Xencor, 111 West Lemon Avenue, Monrovia, CA 91016, USA.
Published: December 2002
AI Article Synopsis
Article Abstract
We present a combined computational and experimental method for the rapid optimization of proteins. Using beta-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of approximately equal 200,000 mutants. These were then constructed and experimentally screened to select for variants with improved resistance to the antibiotic cefotaxime. In a single round, we obtained variants exhibiting a 1,280-fold increase in resistance. To our knowledge, all of the mutations were novel, i.e., they have not been identified as beneficial by random mutagenesis or DNA shuffling or seen in any of the naturally occurring TEM beta-lactamases, the most prevalent type of Gram-negative beta-lactamases. This combined approach allows for the rapid improvement of any property that can be screened experimentally and provides a powerful broadly applicable tool for protein engineering.
Despite the remarkable resistance of the nucleic acid phosphodiester backbone to degradation affording genetic stability, the P-O bond must be broken during DNA repair and RNA metabolism, among many other critical cellular processes. Nucleases are powerful enzymes that can enhance the uncatalyzed rate of phosphodiester bond cleavage by up to ∼10-fold. Despite the most well accepted hydrolysis mechanism involving two metals (M to activate a water nucleophile and M to stabilize the leaving group), experimental evidence suggests that some nucleases can use a single metal to facilitate the chemical step, a controversial concept in the literature.
The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments.
Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.
Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm to compute immune and stromal cell scores for HNSC samples from the TCGA database and identified differentially expressed genes (DEGs) based on these scores.
The urge to adopt cleaner technologies drives the search for novel and sustainable materials such as Hydrophobic Natural Deep Eutectic Solvents (HNADESs), a new class of green solvents characterized by their low toxicity, biodegradability, and tunable properties, aiming to be applied in various fields for handling non-polar substances. In this work, the solubilization of hydrocarbons in type V HNADESs (non-ionic organic molecules) formed by mixing carvone, a natural monoterpenoid, with organic acids (hexanoic to decanoic acids) is examined by applying both experimental and theoretical approaches. The synthesis and physicochemical characterization of different HNADESs allowed us to tailor their properties, aiming for optimal interactions with desired hydrocarbons.
Applying artificial intelligence (AI) to image-based morphological profiling cells offers significant potential for identifying disease states and drug responses in high-content imaging (HCI) screens. When differences between populations (e.g.
