37 results about "Protein database" patented technology

The invention relates to renewable energy and bioengineering technology, and discloses a computer-designed enzyme preparation feeding method in the fermentation process and its application. The method comprises: under the condition that the target product of the fermentation process exists, respectively measuring the activity of each enzyme component, wherein the concentration of the target product is set to a plurality of different simulated concentrations; obtaining the crystal structure of the enzyme component from a protein database, Select the model of the target product, and carry out the molecular dynamics simulation of the enzyme component when the concentration of the target product is the simulated concentration; obtain the concentration value of the target product during the fermentation process without adding the enzyme preparation, and compare the activity data with the molecular dynamics simulation results In combination, the feeding time period and feeding amount of each enzyme component were proposed. The method can optimize the adding strategy of cellulase during fermentation to produce ethanol, increase the yield of ethanol, reduce the cost, and further improve the economic benefit of the whole ethanol process.

Systems and methods are provided for identifying a precursor ion without using any a priori precursor ion information. In one method, a sample is analyzed using a tandem mass spectrometer, producing at least one measured product ion spectrum from a precursor mass-to-charge ratio range. The at least one measured product ion spectrum are received. A subset of measured product ions is selected from the at least one measured product ion spectrum. A list of candidate compounds is created by searching a dictionary of potential compounds that includes one or more predicted product ions for each of the potential compounds using the subset of measured product ions. A candidate compound on the list is selected as the identified compound. In another method, the measured product ions are assumed to correspond to shortened forms of the peptide and a protein database is searched for shortened forms of the peptide.

A method for predicting at least one fitness value of a protein is executed on a computer and comprises the steps of: encoding (100) an amino acid sequence of the protein into a numerical sequence according to a protein database, the numerical sequence comprising a value for each amino acid in the sequence; calculating (110) a protein profile from the sequence of values; and for each fitness: comparing (130) the calculated protein profile with protein profile values of a predetermined database containing protein profile values for different values of the fitness, predicting (130) the value of the fitness according to the comparing step.

The invention discloses a deep learning method for DNA binding protein identification and functional annotation based on a self-attention mechanism. A data set is selected from a protein database, and the constructed deep learning model is trained and tested by using the selected data set, and then the trained deep learning model is used for training and testing. The deep learning model can predict whether protein can bind to DNA; wherein, the deep learning model includes coding layer, embedding layer, long short-term memory neural network layer (LSTM), convolutional neural network layer (CNN) and self-attention Layer (Self‑Attention). Through the deep learning model based on the self-attention mechanism, the present invention can predict whether it has the function of binding with DNA according to the primary sequence information of the protein, and find out the region with the binding function.

The invention discloses a method for predicting a protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly. The method comprises the following steps that firstly, according to the difficult problem that search space of protein high-dimensional conformation space is complex, the effectiveness of fragment replacement is judged under a Rosetta force field model through the Monte Carlo statistical method according to a protein database configuration fragment bank; under a differential evolution group algorithm framework, the complexity of the search space is reduced through fragment assembly, meanwhile, false fragment assembly is removed through the Monte Carlo statistical method, and the conformation search space is gradually reduced through the diversity of an evolutionary algorithm, and therefore the searching efficiency is improved; meanwhile, a module with coarseness is adopted, a side chain is ignored, and cost of a search is effectively reduced. The method for predicting the protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly can effectively obtain optimal local stable conformation and is high in predicting efficiency and good in convergence correctness.