The invention provides computational methods for engineering, selecting, and/or identifying proteins with a desired activity. Further provided are automated computational design and screening methods to engineer proteins with desired functional activities including, but not limited to ligand binding, catalytic activity, substrate specificity, regioselectivity and/or stereoselectivity.

The invention provides computational methods for engineering, selecting, and/or identifying proteins with a desired activity. Further provided are automated computational design and screening methods to engineer proteins with desired functional activities including, but not limited to ligand binding, catalytic activity, substrate specificity, regioselectivity and/or stereoselectivity.

Provided herein are methods and compositions relating to libraries of optimized antibodies having nucleic acids encoding for an antibody comprising modified sequences. Libraries described herein include variegated libraries comprising nucleic acids each encoding for a predetermined variant of at least one predetermined reference nucleic acid sequence. Further described herein are protein libraries generated when the nucleic acid libraries are translated. Further described herein are cell libraries expressing variegated nucleic acid libraries described herein.

Provided herein are methods and compositions relating to libraries of optimized antibodies having nucleic acids encoding for an antibody comprising modified sequences. Libraries described herein include variegated libraries comprising nucleic acids each encoding for a predetermined variant of at least one predetermined reference nucleic acid sequence. Further described herein are protein libraries generated when the nucleic acid libraries are translated. Further described herein are cell libraries expressing variegated nucleic acid libraries described herein.

This disclosure relates to a method and apparatus for training a prediction model. The method includes: obtaining a training sample set; determining a current training sample from the training sample set based on the training sample weights; inputting current target energy characteristics corresponding to the current training sample into a pre-trained prediction model for basic training to obtain a basic prediction model after completing the basic training; updating the training sample weights corresponding to the training samples based on the basic prediction model; and returning to perform the operation of determining the current training sample from the training sample set based on the updated training sample weights until completing model training to obtain a target prediction model.

A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.

A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.

Unveiling of chimeric biomolecule sequences and applications thereof are described. Generally, systems comprising statistical analysis are performed to unveil chimeric biomolecule sequences from sequencing data sets. Bloom filters and hierarchical bloom filter tree data structures can be constructed such that chimeric sequence unveiling systems are more efficient. Finally, chimeric sequences are used to develop research tools, diagnostics, and medicaments.

A computer based biological sequence analysis method provides, after a training phase adopting data from screening experiments, either an evaluation of an input sequence expressing the performance with reference to the chemical-physical feature object of the screening experiment, or at least an optimized output sequence. The method provides the use of a set or library of sequences derived from DMS experiments and SELEX for the generation of a second set of high efficiency biological sequences, whereby high efficiency means, for example, high catalysis capacity, high fitness, high ability to bind to a specific target, high fluorescence activity and, in general, a high performance with reference to the chemical-physical properties of a molecule which are defined at the start and can be selected through experiments.

Polypeptides are disclosed herein having significantly improved secretion ability from eukaryotic cells, together with fusion proteins, nanoparticles, and uses thereof, and methods for designing such polypeptides.