We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.
Protein loops are often involved in diverse biological functions, and some functional loops show conformational changes upon ligand binding. Since this conformational change is directly related to ligand binding pose and protein function, there have been numerous attempts to predict this change accurately. In this study, we show that it is plausible to obtain meaningful ensembles of loop conformations for flexible, ligand-binding protein loops efficiently by applying a loop modeling method. The loop modeling method employs triaxial loop closure algorithm for trial conformation generation and conformational space annealing for global energy optimization. When loop modeling was performed on the framework of ligand-free structure, loop structures within 3 Å RMSD from the crystal loop structure for the ligand-bound state were sampled in 4 out of 6 cases. This result is encouraging considering that no information on the ligand-bound state was used during the loop modeling process. We therefore expect that the present loop modeling method will be useful for future developments of flexible protein-ligand docking methods.
Predicting the local structural features of a protein from its amino acid sequence helps its function prediction to be revealed and assists in three-dimensional structural modeling. As the sequence-structure gap increases, prediction methods have been developed to bridge this gap. Additionally, as the size of the structural database and computing power increase, the performance of these methods have also significantly improved. Herein, we present a powerful new tool called S-Pred, which can predict eight-state secondary structures (SS8), accessible surface areas (ASAs), and intrinsically disordered regions (IDRs) from a given sequence. For feature prediction, S-Pred uses multiple sequence alignment (MSA) of a query sequence as an input. The MSA input is converted to features by the MSA Transformer, which is a protein language model that uses an attention mechanism. A long short-term memory (LSTM) was employed to produce the final prediction. The performance of S-Pred was evaluated on several test sets, and the program consistently provided accurate predictions. The accuracy of the SS8 prediction was approximately 76%, and the Pearson’s correlation between the experimental and predicted ASAs was 0.84. Additionally, an IDR could be accurately predicted with an F1-score of 0.514. The program is freely available at https://github.com/arontier/S_Pred_Paper and https://ad3.io as a code and a web server.
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