PipeBio

🎉 We’re excited to announce that Benchling has acquired PipeBio! Together with Benchling, we’ll continue delivering innovation in bioinformatics for biologics discovery and more — on an open digital platform that’s connected to the rest of your R&D. We’re grateful to our customers and committed to continue serving them. We're excited about joining Benchling and expanding our vision to build the next generation of solutions for biologics R&D. https://lnkd.in/dwp4RnHi

We're in San Diego again at #Biologics US, soliciting runners for a leisurely run! Are you up for it?😉🏃♀️ Here's the link: https://lnkd.in/dhkSyT-s

CDRs comprising more than 50 residues organized in a stalk and a disulfide-rich knob? Yes, just a bovine CDR! This structural unit is distinct from the typical CDR length of 6-20 residues found in most antibodies, and the design features of this ultra-long CDR and its influence on antibody stability were previously unknown. Svilenov et al. have confirmed that the stalk length influences the folding and stability of antibodies with an ultra-long CDR, while the disulfide bonds in the knob do not contribute to stability. Instead, they are important for organizing the antigen-binding knob structure. The potential integration of bovine ultra-long CDRs into human antibody scaffolds presents an opportunity for developing novel therapeutic antibodies with enhanced properties. 3 significant factors influencing the stability, folding, and antigen-binding capabilities of these specialized antibodies: - Stalk Length: The study reveals that stalk length is critical for the folding and stability of antibodies with ultra-long CDRs. The stalk provides stability and contributes to the overall three-dimensional structure of the antibody. - Disulfide Bonds: While disulfide bonds in the knob structure do not significantly contribute to overall stability, they play an important role in organizing the antigen-binding knob structure. - Structural Organization: The presence of specific amino acid sequences in the stalk, along with its intrinsic structural features, confers stability and helps maintain a specific three-dimensional structure. Full article text here: https://lnkd.in/dKFYqCxb #bovinecdrs #antibodies #antibodystructure

Efficient analysis of full-length IgG antibodies can be achieved by pairing VH and VL chains post-sequencing. As example, in hybridoma sequencing, the heavy and light chains are often sequenced separately on different plates. After sequencing, the chains can be paired using unique identifiers, allowing for accurate downstream analysis, including structural prediction and functional assessment. The VH/VL pairing process can be executed seamlessly with PipeBio platform without the need for any programming expertise. Watch this video to learn how: https://lnkd.in/dU7tDpvJ

What are the main advantages of each in vitro display technology? Is the potential for the number of unique clones the same for each technology? As in vitro display technologies have led to the development of several clinically approved therapeutic antibodies, their use in research and therapeutic applications is expected to increase. Phage display remains the most widely used in vitro display technology due to its ease of use and versatility. Mammalian display offers several advantages, such as post-translational modifications. Ribosome display allows for cell-free synthesis of antibodies, while yeast display allows for native expression of antibodies. Bacterial display, although less commonly used, offers advantages such as low cost and ease of expression. Learn about differences and advantages of in vitro display technologies from the blogpost: https://lnkd.in/dyWGjgtM #antibodydisplaytechnologies #phagedispaly #invitrodisplay

Some photos from the largest #AntibodyAthletes run yet at The Antibody Series 2024 meeting (by FairJourney Biologics) on Madeira! Lots of steep hills on the island but the views were absolutely stunning. Again, we think it's the perfect way to start a day filled with top-notch #antibody keynotes and scientific discussions. The meeting continues today. Thank you to all runners for yesterday! 🙏 🏃♀️ 🏃♂️ #TAS24 #socialrun

Comparison of samples is an integral part of antibody drug discovery analysis after biopanning. It is important to learn if certain groups of antibodies are being expanded over time due to an immunization campaign. Check out how to go through all this steps in just couple clicks on Pipebio platform: https://lnkd.in/dgjGR8rq #biopanning #antibody #antibodydiscovery

One of the most important features of in silico antibody analysis is integration of the data from diverse sources into one analysis - sequences, structures, aggregation, binding propensities, and more. But the challenge lies in seeing the whole picture without being overwhelmed - when there is plenty of data it is hard to visualize or analyze, not even to mention finding any meaningful patterns. It’s just hard to decide where to look. Good data is the cornerstone of any successful prediction model, but how do you choose the right data to drive informed decisions in therapeutic candidate selection? Join Jannick Bendtsen at The Antibody Series 2024 on Madeira and learn how to gain predictive power from real-life screening and functional data with the PipeBio platform. Read more about THE ANTIBODY SERIES 2024 by FairJourney Biologics on https://lnkd.in/dsdRVya7

10 times smaller than a regular antibody, stable under a wide range of temperature and chemical conditions, easy to clone. Looks like a dream. Camelid VHHs can recognize distinct epitopes that are inaccessible to human antibodies - their small size also simplifies humanization, manufacturing, and tissue penetration.  However, establishing standardized analytical and characterization methods for these antibodies may pose challenges, as they differ structurally from conventional mAbs. Interested in the evolutionary landscape of structural adaptations behind all these features? Read the latest blogpost from Pipebio and check out the gene repertoire overview in Camelids! https://lnkd.in/dyWdJV6w

Assessing antibody off-target binding and complementing in-vitro assays using computational approaches may provide a cost-effective method for mitigating risks associated with polyreactivity. In a recent paper from Xin et al., the authors describe developinged an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (Antiberty). These models were trained using a transfer learning approach to predict the outcomes in the BVP and BSA assays. A large dataset of antibody sequences and experimental conditions was generated and augmented with information on antibody type (monospecific, bispecific, heavy-chain-only) and format. The training dataset included canonical monoclonal antibodies, bispecific antibodies, and single-domain Fc (VHH-Fc) antibodies. The resulting models demonstrated robust performance, with the ensemble model achieving an AUC of 0.871 on the validation and test sets. Importantly, the models were able to handle the diverse range of antibody formats, including 12 different bispecific subtypes, with the bispecific-specific model performing particularly well (AUC of 0.906). Embeddings from the antibody-specific and foundational protein language models resulted in similar predictive performance, suggesting that the pan-protein models can effectively capture the relevant features for polyreactivity prediction. Based on the relationship between polyreactivity and antibody properties, it was found that antibody concentration, hydrophobicity, and charge were important factors contributing to polyreactivity. Moreover, The PLMs, specifically Antiberty, ProtT5, and ESM2, were able to capture complex sequence and structural features as embeddings, allowing for more accurate prediction of assay outcomes compared to models based on molecular descriptors derived from AlphaFold 2 predicted structures. This suggests that the PLMs were able to effectively learn and represent the nuances of antibody sequences and structures, leading to enhanced performance in predicting polyreactivity. Full article text: https://lnkd.in/dZjmGZFG #polyreactivity #antibodies #computationalbiology