637 results about "Binding affinities" patented technology

The invention discloses a ligand information generation model training method and device and a ligand information generation method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring sample receptor information and sample ligand information, wherein the binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information is not less than a set affinity; denoising the reference noise data based on the sample receptor information through a to-be-trained neural network model to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model. The reference ligand information can be generated based on the reference receptor information through the ligand information generation model, and the binding affinity between the ligand described by the reference ligand information and the receptor described by the reference receptor information is high.

The invention relates to the field of drug target interaction, and discloses an application of a multi-modal feature fusion model in drug target binding affinity prediction, which comprises the following steps: S1, obtaining an original data set of drug target binding affinity, and taking a binding affinity index in the data set as a label of a training set and a test set; s2, acquiring representation information of other modals by using the drug sequence and target sequence representation provided in the original data set, preprocessing the acquired representation information of drugs and targets in different modals, and processing the representation information into a model acceptable feature vector form; and S3, respectively storing the multi-modal feature vectors of the drug and the target as well as the binding affinity tags in a form of pytorchsensor (. Pt). According to the method, shallow characterization of the drug and protein sequences is learned by applying the pre-training model, and the problem that a drug target binding affinity data set is insufficient is solved.

The invention relates to a drug target affinity prediction method based on multi-mode collaborative generation, which comprises the following steps: respectively preprocessing a drug and a protein to obtain preset characterization data of the drug and the protein; performing feature extraction on the preset characterization data of the medicine and the protein to obtain feature extraction results of the preset characterization data of the medicine and the protein; fusing the feature extraction result of the drug and the feature extraction result of the protein to obtain drug fusion features and protein fusion features; performing interactive processing on the drug fusion features and the protein fusion features by adopting a joint attention mechanism to obtain interactive features; and based on the interaction characteristics, predicting the binding affinity between the drug and the protein.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention relates to the technical field of biological information, in particular to an immunoreaction evaluation method based on tumor neoantigen activity sorting. The method comprises the following steps: obtaining genome data of tumor and normal tissues through whole exon sequencing, extracting multi-dimensional features of candidate somatic mutation, and screening by combining a Gaussian mixture model and a Transform model to obtain a high-confidence mutation genome set; predicting the HLA genotype of a patient based on sequencing data, translating and mutating into a peptide fragment, predicting the binding affinity of the peptide fragment and an MHC molecule by using an XGBoost model, and calculating a new antigen activity score sequence in combination with various parameters; and finally, synthesizing a new antigen peptide fragment according to a sorting result, carrying out in-vitro co-culture to detect an IFN-gamma secretion result, and dynamically optimizing a characteristic combination coefficient through a PPO algorithm to realize intelligent iterative updating, so that the accuracy of new antigen screening and the immunoreaction prediction capability are remarkably improved.

The invention discloses a method and device for predicting protein-ligand binding affinity based on distance and atomic contact number, comprising: acquiring a data set of protein-ligand binding affinity, the data set comprising: atomic information of a protein receptor and atomic information of a ligand; classifying atoms of a protein-ligand compound in the data set according to a preset atom type, and calculating a distance and an atom contact number of an atom pair of a protein-ligand in the data set according to the atom type; training a preset model by taking the protein-ligand distance and the atom contact number as training data to obtain a protein-ligand binding affinity prediction model; and obtaining to-be-predicted protein-ligand data information, inputting the to-be-predicted protein-ligand data information into the protein-ligand binding affinity prediction model, and outputting the to-be-predicted protein-ligand binding affinity. The method is not limited to the power exponent of the distance in the traditional electrostatic interaction energy and Van der Waals interaction energy, and the prediction capability of the affinity is improved.

The invention discloses a polypeptide with binding affinity to IL-6 (interleukin-6) and application of the polypeptide. The amino acid sequence of the polypeptide comprises one or more of the sequences as shown in SEQ ID NO.1-6. The polypeptide compatible with IL-6 is obtained through screening, the polypeptide can be specifically compatible with IL-6, IL-6 is enriched, then IL-6 is adsorbed at an inflammation infection part, more choices are provided for absorption and removal of inflammatory factors and treatment of diseases related to too high IL-6, and the polypeptide can be used for IL-6 detection and preparation of drugs for targeted combination of IL-6 or treatment of Il-6 related diseases.

The invention discloses a method for constructing a protein-ligand docking scoring model based on a graph Transform, which comprises a graph neural network, a multilayer network based on an attention mechanism and a decoding module, and is characterized in that the decoding module comprises an MND decoding module and a pKd decoding module; the MND decoding module is used for calculating the distance probability distribution between the protein and the ligand; the pKd decoding module is used for predicting an affinity value when the protein is combined with the ligand; the loss function comprises MDN loss and pKd loss, a first layer of the multi-layer network has mask vectors which are updated along with training iteration and are used for indicating that features are reserved or shielded, and docking scoring prediction is carried out by taking the protein graph and the ligand graph as input. According to the scheme, the model simultaneously uses the MDN decoding module and the pKd decoding module, the MDN module optimizes model parameters from the perspective of probability distribution, the pKd module optimizes the model parameters from the perspective of binding affinity, the MDN decoding module and the pKd decoding module carry out docking scoring from the spatial relation and the interaction relation, and the comprehensive evaluation ability of the model can be effectively improved through cooperation of the MDN decoding module and the pKd decoding module.

The invention provides a method for predicting binding affinity of human leukocyte antigen I-type molecules and peptide fragments, and a construction method of the method comprises the following steps: acquiring antigen binding site residues of the human leukocyte antigen I-type molecules, and extracting antigen binding region sequences of the human leukocyte antigen I-type molecules through secondary multi-sequence alignment; respectively generating a human leukocyte antigen class I molecule coding matrix and a peptide fragment coding matrix based on the paired human leukocyte antigen class I molecule-peptide fragment; and then constructing a model of a convolutional neural network to quantify the binding affinity score of the human leukocyte antigen class I molecule and the peptide fragment. The invention develops a method for predicting binding affinity of human leukocyte antigen class I molecules and peptide fragments based on a convolutional neural network by combining physicochemical characteristics of amino acids, complementary epitope sequences of the human leukocyte antigen class I molecules and an interaction relationship among the sequences.

The invention discloses a protein-protein binding affinity prediction method and device, equipment and a medium, and can be applied to the technical field of drug target affinity prediction. According to the method, after the first affinity prediction model comprising the structure model and the first monotonic neural network model is constructed, the first affinity prediction model is trained based on the self-attention mechanism and the mutual attention mechanism, so that the interaction mechanism of the receptor protein and the ligand protein can be simulated, and the accuracy of the model is improved; then, after a second affinity prediction model comprising a sequence model and a second monotonic neural network model is constructed, training the second affinity prediction model based on a structural feature vector output by the trained structural model and by adopting an alignment mechanism; therefore, the sequence model has the accuracy of the structure model and keeps the original high-throughput screening characteristic, and the affinity prediction accuracy of the algorithm model can be improved while the algorithm model training cost can be effectively reduced.

Aptamers as nucleic acid affinity reagents are gaining traction in analytical applications owing to their high stability, compact size, and amenability to chemical modifications. It is highly desired to generate aptamers with different binding affinities to meet the requirements of various applications. However, systematic evolution of ligands by exponential enrichment (SELEX), the gold standard for aptamer generation, is unable to quantitatively produce aptamers with desired binding affinities and requires multiple rounds of selection to eliminate false positive hits. Here we introduce Pro-SELEX, a new approach for rapid discovery of aptamers with precisely defined binding affinities that combines efficient particle display, high-performance microfluidic sorting and high-content bioinformatics. Using the Pro-SELEX workflow, we were able to investigate the binding performance of individual aptamer candidates under different selective pressure in a single round of selection. Using human myeloperoxidase (MPO) as a target, we demonstrated that aptamers with dissociation constants spanning a twenty-fold range of affinities can be identified within one round of Pro-SELEX. Our selection strategy will have a significant impact on the fields of biosensing and bioseparation by generating aptamers with tailored binding affinities.

The invention discloses a deep mutation scanning method based on protein-protein interaction in bacterial cells and a binding affinity prediction method. In the invention, split dCas9 proteins (SEQ ID NO.1 and SEQ ID NO.2) are respectively subjected to fusion expression with a receptor and a ligand with binding affinity to be detected, and are combined with a toxic sgRNA guide sequence (SEQ ID NO.3), and when PPI occurs, a toxic dCas9: sgRNA complex is formed to inhibit bacterial growth, so that a PPI affinity size signal is converted into a bacterial cell generation speed signal, and the signal can be quantified into a copy number value through next-generation sequencing. The invention further designs a deep learning binding affinity prediction model, high-affinity sequences can be screened from theoretical mutation space, the workload and cost of experimental verification are remarkably reduced, and a reliable basis is provided for subsequent protein function optimization and design.

The invention provides an anti-cancer active component optimization method for breast cancer treatment, which comprises the following steps: analyzing a potential interference path of an anti-cancer active component on immune system cell viability by adopting a computational chemistry simulation method according to a preliminary structural function mapping relationship, and determining a specific molecular mechanism range of immune system weakening; according to the synergistic effect evaluation result, optimizing the structural parameters of the active components through a molecular docking algorithm, adjusting the binding affinity of the active components with tumor cell targets and pathogenic bacteria targets, and determining a final structural optimization scheme; aiming at the final structure optimization scheme, verifying the expression of the modified active component on tumor inhibition and antibacterial ability by adopting simulation data of an in-vitro activity test, and obtaining a verification data set of comprehensive performance; and aiming at the updated active component design data, through a multi-objective optimization model, balancing the synergism of an anti-cancer effect and a health protection mechanism, and determining a final compound structure configuration suitable for complex requirements of a clinical environment.

The embodiment of the invention relates to a phycobiliprotein active peptide with a hypoglycemic function and a preparation method and application thereof, and belongs to the technical field of biological active peptides. According to the phycobiliprotein active peptide with the blood sugar reducing function, the amino acid sequence of the phycobiliprotein active peptide is Ser-Tyr-Phe-Asp-Arg-Ala, Ile-Ala-Ser-Tyr-Phe-Asp-Arg, Gly-Tyr-Tyr-Leu-Arg or Val-Ala-Phe-Gly-Arg-Phe, and the amino acid sequence of the phycobiliprotein active peptide with the blood sugar reducing function is shown in the description. The phycobiliprotein active peptide in the embodiment of the invention is separated from spirulina phycobiliprotein, and the spirulina phycobiliprotein is an important protein resource in spirulina and a high-quality source of bioactive peptide, and has wider application; the phycobiliprotein active peptide is low in molecular weight, and compared with a chemically synthesized hypoglycemic preparation, food-borne peptides are safer and more efficient, free of toxic and side effects and easy to digest and absorb; the phycobiliprotein active peptide has high binding affinity with the alpha-glucosidase and the DPP-IV, can form a very stable ligand receptor compound, and has a good inhibition effect on the alpha-glucosidase and the DPP-IV; the compound has excellent inhibition performance on alpha-glucosidase, alpha-amylase and DPP-IV (dipeptidyl peptidase-IV).

A fusion protein comprising at least one first polypeptide moiety and at least one second polypeptide moiety, wherein the first polypeptide moiety is a single-chain polypeptide capable of binding a target entity, and the second polypeptide moiety is a stabilizing polypeptide and comprises a single-chain α-helix-containing domain, wherein the second polypeptide moiety does not have binding affinity for said target entity. The presence of the second polypeptide moiety can improve at least one property of the fusion protein as compared to the property of the first polypeptide moiety alone, wherein said improved property is selected from the group consisting of: alkaline stability, recombinant protein expression and coupling to a support.

The invention relates to the technical field of computational biology, and discloses a high-precision drug screening method and system based on hierarchical multi-modal representation learning, and the method comprises the steps: carrying out the feature extraction of an amino acid sequence of a target protein through a plurality of pre-trained protein language models; based on the three-dimensional structure of the target protein, multi-scale graph representation and ligand molecular graph representation of the protein are constructed; performing multi-modal feature learning on the multi-scale graph representation and the ligand molecular graph representation of the constructed protein by using a neural network based on message passing; and based on the multi-modal features, constructing a binding affinity prediction model for drug screening, and completing a drug screening task. A complex interaction relationship in a protein and ligand graph structure is effectively captured by using a multilayer graph neural network, and efficient coding of protein-ligand pair features is realized. The surface, the structure and the ligand embedding vector of the protein can be extracted, and the accuracy and the efficiency of protein-ligand interaction prediction are improved.

The invention belongs to the technical field of medicinal chemistry, and discloses a preparation method and application of a benzofuran [2, 3-b] pyridine derivative of a targeted nuclear receptor co-activator 4 (NCOA4). The derivative has a structural general formula as shown in the following formula (I), wherein R1, R2 and R3 substituent groups can be combined at will. The compounds are combined with NCOA4 to interfere the cell iron autophagy process and effectively inhibit the ferroptosis of various cells induced by RSL3 or Erastin. Particularly, the binding affinity of the compound 4k and NCOA4 is optimal (Kd is equal to 0.78 mu M), and the compound 4k has remarkable ferroptosis inhibition activity and can be used for research and development of medicines for ferroptosis-related diseases. # imgabs0 #

Provided herein are methods of developing a meta-modeling framework for ligand-protein binding affinity prediction by integrating empirical scoring function-based docking and deep learning-based regression outputs through linear and non-linear machine learning models. Thus, empirical docking models and deep learning models were used as base models data. By taking predictions from those base models, machine learning regression models were developed as meta-models. These meta-models significantly improve binding affinity prediction over individual base models and achieve comparable performance to other structure-based deep learning models.

The invention discloses a method, a system, equipment and a medium for predicting binding affinity of protein and ligand. The method for predicting the binding affinity of the protein and the ligand comprises the following steps: acquiring olfactory protein and odorant molecules; inputting the olfactory protein and the odorant molecules into a prediction model to obtain a combined prediction result of the olfactory protein and the odorant molecules, the prediction model being pre-trained based on the data sample, the data sample comprises olfactory protein-odorant molecule experimental data from a data source and protein-ligand binding biological experimental data which is screened from a protein-ligand binding affinity biological experimental database and meets the similarity requirement with an olfactory protein sequence; the artificial bait is generated according to a positive sample in olfactory protein-odorant molecule experimental data and protein-ligand combined biological experimental data. By adopting the embodiment of the invention, the prediction accuracy and prediction efficiency of the binding affinity of the protein and the ligand are effectively improved through data collection and amplification and setting and introduction of novel characteristics.

The invention provides a CD4 + T cell receptor and polypeptide combination prediction method and prediction system based on deep learning. The prediction method comprises the following steps: respectively obtaining TCR sequence features and polypeptide sequence features based on a CD4 + TCR sequence and a polypeptide sequence, and fusing the TCR sequence features and the polypeptide sequence features to obtain fused sequence features; obtaining TCR descriptor features and polypeptide descriptor features based on the CD4 + TCR sequence and the polypeptide sequence, and fusing the TCR descriptor features and the polypeptide descriptor features to obtain fused descriptor features; obtaining TCR spatial characteristics and polypeptide spatial characteristics based on the CD4 + TCR sequence and the polypeptide sequence, and fusing the TCR spatial characteristics and the polypeptide spatial characteristics to obtain fused spatial characteristics; and predicting the binding force based on the fusion sequence features, the fusion descriptor features and the fusion spatial features. Therefore, the accuracy of combination prediction of the CD4 + T cell receptor and the polypeptide can be improved.

The invention discloses an anti-TL1A antibody or an antigen binding fragment thereof and application of the anti-TL1A antibody or the antigen binding fragment thereof. The anti-TL1A antibody or the antigen binding fragment thereof comprises HCDR1: X1YX2MH, hCDR2: X < 3 > X < 4 > NPYX < 5 > X < 6 > X < 7 > TX < 8 > YX < 9 > X < 10 > KFKG; hCDR3: X < 11 > X < 12 > X < 13 > X < 14 > X < 15 > X < 16 > X < 17 > X < 18 > X < 19 > Y; lCDR1: X < 20 > ASX < 21 > X < 22 > VX < 23 > X < 24 > X < 25 > X < 26 > X < 27 >; lCDR2: X < 28 > X < 29 > X < 30 > X < 31 > X < 32 > X < 33 > X < 34 >; and LCDR3: QQX35SSX36PX37T, and LCDR3: The anti-TL1A antibody or the antigen binding fragment thereof provided by the invention has TL1A binding affinity, especially can be specifically bound with TL1A, and can be used for detecting TL1A; the compound also can block the activity of a TL1A activated transcription factor NF [kappa] B, has an effect of inhibiting IFN-gamma, and can be used for preventing and / or treating TL1A mediated related diseases.

The present disclosure provides a MAGE-A1 specific TCR and uses thereof, and in particular relates to a TCR modified to improve binding affinity, a nucleic acid encoding the same, a vector comprising the nucleic acid of the invention, a cell comprising the TCR, the nucleic acid or the vector of the invention, and a method of preparing the cell of the invention. The disclosure also relates to conjugates or compositions comprising the TCRs of the invention, and methods of treating cancer by using the TCRs or cells of the invention.

The present disclosure is directed to a system 100 and a method 300 for predicting binding affinity of small molecules to Ribo-Nucleic Acid (RNA) targets The method comprises of receiving an input from a user, comprising a sequence of an RNA target, information related to a small molecule and a sub-type of the RNA target. The method further comprises of predicting a binding affinity of the small molecule to the RNA target of the sub-type, using a prediction model. Generating the prediction model comprises of, obtaining a plurality of RNA sequence based features for a plurality of sub-types of a plurality of RNA targets, and a plurality of small molecule features for a plurality of small molecules. Generating the prediction model further comprises of identifying a plurality of non-correlated features from the plurality of RNA sequence based features and the plurality of small molecule features and generating a final feature combination from the plurality of non- correlated features for each sub-type of the plurality of sub-types of the plurality of RNA targets based on a quasi-binding affinity of each of the plurality of small molecules with each sub-type of the plurality of sub-types of the plurality of RNA targets. The final feature combination is used to generate the prediction model.

A method for operating a protein structure-based candidate discovery system may include: displaying a first screen for receiving input of a plurality of ligands to be docked to a target protein; calculating predicted binding energy by sequentially performing molecular docking on the plurality of ligands based on settings input by a user via the first screen; displaying a first list on a second screen, the first list containing rows including a user interface control for user selection, names of the plurality of ligands, and the calculated binding energy values; predicting binding affinity for the rows selected by the user via the user interface control from the first list displayed on the second screen; and displaying a second list on a third screen, the second list containing rows including the names of the plurality of ligands, values of the binding energy, and values of the predicted binding affinity.

The present disclosure provides an anti-PD-L1 / anti-4-1BB bispecific antibody capable to effectively block the interactions between PD-L1 and its receptor PD-1 and between 4-1BB and its ligand. The bispecific antibody may have high binding affinity to both of a PD-L1 protein and a 4-1BB protein.

The invention discloses a protein-ligand binding affinity prediction method and system based on structure perception, and belongs to the field of bioinformatics and drug research and development. In order to solve the problem of low accuracy of affinity prediction caused by neglect of structural modal information of a protein-ligand compound in the existing affinity prediction, the invention provides the affinity prediction method. The method comprises the following steps: performing integer coding on a protein sequence and a ligand SMILES character string to obtain a sequence predicted value; expressing the protein binding pocket-ligand compound as an isomeric graph, and encoding the protein-ligand compound isomeric graph to obtain predicted values corresponding to node features and edge features; on the basis of the isomerism graph, homographs are generated through two element paths of'protein atoms-ligand atoms-protein atoms' and'ligand atoms-protein atoms-ligand atoms', encoding is carried out according to the homographs of the element paths, fusion features corresponding to the two element paths are obtained, and then corresponding predicted values are obtained; and obtaining a final predicted value based on all predicted values.

The present disclosure describes novel hybrid soluble ActRIIB-ECD polypeptides which fully retain binding affinity for myostatin and activin A but demonstrate significantly reduced binding to BMPs, especially BMP-9. The novel compositions described herein can be used to prepare novel hybrid ActRIIB ligand trap proteins, which can be used for modulating the growth of muscle, bone, cartilage, fat, fibroblast, blood and neuronal tissue to counteract muscle wasting, bone loss, anemia, inflammation and fibrosis in a therapeutically meaningful manner. Because these novel next-generation myostatin / activin inhibitors are safer and more effective molecules than the currently available myostatin inhibitors, they are useful in a wide variety of clinical indications.

The invention relates to the technical field of bioinformatics and artificial intelligence, and provides a drug-target interaction prediction method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the word segmentation coding of an obtained to-be-recognized drug sequence and a target sequence, and respectively extracting the subsequence features of a drug and a target; constructing a two-dimensional molecular diagram of the drug and a three-dimensional structure diagram of the target, and respectively extracting diagram structure characteristics of the drug and the target; fusing the subsequence features and graph structure features of the drug and the target through a cross attention mechanism; and carrying out interactive fusion by adopting a bidirectional collaborative attention mechanism to obtain a prediction result of the binding affinity of the drug and the target. According to the invention, by combining the multi-modal complementary information of the drug and the target, deep interaction between different modal features is deeply mined; meanwhile, a two-way collaborative attention mechanism is introduced into interaction modeling of the drug and the target, and the accuracy of drug-target binding affinity prediction is effectively improved.

The invention relates to bispecific antibodies comprising a first antigen-binding site that specifically binds to PD1 and a second antigen-binding site that specifically binds to TIM3, in particular to bispecific antibodies, wherein the bispecific antibody binds to 5 TIM3 with a lower binding affinity when compared to the binding to PD1. The invention further relates to methods of producing these molecules and to methods of using the same.

An activatable protein comprising a first target binding protein (TB1) that specifically binds to the first target; a second target binding protein (TB2) specifically binding to a second target, wherein TB2 is directly or indirectly coupled to TB1; a first masking moiety (MM1) that inhibits binding of TB1 to a first target and is coupled to TB1 directly or indirectly via a cleavable moiety, such as via one or more linkers or other components; a second masking moiety (MM2) that inhibits binding of TB2 to a second target and is coupled directly or indirectly to TB1 or to TB2 via a cleavable moiety; and a half-life extending moiety (EM) coupled directly or indirectly to the TB1 or to the TB2 via a cleavable moiety. The activatable protein is configured such that when the cleavable moiety cleaves, the resulting activatable protein has a shorter half-life, a higher binding affinity for the first target and a higher binding affinity for the second target than the intact activatable protein.