RESUMO
Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.
Assuntos
Anticorpos Monoclonais , Engenharia de Proteínas , Viscosidade , Engenharia de Proteínas/métodos , Anticorpos Monoclonais/química , Anticorpos Monoclonais/imunologia , Aprendizado de Máquina , Sequência de Aminoácidos , HumanosRESUMO
Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.
Assuntos
Anticorpos Monoclonais , Descoberta de Drogas , Animais , Camundongos , Anticorpos Monoclonais/química , Simulação por Computador , Proteínas Recombinantes , ViscosidadeRESUMO
Background: The use of Monoclonal Antibodies (MAbs) as therapeutics has been increasing over the past 30 years due to their high specificity and strong affinity toward the target. One of the major challenges toward their use as drugs is their low thermostability, which impacts both efficacy as well as manufacturing and delivery. Methods: To aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. To improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in >800 MAbs' 3D structures. Results: Combining consensus sequence and structural residue pair covariance methods, we developed an in-house application for predicting human MAb thermostability to guide protein engineers to design stable molecules. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. This application has shown success in designing MAb engineering panels in multiple biologics programs. Conclusions: Our data science-based method shows impacts in Mab engineering.
RESUMO
Maple sap is a complex nutrient matrix collected during spring to produce maple syrup. The characteristics of sap change over the production period and its composition directly impacts syrup quality. This variability could in part be attributed to changes in tree metabolism following dormancy release, but little is known about these changes in deciduous trees. Therefore, understanding the variation in sap composition associated with dormancy release could help pinpoint the causes of some defects in maple syrup. In particular, a defect known as "buddy", is an increasing concern for the industry. This off-flavor appears around the time of bud break, hence its name. To investigate sap variation related to bud break and the buddy defect, we monitored sap variation with respect to a dormancy release index (Sbb) and syrup quality. First, we looked at variation in amino acid content during this period. We observed a shift in amino acid relative proportions associated with dormancy release and found that most of them increase rapidly near the point of bud break, correlating with changes in syrup quality. Second, we identified biological processes that respond to variation in maple sap by performing a competition assay using the barcoded Saccharomyces cerevisiae prototroph deletion collection. This untargeted approach revealed that the organic sulfur content may be responsible for the development of the buddy off-flavor, and that dormancy release is necessary for the appearance of the defect, but other factors such as microbial activity may also be contributing.
Assuntos
Acer/química , Dormência de Plantas , Exsudatos de Plantas/química , Edulcorantes/normas , Biologia de Sistemas/métodos , Acer/fisiologia , Qualidade dos Alimentos , Armazenamento de Alimentos , Estações do Ano , Paladar , Árvores/química , Árvores/fisiologiaRESUMO
Phosphotyrosine (pTyr) signaling has evolved into a key cell-to-cell communication system. Activated receptor tyrosine kinases (RTKs) initiate several pTyr-dependent signaling networks by creating the docking sites required for the assembly of protein complexes. However, the mechanisms leading to network disassembly and its consequence on signal transduction remain essentially unknown. We show that activated RTKs terminate downstream signaling via the direct phosphorylation of an evolutionarily conserved Tyr present in most SRC homology (SH) 3 domains, which are often part of key hub proteins for RTK-dependent signaling. We demonstrate that the direct EPHA4 RTK phosphorylation of adaptor protein NCK SH3s at these sites results in the collapse of signaling networks and abrogates their function. We also reveal that this negative regulation mechanism is shared by other RTKs. Our findings uncover a conserved mechanism through which RTKs rapidly and reversibly terminate downstream signaling while remaining in a catalytically active state on the plasma membrane.