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.

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.

The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind a target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data, identifying, by a first machine-learning model having model parameters learned from the initial sequence data, a first set of aptamer sequences, obtaining, using an in vitro binding selection process, subsequent sequence data including sequences from the first set of aptamer sequences, identifying, by a second machine-learning model having model parameters learned from the subsequent sequence data, a second set of aptamer sequences, determining, using one or more in vitro assays, analytical data for aptamers synthesized from the second set of aptamer sequences, and identifying a final set of aptamer sequences from the second set of aptamer sequences based on the analytical data associated with each aptamer.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

A method and apparatus are provided for designing sequence-defined biomolecules, such as proteins using a data-driven, evolution-based process. To design proteins, an iterative method founded on a combination of an unsupervised sequence-based model with a supervised functionality-based model can select candidate amino acid sequences that are likely to have a desired functionality. Feedback from measuring the candidate proteins using a high-throughput gene-synthesis and a protein screening process is used to refine and improve the models guiding the candidate selection to the most promising regions of the very large amino acid sequence search space.

A method and apparatus are provided for designing sequence-defined biomolecules, such as proteins using a data-driven, evolution-based process. To design proteins, an iterative method founded on a combination of an unsupervised sequence-based model with a supervised functionality-based model can select candidate amino acid sequences that are likely to have a desired functionality. Feedback from measuring the candidate proteins using a high-throughput gene-synthesis and a protein screening process is used to refine and improve the models guiding the candidate selection to the most promising regions of the very large amino acid sequence search space.

Systems and methods for identifying mechanisms for editing genome sequences are provided. One example computer-implemented method includes, for each of multiple guide nucleic acid sequences, for a desired edit: identifying characteristics of the guide nucleic acid sequence and/or sequence segment; assigning, based on a scoring data structure, a score to the guide nucleic acid sequence for each identified characteristic; and aggregating the assigned scores into an edit score for the guide nucleic acid sequence. The method then includes compiling a report that includes the multiple guide nucleic acid sequences and the edit score for each of the guide nucleic acid sequences, thereby permitting selection, from the report, of at least one of the guide nucleic acid sequences based on the associated edit score. Additionally, based on the edit score, a number of guide nucleic acid sequences tested, a sample size, and/or a number of experiments can be set to reach the desired edit.

Various embodiments generally relate to intelligently designing aptameric peptides for binding with a specific receptor and forming aptameric peptide libraries with the designed peptides. The aptameric peptides libraries can be tissue-specific and be used in drug delivery and therapeutic applications, in which designed peptides can be implanted on exosome surfaces for exosomal cargo delivery to a specific tissue. Various embodiments of the present disclosure involve the use of a generative adversarial network (GAN) machine learning model configured (e.g., trained) and used to output designed peptides that are similar to pre-existing peptides of a peptide dataset but that specifically bind to a selected receptor and have various selected physiochemical properties. In various embodiments, GAN machine learning models may receive representations of the pre-existing peptides and may output representations of designed peptides according to peptide vectorization and encoding schemas based at least in part on the amino acids within a peptide.