93 results about "Protein design" patented technology

The invention discloses a protein optimization design and screening method and device based on an artificial intelligence algorithm, and the method takes fungal luciferase as an example, and integrates multi-dimensional bioinformatics analysis and deep learning technology to realize efficient protein engineering transformation and optimization. The method comprises the following steps: firstly, identifying a binding domain of luciferase and fluorescein by utilizing an AI-driven molecular docking simulation method, and determining a key conservative site by combining literature, evolutionary analysis and structural prediction; then, generating a protein functional domain skeleton under the constraint of a fixed site by adopting a diffusion model, and performing protein sequence prediction by utilizing a graph neural network model; and finally, scoring and screening the generated sequences to obtain high-stability candidate variants. According to the method, a conservative site dynamic fusion strategy is innovatively constructed, a fixed region is optimized through logic of structure prediction, evolution site intersection priority and literature site union set expansion, the diversity and adaptability of a protein design sequence are met, and the efficiency bottleneck of a traditional scheme is broken through.

The invention discloses a multi-dimensional screening method for protein design based on a lexicographical order optimization strategy, and belongs to the field of biotechnology and synthetic biology. The method comprises the following steps: predicting a three-dimensional structure of a compound of a target protein and a ligand to construct a three-dimensional skeleton model of the target protein; generating a functional adaptive amino acid sequence; and adopting multi-dimensional screening indexes, including structure prediction precision, thermodynamic stability evaluation, thermal stability analysis and physicochemical property calculation, and performing priority ranking and hierarchical screening on candidate sequences in combination with a lexicographical order optimization algorithm. Through strict priority-driven multi-objective optimization, the design efficiency and success rate are remarkably improved, the number of experimental verification times is reduced, and the method is suitable for the fields of drug development, industrial enzyme engineering, gene therapy and the like and has important application value.

An MHCl binding peptide design method based on evolutionary information and a Transform neural network algorithm relates to the field of protein design, and comprises the following steps: S1, extracting evolutionary information features of alleles of MHCII molecules and binding core sequences of binding peptides corresponding to the alleles, S2, establishing a neural network model based on fusion of a convolution module and a Transform module, and S3, establishing a neural network model based on fusion of the convolution module and the Transform module, the method comprises the following steps: S1, extracting two frequency characteristic tensors from S11 and S12, taking the two frequency characteristic tensors extracted in S11 and S12 as double inputs, and finally obtaining probability distribution of 20 amino acids at each position of each sequence, and S3, according to an output result of a neural network model, carrying out random sampling according to the probability, and generating a binding core sequence of MHCII-peptide meeting target distribution. According to the method, evolutionary information such as sequence position amino acid frequency (first-order conservative analysis) and combined frequency (second-order conservative analysis) of amino acid pairs is introduced to design a new short peptide sequence, the problem that short peptides cannot be designed based on structures is solved, and the reliability of short peptide sequence design based on evolutionary information is provided.

A system and method for generating thermostable variants of a protein is disclosed. The system receives a three-dimensional structure of a target protein and identifies mutable regions, including solvent-exposed residues and loop regions. Conserved and active site residues are excluded from mutation through a fixed-position mask. A message-passing neural network (MPNN) generates mutant sequences at unmasked positions, executed under multiple temperature parameters. Design scores based on Shannon entropy and log probability are computed, and high-confidence variants are selected. Predicted structures for selected variants are evaluated using structural and sequence-based features to compute stability scores. A ranked list of thermostable variants is generated. Top candidates undergo molecular dynamics simulations to compute dynamic metrics such as RMSD, radius of gyration, SASA, and ddG, and are re-ranked accordingly. The system enables accurate, constraint-driven protein design with high structural and functional fidelity, suitable for industrial and therapeutic applications.

A method for designing proteins using multi-objective reinforcement learning can include generating, by one or more processors using a machine model, based on an initial protein sequence data structure, a plurality of protein sequences, the machine learning model configured based on reinforcement learning from a plurality of reward metrics including at least one reward metric associated with experimental data regarding example sequence data, scoring, by the one or more processors, using a plurality of scoring functions, the plurality of protein sequences, to select a subset of protein sequences of the plurality of protein sequences, and outputting one or more selected protein sequences of the subset of selected protein sequences.

Disclosed herein are predictive models for T-cell epitope prediction, B-cell epitope prediction, and protein design wherein a method is implemented for generating a protein variant amino acid sequence of a target protein having one or more modified properties, the method comprising: (a) iteratively sampling an input amino acid sequence of the target protein, and (b) sampling the individual protein score of at least one weighted relative contribution of the single residue mutant input amino acid sequence to the at least one target property across a plurality of other single residue mutant input amino acid sequences to generate a combined protein score, wherein the combined protein score corresponds to the protein variant comprising one or more amino acid mutations of the single residue mutant input amino acid sequences.

The invention provides a protein function topology design method based on SE (3) isovariant diffusion and ex-situ generation and a product, and relates to the technical field of protein design. According to the method, geometric deep learning and generative artificial intelligence are fused, isovariant generation and optimization of protein function sites under the action of a three-dimensional Euclidean space (SE (3) group) are achieved, and the limitation of traditional protein design on conformation sampling efficiency, function guidance and physical realizability is broken through.

The invention relates to germline-targeting designs, stabilization designs, and / or combinations thereof, of proteins designed with modified surfaces helpful for immunization regimens, other protein modifications and / or development of nanoparticles, methods of making and using the same, and to (a) germline-targeting priming or boosting / shepherding immunogens to initiate or guide maturation of VRC01-class responses (b) PCT64 / PG9-germline-targeting designs (c) BG18-germline-targeting designs or boosting / shepherding immunogens to initiate or guide maturation of BG18-like responses, and / or (d) trimer stabilization and presentation in a membrane-bound format.

The invention relates to the technical field of protein design, and particularly discloses an Mfp-SOD recombinant protein as well as a preparation method and application thereof. According to the recombinant protein, mussel mucin Mfp-3 is used as a skeleton to assist anchoring of human-derived protein SOD, and a novel recombinant protein is obtained. According to the recombinant protein, two proteins are fused, so that the recombinant protein has excellent skin surface affinity and the functions of oxidation resistance, aging resistance, whitening, spot fading, inflammation resistance, sunscreen and the like, and the effects of oxidation resistance and aging resistance can be exerted for a long time.

The application relates to the technical field of protein design, and particularly discloses a kind of Mfp-SOD recombinant proteins and a preparation method and application thereof.The recombinant protein takes the mussel mucin Mfp-3 as a skeleton, assists anchoring human-derived protein SOD, and obtains a new type of recombinant protein.The recombinant protein is fused by two kinds of proteins, has excellent skin surface affinity and functions of antioxidation, anti-aging, whitening, spot-fading, anti-inflammation and sun protection, and can achieve long-acting antioxidation and anti-aging effects.

A method for segment preserving protein design includes determining, within a protein structure having a first sequence of residues, one or more fixed segments and adjustable segments. The protein structure may be identified as having a desired property. A protein design computational model may be used to generate a second sequence of residues comprising at least one of a corruption and a length change to the first adjustable segment. The protein design computational model may be further used to generate a modified protein structure having the second sequence of residues. The second sequence of residues forming the modified protein structure includes the fixed segments present in the first sequence of residues. Structural and / or functional analysis may be performed to determine whether the modified protein structure also exhibits the same desired property as the protein structure. Related systems and computer program products are also provided.

The present invention discloses a recombinant porcine circovirus type 3 trimer protein, a preparation method and an application thereof. The present invention uses bioinformatics methods and resources to predict the B cell antigen epitopes and T cell antigen epitopes of the PCV3Cap protein. Taking into account the stability and immunogenicity of the antigen epitopes, a recombinant porcine circovirus type 3 trimer protein based on the PCV3Cap protein antigen epitope is designed, and its efficient soluble expression in Escherichia coli is achieved. The expressed recombinant porcine circovirus type 3 trimer protein can be mass-produced and purified by Ni-NTA affinity chromatography. The purified recombinant porcine circovirus type 3 trimer protein can assemble into a stable trimer structure. The subunit vaccine prepared using the recombinant porcine circovirus type 3 trimer protein can induce experimental pigs to produce a high level of antibodies and has a significant protective effect on the experimental pigs. The recombinant porcine circovirus type 3 trimer protein designed by the present invention provides a new idea for the development of PCV3 vaccine.

The application discloses a kind of fowl secretory bacterium surface proteins and its screening method and application, belong to biotechnology field.The application can be soluble high-efficiency expression in escherichia coli by screening protective antigen from fowl secretory bacterium heparin binding protein, combined with antigen prediction, soluble analysis and structure-oriented protein design, successfully obtained protein A0A3Q9GGY1, A0A3S9QKJ0 and six-site mutant PLO that can be soluble high-efficiency expression in escherichia coli, has good immunoprotective efficiency, and has accumulated experience for the screening and design of high-yield antigen, and has laid a foundation for the research and development of fowl secretory bacterium vaccine.

A biological programming specification that identifies at least one protein design condition in accordance with a biological programming language is received. A machine learning model is used to convert the biological programming specification to a model input format version for a biological reasoning model. The model input format version is used as a conditioning input for the biological reasoning model to generate a protein design having the at least one protein design condition.

The present disclosure relates to a compound of Formula (I) or a salt, enantiomer, diastereomer, isotopically enriched analogue, solvate, prodrug or polymorph thereof, and the use thereof. Further provided in the present disclosure are a pharmaceutical composition comprising, as an active ingredient, the compound of Formula (I) or a salt, enantiomer, diastereomer, isotopically enriched analogue, solvate, prodrug or polymorph thereof, and the use thereof. A series of compounds designed and synthesized in the present disclosure can effectively prevent and / or treat diseases or disorders associated with cereblon protein.

The application provides a method and system capable of end-to-end protein design and analysis verification, and can generate protein or protein complex structure and sequence according to specific requirements, and the method comprises the following steps: performing loss calculation on the three-dimensional structure of the initialized protein sequence obtained through the structure prediction algorithm independently developed by the company based on confidence, stability, target correlation; continuously improving the designed protein sequence and structure based on the Markov chain Monte Carlo algorithm or gradient regression according to the obtained loss; and putting the designed protein structure into a sequence design model to optimize the side chain, and obtaining the optimal candidate protein through screening. The method can train the model to be specific and automatic for protein design, and can generate a new artificial protein which is completely different from natural protein in structure and sequence.

A training set may be generated to include a plurality of noisy sample sequences. Each noisy sample sequence in the training set may be generated by adding noise to a corresponding sample sequence from a data distribution. A protein design computation model may be trained by at least applying the protein design computation model to generate one or more output sequences, and adjusting the protein design computation model to reduce a difference between the one or more output sequences and the plurality of noisy sample sequences in the first training set. The trained protein design computation model may be applied to generate an output sequence by at least modifying an input sequence.

The invention discloses a leaf and branch compost cutinase link and application thereof, and belongs to the technical field of enzyme molecule construction. The leaf-branch compost cutinase kink is obtained by adjusting the connection sequence of fragments in leaf-branch compost cutinase, and comprises a fragment II, a connecting peptide fragment I, a fragment I, a connecting peptide fragment II and a fragment III which are connected in sequence from the N end to the C end; wherein the fragment I, the fragment II and the fragment III respectively correspond to 37th to 79th amino acids, 81st to 143rd amino acids and 150th to 293rd amino acids or homologous sequences thereof of the leaf branch compost cutinase. According to the method, protein topology engineering and an artificial intelligence assisted protein design technology are organically combined, knot topology transformation is performed on the leaf branch compost cutinase, sequence optimization is performed on a connecting peptide fragment of the knot, and the leaf branch compost cutinase knot with good biological activity is obtained; the expression quantity and the stability of the leaf and branch compost cutinase kink are improved, the problem of kinetic barriers existing in protein kink combination is solved, and the method has important application value.

This invention discloses a method for designing transmembrane regulatory proteins based on cueing strategies and generative models, belonging to the field of bioinformatics. It includes a target survey stage and a closed-loop design stage. In the target survey stage, a generative diffusion model is used to generate virtual probes targeting the membrane protein. A set of complex conformations is obtained by combining sequence design and structure prediction models, and binding hotspot regions are identified based on the spatial distribution density of the probes, overcoming the dependence on manually specified binding sites. In the closed-loop design stage, based on the identified hotspot regions, an initial backbone is generated using a generative diffusion model. A structure cueing strategy guides the sequence design and structure prediction models to perform closed-loop iterative optimization, generating sequences that selectively bind to the transmembrane domains of membrane proteins and regulate their functions. This invention achieves automated, function-guided design of transmembrane regulatory proteins, effectively expanding the range of designable targets and significantly improving the stability and functional specificity of the designed products in the membrane environment.

The invention relates to the technical field of recombinant protein, in particular to self-assembled calcium chelate keratin RK35DE and a preparation method thereof. According to the invention, the self-assembled calcium-chelated keratin RK35DE is obtained through protein design, whole-gene synthesis, vector construction and escherichia coli expression. The keratin RK35DE is subjected to self-assembly research, and the TEM image of the keratin RK35DE shows that the keratin RK35DE has self-assembly capability. In a calcium chelating ability experiment, compared with that before recombination, the self-assembled calcium-chelated keratin RK35DE disclosed by the invention has a remarkable chelating effect on calcium ions. In the prior art, keratin with self-assembly capability lacks effective coordination capability of forming stable chelates with calcium ions in hydroxyapatite, so that stable compounds are difficult to form and exert effects. The self-assembled calcium chelate keratin researched and developed by the scheme provides a new thought for researching a new hydroxyapatite tooth restoration material, and has an ideal popularization and application prospect.

The present application relates to the technical field of protein design, and discloses a protein design knowledge graph construction method based on multi-agent, comprising the following steps: obtaining a natural language research topic input by a user; obtaining entity terms and intention objects by using a large language model, and generating a query strategy for a multi-source heterogeneous database based on the standardized entity terms and intention objects; obtaining original data from the multi-source heterogeneous database by using the query strategy; processing the parsed original data uniformly, and storing the processed data into a relational database; automatically extracting knowledge triples from the relational database and unstructured data, performing knowledge verification and deduplication, and storing the knowledge triples into a graph database; simultaneously, using a graph embedding algorithm and link prediction to perform knowledge completion and reasoning, and forming a domain-enhanced protein design knowledge graph; the present application solves the problems of low efficiency and poor generality of the existing protein design knowledge graph construction method.

The invention discloses arcanobacterium pyogenes surface protein as well as a screening method and application thereof, and belongs to the technical field of biology. According to the invention, a protective antigen which can be soluble and efficiently expressed in escherichia coli is screened from arcanobacterium pyogenes heparin binding protein, and antigen prediction, solubility analysis and structure-oriented protein design are combined; proteins A0A3Q9GGY1, A0A3S9QKJ0 and a six-site mutant PLO which can be soluble and efficiently expressed in escherichia coli and have good immune protection efficacy are successfully obtained, experience is accumulated for screening and design of high-yield antigens, and a foundation is laid for research and development of arcanobacterium pyogenes vaccines.

The present invention relates to a method for de novo design of part or all of a protein, comprising: a. generating a highly designable backbone structure; b. iteratively selecting and modifying the amino acid sequence. This method uses pre-specified features as constraints to generate the backbone spatial structure of the protein to be designed, and then determines the amino acid sequence of the protein to be designed, ensuring that the protein to be designed possesses the pre-specified features. This method generates the backbone spatial structure of the protein to be designed without using known specific protein fragments as structural templates to splice together to generate the protein structure. Instead, it uses computer optimization to generate a mathematical model (statistical energy function model) learned from a large number of natural protein structures.

The invention relates to a voltage-gated anion channel protein designed from de novo and application of the voltage-gated anion channel protein. The voltage-gated anion channel protein (dVGACs) designed from the beginning has a funnel-shaped pentamer structure, each monomer in the pentamer is composed of three transmembrane helixes, and the amino acid sequence of each monomer comprises SEQ ID No. 1 or a mutant thereof, at least one amino acid of the 43rd, 47th, 51th and 55th sites of the amino acid sequence of the mutant is mutated into arginine or aspartic acid. The dVGACs provided by the invention have selectivity to chloride ions, have voltage-responsive conformational change, and cause opening of a central pore channel at a membrane potential of about + 40 mV or + 20 mV and above. Due to the properties, the biosensor has a wide application prospect in the aspects of neurological disease treatment, cell behavior regulation and control, biosensor development and the like.

A biological programming specification that identifies at least one protein design condition in accordance with a biological programming language is received. A machine learning model is used to convert the biological programming specification to a model input format version for a biological reasoning model. The model input format version is used as a conditioning input for the biological reasoning model to generate a protein design having the at least one protein design condition.

Provided herein are systems and methods for protein sequence design. Also provided herein are engineered antibody sequences.

The invention relates to the technical field of protein design, and discloses a multi-agent-based protein design knowledge graph construction method, which comprises the following steps of: obtaining a natural language research theme input by a user; obtaining entity terms and intention objects by using a large language model, and generating a query strategy for the multi-source heterogeneous database based on the standardized entity terms and intention objects; acquiring original data from the multi-source heterogeneous database by utilizing a query strategy; performing alignment processing on the analyzed original data, and storing the processed data in a relational database; knowledge triples are automatically extracted from the relational database and the unstructured data, knowledge verification and duplicate removal are carried out, the knowledge triples are stored in a graph database, meanwhile, a graph embedding algorithm and link prediction are used for knowledge completion and reasoning, and a domain-enhanced protein design knowledge graph is formed; the problems that an existing protein design knowledge graph construction method is low in efficiency and poor in universality are solved.

Disclosed in the present invention are a protein for inhibiting the conserved helix of TDP-43 and the use thereof. The protein contains an amino acid sequence as shown in SEQ ID NO: 1 or SEQ ID NO: 2. Further disclosed in the present invention are the use of the protein in the preparation of a drug for diagnosing, preventing and / or treating neurodegenerative diseases, and the use in the preparation of an inhibitor of a target protein containing the conserved helix of TDP-43 LCD. In the present invention, by means of using artificial intelligence-assisted protein design techniques, a high-affinity non-natural protein that precisely binds to the conserved helix of a TDP-43 protein is designed, which is used for inhibiting the participation of the conserved helix region in the formation of a β-sheet aggregation core, is mainly used in the treatment of diseases targeting the TDP-43 protein, such as amyotrophic lateral sclerosis, and is used as a research tool for TDP-43 protein phase separation experiments.

A system is disclosed for de novo protein generation. The system receives a set of design condition(s) that specify target characteristics of a synthetic protein. The system defines a modular energy function as a composition of a diffusion energy component and one or more conditioner energy components. The system applies a diffusion model to determine a denoised protein backbone. In applying the diffusion model, in each sampling step: the system transforms one prior sampled state of the synthetic protein from unconstrained space into constrained space based on the one or more design conditions, denoises the prior sampled state in the constrained space, and samples a subsequent sampled stated by applying a gradient of the modular energy function to the denoised prior sampled state in the constrained space. The final sampled state is a denoised protein backbone for the synthetic protein that satisfies the set of design condition(s).

This invention discloses a protein sequence spatial compression and functional optimization method based on a large-scale model, belonging to the fields of artificial intelligence and proteomics. This invention mines potential amino acid substitution sites in consensus sequences and then controls the sequential substitution process using a large protein language model, thereby maintaining the functional stability of proteins during sequence substitution and subsequently screening for substitution combinations that effectively enhance protein function. Introducing a large protein model transforms protein sequences into embedding vectors representing protein structure, function, and physicochemical properties. By analyzing the embedding vectors during the substitution process, it is possible to prevent new proteins from deviating from their original function and basic structure due to substitution. This invention combines consensus substitution identification with large-scale model analysis, effectively compressing the sequence space of amino acid substitutions, thereby significantly improving the efficiency of protein design and modification.