Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.

Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.

In one aspect, a method is disclosed for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool. The method includes presenting, in a first screen of the GUI, a design space for a protein for an application, where the design space includes a set of sequences, where each sequence contains a respective set of activities pertaining to the application. The method also includes receiving, via a graphical element in the first screen, a selection of one or more query parameters of the design space, and presenting, in a second screen of the GUI, a solution space that includes a subset of the set of sequences, where each sequence contains the respective set of activities, where the subset of the set of sequences is selected based on the one or more query parameters.

Co-crystals comprising the Nuclear receptor related 1 protein-ligand binding domain (Nurr1-LBD) and a cyclopentenone prostaglandin are provided. Also provided are methods of identifying or designing Nurr1-modulating ligands and compounds based on the crystal structures described herein as well as the applications of said ligands and compounds as Nurr1 modulators or medicaments.

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.

Methods and compositions related to the selective, specific disruption of multiple ligand-receptor signaling interactions, such as ligand-receptor interactions implicated in disease, are disclosed. These interactions may involve multiple cytokines in a single receptor family or multiple ligand receptor interactions from at least two distinct ligand-receptor families. The compositions may comprise polypeptides having composite sequences that comprise sequence fragments of two or more ligand binding sites. The methods and compositions may involve sequence fragments of two or more ligand binding sites that are arranged to conserve the secondary structure of each of the ligands from which the sequence fragments were taken.

Methods and compositions related to the selective, specific disruption of multiple ligand-receptor signaling interactions, such as ligand-receptor interactions implicated in disease, are disclosed. These interactions may involve multiple cytokines in a single receptor family or multiple ligand receptor interactions from at least two distinct ligand-receptor families. The compositions may comprise polypeptides having composite sequences that comprise sequence fragments of two or more ligand binding sites. The methods and compositions may involve sequence fragments of two or more ligand binding sites that are arranged to conserve the secondary structure of each of the ligands from which the sequence fragments were taken.

Systems and methods for in silico prediction of HLA type of a patient are presented in which patient sequence reads and a reference sequence with known and distinct HLA alleles are used in a de Bruijn graph. A composite match score is then used to rank HLA alleles, thus providing a first HLA type. A second HLA type is identified by re-ranking using an adjusted composite match score.

Systems and methods for in silico prediction of HLA type of a patient are presented in which patient sequence reads and a reference sequence with known and distinct HLA alleles are used in a de Bruijn graph. A composite match score is then used to rank HLA alleles, thus providing a first HLA type. A second HLA type is identified by re-ranking using an adjusted composite match score.

Contemplated compositions and methods are directed to cancer neoepitopes and uses of such neoepitopes, especially to generate synthetic antibodies against neoepitopes that may then be employed in the manufacture of a therapeutic agent. Preferred therapeutic agents will comprise a synthetic antibody against a neoepitope, and most preferably in combination with a cellular or non-cellular component for use as a diagnostic or therapeutic agent.