553 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 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 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.

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 #

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.

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 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 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.

The invention discloses an anti-TL1A antibody or an antigen binding fragment thereof and application thereof. The anti-TL1A antibody comprises at least one of the following CDRs: a heavy chain variable region CDR: an amino acid sequence as shown in SEQ ID NO: 1-3, SEQ ID NO: 7-9 or SEQ ID NO: 13-15; and the light chain variable region CDR has an amino acid sequence as shown in SEQ ID NO: 4-6, SEQ ID NO: 10-12 or SEQ ID NO: 16-18. 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 application provides methods of treating a tumor or cancer in a subject using antibody-toxin conjugates having the structure shown in General Formula (I), wherein Ab is conjugated to a camptothecin derivative through a linker. Ab refers to a bispecific antibody that simultaneously targets two different epitopes or targets. In one embodiment, Ab is a bispecific antibody having binding affinity to EGFR and HER3.

The invention provides an application of polyphyllin VII in preparation of an Akt signaling pathway inhibitor targeting GRB2, the invention reveals that PPVII is specifically combined with GRB2 protein and regulates and controls the function of the GRB2 protein for the first time, the Akt pathway inhibition effect is exerted, and the molecular mechanism of PPVII is specifically shown as follows: molecular docking and dynamic simulation confirms that PPVII and an SH3 structural domain of GRB2 form a stable compound, and the stable compound is used for inhibiting the Akt pathway of the GRB2. And the key amino acid binding sites are Ile65, Gln157, Tyr160 and Phe182. Cell thermal transfer experiments (CETSA) show that PPVII can significantly enhance the thermal stability of GRB2 protein, surface plasmon resonance (SPR) determines the direct binding affinity KD = 58.5 [mu] M, functional research proves that PPVII inhibits an Akt signal channel through a GRB2 dependency mode, the polyphyllin VII is used as a GRB2 targeted inhibitor, and by specifically binding with an SH3 structural domain of GRB2 and inhibiting a downstream Akt signal channel, the polyphyllin VII can be used for inhibiting the Akt signal channel in the GRB2 targeted inhibitor. And a new strategy and a candidate compound are provided for intervention of related diseases.

The invention belongs to the technical field of bioinformatics, and particularly relates to a drug target binding affinity prediction method for reducing feature redundancy through multi-feature fusion. According to the PSDTA model, physicochemical properties are fused in initial features, and meanwhile, structural information of amino acid is explicitly introduced, so that the generalization performance of the model is improved. According to the model, a main neighborhood aggregation network is adopted for modeling molecular internal interaction and propagating characteristics. Besides, two independent channels are designed for the model and used for reducing redundant node features and predicting more key amino acids in the combination process, so that the model can extract more accurate information. Compared with other drug target binding affinity methods on three reference data sets, the model achieves the optimal effect on all evaluation indexes such as mean square error, mean absolute error, consistency index, adjusted decision coefficient, Pearson's correlation coefficient and the like.

The embodiment of the invention provides a drug optimization method and system based on the overall effect of a protein network. The method comprises the following steps: constructing a protein interaction network related to a target disease, and dividing each protein target into a risk protein set and a protection protein set; respectively calculating first binding affinity data of the candidate drugs and each protein target in the risk protein set, and generating a first network comprehensive score based on the first binding affinity data; respectively calculating second binding affinity data of the candidate drugs and each protein target in the protection protein set, and generating a second network comprehensive score based on the second binding affinity data; calculating network confrontation scores of the candidate drugs according to the first network comprehensive score and the second network comprehensive score; and determining whether the candidate drug is a preferred drug based on the network adversarial score. The method can overcome the defect that a single-target drug is insufficient in curative effect due to a network compensation effect, so that safer and more effective candidate drugs are screened out.

Embodiments of the present disclosure relate to modeling DEL data using factorized molecular representations (e.g., hierarchical single and double synthon building blocks), which leverages the hierarchical structure inherent to these molecules. Using factorized molecular representations, machine learning models are trained to learn the underlying binding affinity of compounds to targets and one or more covariates (e.g., loading / repeat noise). This results in machine learning models producing improved predictions in the form of higher enrichment scores that have good correlation with compound-target binding affinity.

The invention relates to the technical field of safe conveying of oil and gas pipelines, in particular to a polypeptide type hydrate kinetic inhibitor screening method based on molecular simulation, which comprises the following steps: selecting target protein related to hydrate growth, and designing a polypeptide sequence; performing polypeptide pre-screening; evaluating the binding affinity and binding mode of the polypeptide and the target protein; performing MD simulation on the screened high-affinity polypeptide; verifying the binding affinity of the polypeptide and the target protein; and collecting all experiment and simulation data for statistical analysis, and optimizing the polypeptide sequence. The structure and function of the target protein are systematically analyzed by using a bioinformatics database and tools, it is ensured that the selected target plays an important role in hydrate formation, and meanwhile, a polypeptide sequence designed based on the target protein structure can effectively improve the binding capacity; and in the pre-screening and docking analysis process of the polypeptide, dynamic parameters are simulated and extracted by molecular dynamics, so that the interaction between the polypeptide and the target protein can be deeply understood.

A method for training a ligand information generation model performed by an electronic device includes obtaining sample receptor information and sample ligand information, a binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information being not less than a set affinity; denoising reference noise data based on the sample receptor information using a neural network model undergoing training 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 ligand information generation model being configured to generate reference ligand information based on reference receptor information.

The invention relates to the technical field of biological medicines, in particular to a preparation method of an inhibitor targeting a MAX-PD-L1 promoter specific binding motif and a double-target inhibitor, through ChIP-seq and site-directed mutagenesis experiments, a core binding motif of MAX and a PD-L1 promoter, such as 5 '-CAC [GA] TG-3', is defined, it is ensured that the inhibitor only targets a key site of MAX-PD-L1 interaction, and the activity of the MAX-PD-L1 promoter specific binding motif is improved. The off-target effect is avoided, and a high-specificity target spot is provided for subsequent inhibitor design. The cell permeability of the DNA aptamer screened based on the specific binding motif is improved after cholesterol modification, and the affinity of the DNA aptamer is obviously higher than that of a traditional antibody. A small molecule compound virtually screened through a molecular docking model is optimized through hydrogen bond and hydrophobic interaction, and then the binding affinity with MAX is improved. After treatment with the inhibitor, the combination inhibition rate of MAX and the PD-L1 promoter is high, the transcriptional activity of PD-L1 is obviously reduced, the killing rate of T cells to tumor cells is also improved, and immune escape is effectively blocked.

The invention relates to the field of biological medicine, and discloses a pTH long-acting polypeptide compound as well as preparation and application thereof. According to the polypeptide, at least one amino acid residue of the 13th, 26th and 27th amino acid residues of a pTH (1-34) parent peptide sequence is connected with octadecanedioic acid or eicosanoic acid and a derivative modification group of octadecanedioic acid or eicosanoic acid, and the polypeptide is covalently combined with a polypeptide skeleton through an AEA-AEA-gamma-Glu connecting unit. The binding capacity of the polypeptide and serum albumin is remarkably enhanced by utilizing a fatty acid side chain modification means, and meanwhile, the half-life period is prolonged. The pTH long-acting polypeptide compound disclosed by the invention has remarkable osteogenic activity promoting effect, can promote proliferation of MC3T3-E1 cells and generate osteogenic markers and calcium nodules under osteogenic induction conditions, and the binding capacity of the pTH long-acting polypeptide compound and serum albumin is improved by 2-6 times. The invention further provides a solid-phase synthesis method of the polypeptide compound and a pharmaceutical composition containing the polypeptide compound, the polypeptide compound can be used for treating osteoporosis and parathyroid hypofunction, and the compliance problem that an existing medicine needs to be injected every day is effectively solved.