End-to-end learning for compound activity prediction based on binding pocket information

Abstract Background Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, docking simulation is generally applied because it only requires a tertiary structure of the target protein. However, the conformation search and the evaluation of binding energy of docking simulation are computationally heavy and thus docking simulation needs huge computational resources. Thus, if we can apply a machine learning-based activity prediction method for a novel target protein, such methods would be highly useful. Recently, Tsubaki et al. proposed an end-to-end learning method to predict the activity of compounds for novel target proteins. However, the prediction accuracy of the method was still insufficient because it only used amino acid sequence information of a protein as the input. Results In this research, we proposed an end-to-end learning-based compound activity prediction using structure information of a binding pocket of a target protein. The proposed method learns the important features by end-to-end learning using a graph neural network both for a compound structure and a protein binding pocket structure. As a result of the evaluation experiments, the proposed method has shown higher accuracy than an existing method using amino acid sequence information. Conclusions The proposed method achieved equivalent accuracy to docking simulation using AutoDock Vina with much shorter computing time. This indicated that a machine learning-based approach would be promising even for novel target proteins in activity prediction. </jats:sec

Iterative blind deconvolution method for dwell-time adjustment

Negative values occur in the calculation results when a fast Fourier transform is used in the algorithm to calculate the dwell time via the dwell-time adjustment algorithm for optical element grinding. In the present study, a processing method for negative values is used in the Fourier iterative Ayers–Dainty (AD) algorithm. The method in which the range of the unit removal shape was adopted in the AD algorithm resulted in a smaller error and enabled the error to be minimized using the quantized dwell-time distribution output.</jats:p

Rapid and Sensitive Method for Erythropoietin Determination in Serum

Abstract An enzyme-linked immunosorbent assay (ELISA) method for erythropoietin determination has been established by using two kinds of monoclonal antibodies with specific affinities to erythropoietin. Besides being rapid (2.5 h) and sensitive (detection limit 0.3 int. unit/L), the present method gives accurate results and is easy to perform. The method may be clinically applicable for discriminative diagnosis of polycythemia and analyses of various anemic conditions.</jats:p