The present invention relates to a virtual drug screening method with high prediction accuracy based on various biological activities extracted from multiple drug screening data, without using structures or structural attribute information of target proteins or compounds; an intensive screening library constructing method; and a system therefor.

Disclosed are methods, compositions, kits, and systems for identifying small-molecule drugs for treating cancer in a subject. The disclosed methods, compositions, kits, and systems may be utilized to identify small-molecule inhibitors of radiation sensitive protein 52 (RAD52) in order to treat cancer in a subject, such as breast cancer in a subject having a BRCA1-deficient, BRCA2-deficient, and/or PALB2-deficient phenotype by administering the identified small-molecule inhibitors to the subject.

The invention relates to anti-sigma factors (anti-sigmas) that bind to sigma factors and block activation of transcription. Anti-sigmas and their cognate sigma factors provide a highly effective mechanism for regulating gene expression in genetic circuits.

A fluid identification system comprising a plurality of particles, each particle encapsulating therein at least one tracer material having an identifiable DNA, the at least one tracer material being encapsulated by an encapsulation material, wherein the particles are adapted to retain the at least one tracer material in an encapsulated form after exposure of the particles to a temperature of at least 75? C. and/or a pressure of at least 1000 psi (6.9?10.sup.6 N/m.sup.2).

The present invention relates to compounds which have been discovered to be potent ligands (inhibitors) of human GLUT5 (glucose transporter type 5), a facilitative glucose transporter that transports fructose, and their use as ligands assays which can uncover additional ligands of GLUT5, having the potential for being used as drugs. In addition, the present invention is directed to compounds, chemical compositions and methods for treating disease states and conditions which are mediated through GLUT5, including such disease states and conditions as GLUT5 deficiency syndrome, diabetes (type I and II), cancer, metabolic diseases including metabolic syndrome and fatty liver disease, among others.

A method of establishing a machine learning model for cancer anticipation includes collecting test results of a plurality of tumor markers of a plurality of eligible individuals and corresponding conditions of cancer; performing a variable selection process on the collected data to select a plurality of robust variables; and using the selected variables, numerals, and conditions of cancer by cooperating with a machine learning method to establish a cancer anticipation model. A method of detecting cancer by using a plurality of tumor markers in a machine learning model for cancer anticipation is also provided.

The present invention relates to peptides, nucleic acids and cells for use in immunotherapeutic methods. In particular, the present invention relates to the immunotherapy of cancer. The present invention furthermore relates to tumor-associated cytotoxic T cell (CTL) peptide epitopes, alone or in combination with other tumor-associated peptides that serve as active pharmaceutical ingredients of vaccine compositions that stimulate anti-tumor immune responses. The present invention relates to several novel peptide sequences and their variants derived from HLA class I and HLA class II molecules of human tumor cells that can be used in vaccine compositions for eliciting anti-tumor immune responses.

An affinity ligand peptide library of IgG constructed on the basis of Protein A affinity model and the application of a design method thereof. According to the Molecular MechanicsPoisson-Boltzmann surface area (MM/PBSA) method and on the basis of the known human IgG-Protein A complex structure, the hot spots of Protein A that have high affinity for human IgG are obtained analytically, and a Protein A simplified affinity model is built thereof. An affinity peptide library of IgG is constructed including heptapeptide and octapeptide structural modes. On the basis of the peptide structural modes, the types of inserted amino acids that X residues represent are further identified using amino acid location method. Then, molecular docking and molecular dynamics simulation methods are used to screen the candidate peptides successively. Finally, the affinity peptide ligands that can effectively purify IgG are identified using affinity chromatography.

The present invention generally relates to methods of rapidly and efficiently searching biologically-related data space. More specifically, the invention includes methods of identifying bio-molecules with desired properties, or which are most suitable for acquiring such properties, from complex bio-molecule libraries or sets of such libraries. The invention also provides methods of modeling sequence-activity relationships. As many of the methods are computer-implemented, the invention additionally provides digital systems and software for performing these methods.

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.