18 results about "Anticancer peptide" patented technology

The invention discloses a polypeptide function prediction method based on machine learning. The method comprises the following steps: collecting data information of wound healing peptides, hypoglycemic peptides, antioxidant peptides and anticancer peptides, analyzing different characterization data of polypeptide sequences by utilizing biological new informatics software, constructing a polypeptide characterization database, and dividing training, verification and test data sets; important features for determining polypeptide function classification are obtained through data preprocessing and feature screening; based on the important feature data, performing model construction on the data by using different machine learning algorithms to obtain an algorithm with the best performance for model optimization; and further performing performance verification on the optimization model on the verification set and the test set. The invention constructs a polypeptide function prediction tool based on a C50 algorithm, and the function of the position function polypeptide can be efficiently and accurately evaluated.

An anticancer peptide comprehensive prediction and screening method based on multi-modal deep learning relates to the technical field of anticancer peptide screening, and obtains a to-be-detected sequence set, and inputs the to-be-detected sequence set into a pre-constructed multi-modal deep learning framework, the multi-modal deep learning framework comprises a dichotomy model, a multi-label classification model and an active regression model, selecting one of the models or sequentially combined three models to process a to-be-detected sequence set, obtaining a plurality of second activity values when the to-be-detected sequence set is sequentially processed by the dichotomy model, the multi-label classification model and the activity regression model, and obtaining candidate anti-cancer peptides according to the second activity values, the method has the advantages of high accuracy, strong generalization and good interpretability, and the calculation and screening efficiency of the candidate anticancer peptide can be obviously improved.

The first aspect of the present application provides an anticancer peptide classification method based on a graph convolution network, comprising: S1, obtaining an anticancer peptide training data set and a test data set; S2, constructing an anticancer peptide sequence into a graph structure network with amino acid nodes as vertices and encoding to obtain graph structure data; S3, constructing a graph convolution collapse pooling and residual network model, inputting the graph structure data into the model for training; and S4, applying the trained model to classify anticancer peptides. Wherein the graph convolution collapse pooling and residual network model comprises a stacked graph convolution network module, a graph collapse pooling module and a residual network module. The above scheme regards the anticancer peptide data as a kind of data similar to the graph structure, uses the graph convolution neural network to process the classification problem of the anticancer peptide, effectively distinguishes the anticancer peptide and the non-anticancer peptide, and avoids the problems of low recognition accuracy, insufficient generalization ability, lack of large-scale evaluation of features and prediction model of the existing model.

Embodiments of the current invention provide a solution to the problems associated with balancing patient toxicity with broad efficacy of cancer therapy. In particular, embodiments are directed to anti-cancer peptides that demonstrate a broad anti-cancer efficacy with a limited toxicity to normal or non-cancer cells.

The application discloses an anticancer peptide generation and screening method and system based on a diffusion model and a seed synchronous autoencoder, and comprises the following steps: preprocessing original anticancer peptide and non-anticancer peptide sequences and extracting multi-dimensional physicochemical features; training a diffusion model in a continuous numerical space based on sequence mapping based on a global random seed, adding noise through forward diffusion and learning the potential distribution of anticancer peptide data features through reverse denoising; based on a reverse seed synchronization strategy, training an autoencoder using the same random seed and environment state, and establishing a mapping from continuous random noise to discrete biological sequences; using the diffusion model to sample and generate potential vectors in the latent space and mapping them into candidate anticancer peptide sequences through the decoded synchronously trained decoder; inputting an integrated learning classifier based on grouping features, selecting the optimal feature group and predicting the anticancer activity probability, and outputting the anticancer characteristic peptide chain; the application solves the mapping contradiction between continuous space and discrete sequences and can efficiently screen anticancer peptides with high activity sequences.

The invention relates to an intelligent anti-cancer peptide recognition method based on a Transform deep learning framework. The problems that in the prior art, an anti-cancer peptide screening method is serious in feature redundancy, poor in model interpretability and prone to over-fitting are solved. The method comprises the following steps: S1, data preprocessing: obtaining a sequence and carrying out data set division; s2, feature extraction: automatically extracting a plurality of sequence features; s3, feature screening: carrying out importance evaluation on the extracted features; s4, model construction: designing a deep learning classifier and constructing a traditional machine learning and classical deep learning architecture for comparison; and S5, performance evaluation and optimization: performing parameter optimization on each model in the training set by adopting five-fold cross validation, and finally evaluating the performance on the independent test set. The method has the advantages that the accuracy and the stability of anticancer peptide prediction are remarkably improved, redundant features are effectively removed, the model complexity is reduced, the calculation efficiency is improved, the reliability of a prediction result is ensured, and the labor cost and the time cost are greatly reduced.

The invention provides an antibacterial peptide de novo design method and system based on a spectrum structure-activity relationship. The method comprises the following steps: obtaining structures, sequences and simulated spectrum data of functional peptides including antibacterial peptides, anti-cancer peptides, anti-inflammatory peptides and the like; constructing a spectral diffusion generation model, and generating spectral data corresponding to the target function; mapping the spectrum into a CA atomic distance matrix through a neural network model, and generating an initial three-dimensional structure in combination with PyRosetta; optimizing the initial structure by using an RFdiffusion model to obtain a high-resolution full-atomic structure; inputting the optimized structure into a retrained ProteinMPNN model, and generating a candidate polypeptide sequence in combination with a function guidance strategy; and finally, performing function screening and structure verification on the sequence by using a deep learning model. The technical problems that an existing antibacterial peptide design method neglects peptide three-dimensional structure information and lacks an optimal design strategy for multiple functions such as antibiosis, anticancer and anti-inflammation, so that design flexibility and functional diversity are low are solved.

The invention discloses an anti-cancer peptide recognition method and system based on multi-feature fusion and double-layer integrated learning. The method comprises the steps of S1, data preprocessing; s2, feature extraction and feature fusion based on a protein language model; s3, feature extraction; s4, performing dimension reduction processing on the high-dimensional features; s5, inputting each feature vector in the multi-source feature set into a corresponding XGBoost classifier for training and prediction, wherein each classifier outputs a peptide sequence as a preliminary prediction probability of the anti-cancer peptide; combining the preliminary prediction probabilities output by all classifiers into a probability feature vector; s6, inputting the probability feature vectors obtained from the upper layer into a K-nearest neighbor classifier and a soft voting classifier at the same time; the KNN outputs a first prediction probability, and the soft voting integrator outputs a second prediction probability; s7, calculating an arithmetic mean value of the first prediction probability and the second prediction probability as a prediction probability; and comparing the peptide sequence with a preset threshold value, if the peptide sequence is greater than or equal to the threshold value, determining that the peptide sequence is an anti-cancer peptide, otherwise, determining that the peptide sequence is a non-anti-cancer peptide.

The embodiment of the invention provides an anti-cancer peptide recognition method based on multi-modal feature fusion. The anti-cancer peptide recognition method is based on multi-modal feature fusion of a natural language processing technology and a deep learning technology, and a high-precision interpretable anti-cancer peptide recognition algorithm is constructed by analyzing amino acid composition and physicochemical properties of a peptide sequence and dynamically fusing multi-source information in combination with a multi-head attention mechanism. According to the method, seven types of key physicochemical indexes can be systematically extracted, multi-dimensional characteristics such as coverage charge distribution, hydrophobicity and structural stability can be systematically extracted, cross-modal fusion is carried out on sequence codes and seven-dimensional physicochemical characteristics through a sequence-physicochemical characteristic dynamic fusion strategy, weights of the two types of characteristics are dynamically adjusted through gating and a multi-head attention mechanism, and therefore, multi-modal fusion is realized. The potential ACPs can be rapidly screened from massive peptide sequences, and efficient calculation support is provided for cancer treatment.

This invention relates to the field of biotechnology, specifically disclosing anticancer peptides and their applications. The anticancer peptides of this invention comprise amino acid sequences as shown in any one of SEQ ID NO. 1-39, or amino acid sequences with greater than or equal to 90% homology to any one of SEQ ID NO. 1-39, or fragments of amino acid sequences as shown in any one of SEQ ID NO. 1-39, or fragments of amino acid sequences with greater than or equal to 90% homology to any one of SEQ ID NO. 1-39. This invention provides novel anticancer peptides, offering a new method for cancer treatment.

The present application relates to an anticancer lipopeptide C8H 15 Preparation of O-Asp-Ser-Asp-Val-Trp-Trp-Gly-Gly-Arg-Arg-Leu-Leu-Arg-Arg-Leu-Arg-Arg-Leu and its application in antitumor treatment, belonging to the field of biological medicine. The anticancer lipopeptide is prepared based on the key biological characteristics (positive charge, alpha-helix structure and amphiphilicity) of membrane lytic peptide. The present application provides a solid-phase synthesis method of the anticancer lipopeptide. The present anticancer lipopeptide has the characteristics of small hydrophobic moment, low hemolytic activity and good serum stability, can effectively make up for the defects of most anticancer peptides with high hemolytic toxicity, and solve the problem of poor in-vivo stability of peptide drugs. In-vitro anticancer experiments and in-vivo antitumor experiments prove that the present anticancer lipopeptide has good antitumor effect and good application prospect.

The invention belongs to the field of polypeptides, and particularly relates to a fatty acid modified anticancer peptide and application thereof. The nitrogen terminal of the anticancer peptide is modified by C16-18 fatty acid, the carbon terminal is carboxyl, the sequence of the anticancer peptide contains lysine K, tryptophan W and glutamic acid E, the structural general formula of the anticancer peptide is C16-18-KaWbE, a = 3 or 4, b = 3 or 4, and a + b = 7; and glutamic acid E is located at the 1-site or 8-site. The anti-cancer peptide is simple in structure and low in production cost. An in-vitro anti-tumor experiment and a toxicity experiment show that the anti-cancer peptide has remarkable anti-tumor activity on various cancer cells and has relatively low toxicity on normal cells. A scanning electron microscope experiment shows that the anti-cancer peptide can kill tumor cells by rapidly breaking a membrane, so that the anti-cancer peptide has a relatively great application potential in the aspects of developing novel anti-tumor drugs and resisting tumor multidrug resistance, and the anti-cancer peptide has a good application prospect in the aspect of preparing clinical application drugs.

The invention provides ginseng oligopeptide with an anti-tumor effect and a preparation method of the ginseng oligopeptide. The ginseng oligopeptide is prepared by performing double-enzyme enzymolysis on ginseng protein, the oligopeptide is further screened and identified to obtain the Rs-A-2 active anti-cancer peptide, and the anti-cancer peptide has a relatively good effect of inhibiting cancer cell proliferation. Meanwhile, animal experiments prove that the oligopeptide and / or the anti-cancer peptide can effectively reduce expression of target surface genes and inhibit growth of mouse tumors, and the anti-cancer peptide is beneficial to improving the immune function of tumor-bearing mice. After the oligopeptide is prepared into a medicine, the medicine has a better anti-tumor effect and a wide application prospect.

The invention provides an anti-cancer peptide sequence classification method based on a multi-scale enhancement conditional diffusion model, which solves the problems of anti-cancer peptide sequence classification and the like, and comprises the following steps: S1, data collection and preprocessing; s2, performing high-level multi-scale enhanced feature extraction and dimension reduction on the sequence; s3, constructing a conditional diffusion model; s4, model training; s5, generating a classification result of the anticancer peptide sequence; and S6, performing model evaluation. The method has the advantages of accurate classification, high calculation efficiency and the like.

The invention belongs to the field of polypeptides, and particularly relates to a nitrogen-terminal fatty acid modified anticancer peptide and application thereof. The nitrogen terminal of the anti-cancer peptide is modified by straight-chain fatty acid, the carbon terminal of the anti-cancer peptide is of an amide structure, and the structural general formula of the anti-cancer peptide is Cn-KaWbEc, wherein Cn represents a fatty acid chain with n carbon atoms, a is the number of lysine K, b is the number of tryptophan W, and c is the number of glutamic acid E; n is equal to 6-18; a + b + c = 8; a = 3 or 4, b = 3 or 4, and c = 0, 1 or 2. According to the anti-cancer peptide modified by the nitrogen-terminal fatty acid, lysine, tryptophan and glutamic acid are used for constructing 8-bit oligopeptide, a nitrogen-terminal fatty acid modification strategy and a carbon-terminal amide structure are matched, and the anti-cancer peptide is simple in structure and low in synthesis cost. In-vitro anti-tumor experiments and toxicity experiments show that the anti-cancer peptide has remarkable anti-tumor activity on various tumor cells such as 4T1, Hela, A549, HCCLM3 and the like, and shows broad-spectrum anti-cancer activity.

The present application relates to the technical field of anticancer peptide prediction, and particularly relates to an anticancer peptide prediction method based on a bidirectional long short-term memory network and feature fusion, comprising: reading four reference peptide sequence datasets, and performing amino acid composition analysis on the datasets; performing feature extraction on the datasets by Bi-LSTM to generate Bi-LSTM feature vectors; performing feature extraction on five amino acid feature vectors by a fully connected neural network; performing feature fusion on the feature vectors by a Concatenate algorithm, obtaining a probability score through a fully connected layer with 1 unit and a Sigmoid activation function, and distinguishing anticancer peptides and non-anticancer peptides through the score. The present application realizes anticancer peptide prediction with high accuracy, high Matthews correlation coefficient, high sensitivity, high specificity and high area under the ROC curve.