred, SCALOP, TAP] are now packaged in a Virtual Box called SAbBox. SAbBox is available under a free academic or a paid commercial license you can download and use it here.

SAbBox Academic License: https://process.innovation.ox.ac.uk/software/p/15303a/sabbox-academic/1
SAbBox Commercial License: https://process.innovation.ox.ac.uk/software/p/15303/sabbox/1

Databases

1. The Structural Antibody Database (SAbDab)
Link: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/
Paper: https://academic.oup.com/nar/article/42/D1/D1140/1044118

SAbDab mines the PDB for antibody/nano-body structures, annotating them with metadata. It can be searched for:

  • A PDB code
  • PDB codes that match a series of metadata (resolution cutoffs, species, bound/unbound, has affinity data etc.)
  • CDRs that match a series of metadata
  • PDB codes with a particular VH-VL orientation

2. The Therapeutic Structural Antibody Database (Thera-SAbDab)
Link: http://opig.stats.ox.ac.uk/webapps/newsabdab/therasabdab/search/
Paper: https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz827/5573951

Thera-SAbDab mines the WHO International Non-proprietary Name Publications and SAbDab to provide the latest sequence and structural data and metadata for all therapeutic antibody/nanobody formats. Sequence and attribute searches available.

3. The Coronavirus Antibody Database (CoV-AbDab)
Link: http://opig.stats.ox.ac.uk/webapps/covabdab/
Paper: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa739/5893556

CoV-AbDab mines the scientific literature (papers, preprints) and patents to pool together sequences of all antibodies/nanobodies proven by experimental assay to bind at least one coronavirus (e.g. SARS-CoV, MERS-CoV, SARS-CoV-2) antigen. Structural information is also provided through mining of SAbDab, and homology models are built for full Fv sequences for which no solved structures currently exist. Sequence and attribute searches available.

4. Observed Antibody Space (OAS)
Link: http://opig.stats.ox.ac.uk/webapps/oas/
Required Input: N/A (Database)
Paper: http://www.jimmunol.org/content/201/8/2502

OAS (Observed Antibody Space) is a quality-filtered, consistently-annotated database of full-chain BCR/antibody sequence data. All sequences are provided pre-numbered in the IMGT numbering scheme and annotated with potential liabilities. Every dataset is freely downloadable; most downloads are fully minimum Adaptive Immune Receptor Repertoire Community Standard (MiAIRR) compliant and the remainder will be updated in the coming months. Here you can:

  • Filter sequencing data by study or filter the data across studies
  • Look at snapshots of the immune repertoire in specific disease states (e.g. healthy, day 7 after vaccination, HIV positive)
  • Analyse different isotype properties
  • Analyse different species properties, and much more…

A recent development is that OAS has been adapted to include single cell VDJ sequencing data; see more in Aleks Kovaltsuk’s latest blogpost: https://www.blopig.com/blog/2020/09/adding-paired-bcr-data-to-oas/

BCR/Antibody Structural Modelling Tools

5. VH-VL Orientation (AbAngle)
Code: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/abangle/
Required Input: Variable domain (Fv) structure
Paper: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/abangle/

AbAngle can characterise Fv VH/VL orientation through a combination of 5 angles and 1 distance measurement.

6. Loop Canonical Form Classifier (SCALOP)
Code: https://github.com/oxpig/SCALOP
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/scalop
Required Input: (Paired/Separate Chain) Antibody Variable Domain Sequence(s)
Paper: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty877/5132697

Five of the CDRs (CDRH1-2, CDRL1-3) are found to fall into distinct, clusterable, canonical structures. SCALOP uses environment-specific substitution matrices to assign likely canonical form from sequence alone. Its high fidelity and speed ensure that this analysis can be performed even on very large datasets (e.g. as part of SAAB+).

7. BCR/Antibody Loop Homology Modelling (FREAD)
Code: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/fread/
Required Input: CDR Sequence + Framework structure (on which the loop will be grafted)Paper: https://pubs.rsc.org/en/content/articlelanding/2011/MB/c1mb05223c

FREAD uses Environment-Specific Substitution Tables to evaluate a score for how compatible the dihedral angles of each template loop would be for the target CDR sequence. It then ranks putative loop templates by ease of graftability onto the chosen framework region (least clash ranks first). FREAD was originally designed for general loop modelling, but can be made CDR-specific by building separate antibody CDR loop template databases.

8. Hybrid CDRH3 Modelling (Sphinx H3)
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/sphinx
Required Inputs: CDR Sequence + Framework structure (on which the loop will be grafted)
Paper: https://academic.oup.com/bioinformatics/article/33/9/1346/2908432

Sphinx is useful when your homology modeller (e.g. FREAD) cannot find a close template match for your loop of interest. It uses a combination of shorter, sequence similar, template data and ab initio methodology to fill in the gaps. It then returns its decoys ranked using the SOAP-loop algorithm.

9. Side-Chain Modelling (PEARS)
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/pears
Required Inputs: Antibody Variable Domain Structure + Corresponding Sequence (with side chains to be remodelled in capital letters)
Paper: https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25453

PEARS remodels side chains by using Gaussian Mixture Models to predict the most probable rotamer for each remodelled residue, given its position in the antibody sequence.

10. Full Fv Modelling Pipeline (ABodyBuilder)
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/abodybuilder/
Required Inputs: (Paired chain) Antibody Variable Domain Sequence (will model Nanobodies if only heavy chain supplied)
Paper: https://www.tandfonline.com/doi/full/10.1080/19420862.2016.1205773

ABodyBuilder chains together ANARCI – FREAD – Modeller/Sphinx (if FREAD fails to find a good loop template) – PEARS as a pipeline to create antibody models from sequence data. It also reports likely model accuracy for each region of the structure. Typical runtime is just over 30s for most antibodies.

BCR/Antibody Informatics Tools

11. BCR/Antibody Sequence Numbering (ANARCI)
Code: https://www.biorxiv.org/content/10.1101/2020.03.24.004051v2
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/anarci/
Required Input: (Separate Chain) Antibody Variable Domain Sequence(s)
Paper: https://academic.oup.com/bioinformatics/article/32/2/298/1743894

Consistent use of a numbering scheme is essential to quickly identify CDR regions or to compare between multiple antibodies. ANARCI uses Hidden Markov Models to align your sequences to germlines of known numbering, and rapidly returns them numbered in the scheme of choice (IMGT, Chothia, Kabat, Martin).

12. BCR-seq Dataset Error Annotation (ABOSS)
Code: http://opig.stats.ox.ac.uk/resources
Required Input: (Separate Chain) Antibody Variable Domain Sequences
Paper: https://www.jimmunol.org/content/early/2018/11/02/jimmunol.1800669

ABOSS highlights potential sequencing errors in BCR-seq datasets. Sequences that do not align to germlines (see ANARCI), have IMGT CDRH3 lengths ≥ 37, possess indels in the canonical CDRs or framework regions, start at IMGT position 24 or later, or have a J gene with sequence identity < 50% to known IMGT germlines are removed. The estimated error rate for your dataset is then calculated based on how often the C23-C104 (IMGT numbering) conserved disulfide bridge is missing from your data. Sequences with residues seen at a given position less frequently than the estimated error rate are then filtered out of the dataset.

13. BCR-seq Dataset Structural Annotation (SAAB+)
Code: https://github.com/oxpig/saab_plus
Required Inputs: (Separate Chain) BCR/Antibody Sequences
Paper: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007636

SAAB+ (developed from the original SAAB software, see https://www.frontiersin.org/articles/10.3389/fimmu.2018.01698/full) rapidly annotates BCR-seq datasets with structural features (e.g. CDRH1-2, CDRL1-3 canonical forms; closest CDRH3 template in the PDB).

14. BCR-seq Fv Structural Diversity Assessment (Repertoire Structural Profiling)
Required Inputs: BCR-seq Dataset (VH + VL, separate or paired)
Preprint: https://www.biorxiv.org/content/10.1101/2020.03.17.993444v2

Repertoire Structural Profiling converts BCR-seq datasets containing VH and VL reads (paired or unpaired) into the maximum diversity of modellable Fv structures that can currently be derived from those sequences. Once fully structurally modelled, these sets of Fv topologies are termed “Antibody Model Libraries”. These Antibody Model Libraries can be used as a geometric basis set for in vitro/in silico screening library development.

15. BCR/Antibody Paratope Prediction (Antibody iPatch)
Webserver: http://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/ABipatch.php
Required Inputs: Antibody and Antigen structures
Paper: https://academic.oup.com/peds/article/26/10/621/1512673

Antibody i-Patch uses contact prediction, antibody binding profiles (derived from PDB antibody-antigen complexes), and the supplied antibody and antigen structures to rank antibody CDR residues based on their propensity to form part of the paratope [the region of the antibody that engages the antigen]. The inverse protocol (where antigen residues are ranked on their likelihood to form part of the epitope) is available here: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/epipred/

16. Predicted Paratope Clustering for Functional Annotation (Paratyping)
Code: http://opig.stats.ox.ac.uk/resources
Required Inputs: BCR/Antibody VH sequence
Preprint: https://www.biorxiv.org/content/10.1101/2020.06.02.121129v1

Paratyping uses the Parapred tool to mark up predicted paratope residues and then clusters BCR/antibody sequences by sequence identity over these residues. This tool is an orthogonal approach to clonotyping for repertoire functional analysis.

17. Paratope Structural and Chemical Similarity Assessment (Ab-Ligity)
Code: http://opig.stats.ox.ac.uk/resources
Required Inputs: BCR/Antibody VH or Fv sequence
Preprint: https://www.biorxiv.org/content/10.1101/2020.03.24.004051v2

Ab-Ligity uses Parapred to predict paratope residues and ABodyBuilder to model the antibody Fv structure. It then converts these paratope residues and their coordinates into a hash-table representation that captures both structural and chemical features. A sufficiently high Tversky index value between two hash tables infers that two antibodies have paratopes similar enough to one another that they might be functionally equivalent.

18. Therapeutic Antibody Developability Assessment (TAP)
Webserver: http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred/tap
Required Input: Antibody Variable Domain Sequence (Paired Chains)
Paper: https://www.pnas.org/content/116/10/4025

TAP builds a model of your variable domain antibody sequence (via. ABodyBuilder) and calculates several surface properties, extreme values of which are linked to poor therapeutic developability outcomes. It then compares these values to a set of therapeutic antibodies that reached Phase II of clinical trials and flags outlying candidates.

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