The present invention relates to the discovery of a method for identifying a treatment regimen for a patient diagnosed with cancer, predicting patient resistance to therapeutic agents and identifying new therapeutic agents. Specifically, the present invention relates to the use of an algorithm to identify a mutation in a kinase, determine if the mutation is an activation or resistance mutation and then to suggest an appropriate therapeutic regimen. The invention also relates to the use of a pattern matching algorithm and a crystal structure library to predict the functionality of a gene mutation, predict the specificity of small molecule kinase inhibitors and for the identification of new therapeutic agents.

Systems and methods have been developed to design and engineer glycan dynamics to improve immunogen antigenicity. These include systems for identify glycosylation sites that that impact binding of antibodies to the immunogen, and modifying the glycan profiles on these glycosylation sites to synthesize novel immunogens, antibodies and vaccines. Then, the machine learning algorithm may output data relating to the glycosylation sites that are determinant or likely impact the binding affinity of the variants to the one or more antibodies.

This invention is directed to a method of providing extra levels of encryption to a message by imposing a mask on top of an already encrypted message, wherein the mask sits on top of a protein folding of a sequence of amino acids.

Some embodiments include a computer-assisted method of biomedical treatment design. For example, a computer system can select a compound model associated with a candidate compound that is structured to bind to a biological target to modulate the biological target into achieving a therapeutic effect. The computer system can then identify a structural feature in the compound model as a hinge region that connects domains in the candidate compound. The computer system then determines a mutation process to introduce a mutation at the hinge region such that the mutation activates the candidate compound. The computer system then generates an updated compound model based on the mutation added to the candidate compound to present in a treatment design interface.

The present invention is an accelerated conformational sampling method for predicting target peptide and protein structures comprising a process of determining energy minimized synthetic templates using a simple system for modeling individual molecular bonds within the subject peptide or protein. Use of these synthetic templates greatly reduces the computational resources necessary for optimally determining structural features of the target peptide or protein. The present invention also provides methods for rapid and efficient analysis of the effect of mutations on target peptides and proteins.

This disclosure presents methods for antibody structure prediction and design. We utilize the growing number of antibody structures and sequences are used with powerful protein modeling methods to design and predict antibody structural models up to sub-angstrom accuracy. The invention also relates to systems and methods for generating an antibody library. Specifically, the invention relates to computer-implemented systems and methods for generating an antibody library for a predetermined epitope. The invention further relates to determining structural models of the interface between an antibody and its antigen. The invention also relates to determining structural models of an unbound complementarity determining region of an antibody.

A method for designing a protein capable of binding in an RNA base selective manner or RNA base sequence specific manner is provided. The protein of the present invention is a protein containing one or more of PPR motifs (preferably 2 to 14 PPR motifs) each consisting of a polypeptide of 30- to 38-amino acid length represented by the formula 1 (wherein Helix A is a moiety of 12-amino acid length capable of forming an -helix structure, and is represented by the formula 2, wherein, in the formula 2, A.sub.1 to A.sub.12 independently represent an amino acid; X does not exist, or is a moiety of 1- to 9-amino acid length; Helix B is a moiety of 11- to 13-amino acid length capable of forming an -helix structure; and L is a moiety of 2- to 7-amino acid length represented by the formula 3, wherein, in the formula 3, the amino acids are numbered i (1), ii (2), and so on from the C-terminus side, provided that L.sub.iii to L.sub.vii may not exist), and combination of three amino acids A.sub.1, A.sub.4 and L.sub.ii, or combination of two amino acids A.sub.4, and L.sub.ii is a combination corresponding to a target RNA base or base sequence.

The present invention relates to a method for determining in an expedient manner and with minimal sample consumption the structure of an unknown carbohydrate by using ion mobility-mass spectrometry (IM-MS) in negative ionization mode and fragmentation and a database containing structures of carbohydrates and/or of the fragments of the negative ions of carbohydrates, and for each of the structures of the target carbohydrates the collision cross section value and the mass-to-charge ratio value of the negative ion thereof, and for each of the structures of the fragments of the negative ions of the target carbohydrates the collision cross section value and the mass-to-charge ratio value of the fragment of the negative ion of the target carbohydrate.

The present invention relates to novel methods of diagnosing, preventing, and treating diseases, disorders, and infections relating to T-cell response. The disclosed methods for predicting MHC class II epitopes that elicit CD4+ T-cell response are N based on the three-dimensional protein structure of an antigen of interest. Given such an antigen, structural properties of the protein taken from experimental and modeling data are used to compute an epitope likelihood score that characterizes the location of epitopes likely to elicit an immune response to CD4+ T-cells. The epitopes are then used to construct biomolecules, including peptides, which may be used to diagnose, prevent, and/or treat a number of diseases, disorders, and infections.

A method of aligning a data sequence to one or more reference sequences represented as a sequence variation graph (SVG) is disclosed. The method can comprise receiving one or more alignment candidate regions and corresponding ordered seeding information. For each of the received alignment candidate regions, a current seed is determined, the current seed being a next-in-order unprocessed seed based on the ordered seeding information. Data paths in the alignment candidate region are then traversed to identify potential next seeds relative to the current seed. If at least one potential next seed is found, a next seed is selected and alignment results are generated by applying a local alignment procedure to align query data in portions of the query data sequence between the current seed and the next seed with reference data in portions of the alignment candidate region located between the current seed and the next seed.