Patent classifications
C07K1/10
Acylation process for preparation of N-substituted peptide
The present invention relates to a facile acylation process for preparation of N-Substituted peptide and proteins. More specifically, the invention relates to acylating a peptide or a protein with deprotected acylating agent.
METHODS OF MAKING INCRETIN ANALOGS
Intermediate compounds are disclosed for making incretin analogs, or pharmaceutically acceptable salts thereof. In addition, methods are disclosed for making incretin analogs by coupling from two to four of the intermediate compounds herein via hybrid liquid solid phase synthesis or native chemical ligation.
METHODS OF MAKING INCRETIN ANALOGS
Intermediate compounds are disclosed for making incretin analogs, or pharmaceutically acceptable salts thereof. In addition, methods are disclosed for making incretin analogs by coupling from two to four of the intermediate compounds herein via hybrid liquid solid phase synthesis or native chemical ligation.
METHOD FOR PRODUCING PEPTIDE COMPOUND
An object of the present invention is to provide a method for producing a peptide with high efficiency, and a method for producing a peptide which comprises the following steps (1) and (2): (1) a step of mixing an N-protected amino acid or an N-protected peptide with a carboxylic acid halide represented by the formula (I)
##STR00001##
(wherein X represents a halogen atom,
R.sup.1, R.sup.2 and R.sup.3 each independently represent an aliphatic hydrocarbon group which may have a substituent, and a total number of the carbon atoms in R.sup.1, R.sup.2 and R.sup.3 is 3 to 40); and (2) a step of mixing the product obtained in the step (1) and a C-protected amino acid or a C-protected peptide
is provided.
METHOD FOR PRODUCING PEPTIDE COMPOUND
An object of the present invention is to provide a method for producing a peptide with high efficiency, and a method for producing a peptide which comprises the following steps (1) and (2): (1) a step of mixing an N-protected amino acid or an N-protected peptide with a carboxylic acid halide represented by the formula (I)
##STR00001##
(wherein X represents a halogen atom,
R.sup.1, R.sup.2 and R.sup.3 each independently represent an aliphatic hydrocarbon group which may have a substituent, and a total number of the carbon atoms in R.sup.1, R.sup.2 and R.sup.3 is 3 to 40); and (2) a step of mixing the product obtained in the step (1) and a C-protected amino acid or a C-protected peptide
is provided.
Method of preparing a keratin-based biomaterial and keratin-based biomaterial formed thereof
Method of preparing a keratin-based biomaterial is provided. The method comprises a) reacting keratin with a polymer having at least one of an amine and carboxylic functional group in the presence of a carbodiimide cross-linking agent to form a cross-linked keratin-polymer material; and b) freeze drying the cross-linked keratin-polymer material to form the keratin-based bio-material. A keratin-based biomaterial thus prepared is also provided.
Method of preparing a keratin-based biomaterial and keratin-based biomaterial formed thereof
Method of preparing a keratin-based biomaterial is provided. The method comprises a) reacting keratin with a polymer having at least one of an amine and carboxylic functional group in the presence of a carbodiimide cross-linking agent to form a cross-linked keratin-polymer material; and b) freeze drying the cross-linked keratin-polymer material to form the keratin-based bio-material. A keratin-based biomaterial thus prepared is also provided.
Selectively controllable cleavable linkers
Selectively controllable cleavable linkers include electrochemically-cleavable linkers, photolabile linkers, thermolabile linkers, chemically-labile linkers, and enzymatically-cleavable linkers. Selective cleavage of individual linkers may be controlled by changing local conditions. Local conditions may be changed by activating electrodes in proximity to the linkers, exposing the linkers to light, heating the linkers, or applying chemicals. Selective cleaving of enzymatically-cleavable linkers may be controlled by designing the sequences of different sets of the individual linkers to respond to different enzymes. Cleavable linkers may be used to attach polymers to a solid substrate. Selective cleavage of the linkers enables release of specific polymers from the solid substrate. Cleavable linkers may also be used to attach protecting groups to the ends of growing polymers. The protecting groups may be selectively removed by cleavage of the linkers to enable growth of specific polymers.
Selectively controllable cleavable linkers
Selectively controllable cleavable linkers include electrochemically-cleavable linkers, photolabile linkers, thermolabile linkers, chemically-labile linkers, and enzymatically-cleavable linkers. Selective cleavage of individual linkers may be controlled by changing local conditions. Local conditions may be changed by activating electrodes in proximity to the linkers, exposing the linkers to light, heating the linkers, or applying chemicals. Selective cleaving of enzymatically-cleavable linkers may be controlled by designing the sequences of different sets of the individual linkers to respond to different enzymes. Cleavable linkers may be used to attach polymers to a solid substrate. Selective cleavage of the linkers enables release of specific polymers from the solid substrate. Cleavable linkers may also be used to attach protecting groups to the ends of growing polymers. The protecting groups may be selectively removed by cleavage of the linkers to enable growth of specific polymers.
MACHINE LEARNING METHOD FOR PROTEIN MODELLING TO DESIGN ENGINEERED PEPTIDES
Provided herein are methods for design of engineered polypeptides that recapitulate molecular structure features of a predetermined portion of a reference protein structure, e.g., an antibody epitope or a protein binding site. A Machine Learning (ML) model is trained by labeling blueprint records generated from a reference target structure with scores calculated based on computational protein modeling of polypeptide structures generated by the blueprint records. The method may include training an ML model based on a first set of blueprint records, or representations thereof, and a first set of scores, each blueprint record from the first set of blueprint records associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. A set of engineered polypeptides are then generated based on the second set of blueprint records.