Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

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Computational prediction of bioactivity has become a critical aspect of modern drug discovery as it mitigates the cost, time, and resources required to find and screen new compounds. Deep Neural Networks (DNN) have recently shown excellent performance in modeling Protein-Ligand Interaction (PLI). However, DNNs are only effective when physically sound descriptions of ligands and proteins are fed into the network for further processing. Furthermore, previous research has not incorporated the secondary structure of the protein in a meaningful manner. In this work, we utilize secondary structure information of the protein which is extracted as the curvature and torsion of the backbone of protein to predict PLI. We demonstrate how our model outperforms previous machine and non-machine learning models on three major datasets: humans, C.elegans, and DUD-E. Visualization of the intermediate layers of our model shows a potential latent space for proteins which extracts important information about the activity of the protein. We further investigate the inner workings of our model by visualizing heatmaps through Grad-CAM. This analysis is adapted to visualize the most important aspects of the protein that the algorithm has learned. We observed that the important residues highlighted by Grad-CAM are the ones responsible for non-covalent interactions with a ligand and is not just confined to the binding site as it also includes allosteric sites and other locations where a ligand interacts. Our new model opens the door in exploration of DNN based on the secondary structure which is not just confined to protein ligand interactions. network for ligands Secondary structure based convolution neural network for proteinsCombined space

Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important frames and optimizing target energy by a global optimum solution. But global optimum may fail to express continuous action or realistically validate how human beings perceive a story. In this paper, we present a bottom-up approach named clip growing for video summarization, which allows users to customize the quality of the video summaries. The proposed approach firstly uses clustering to oversegment video frames into video clips based on their similarity and proximity. Simultaneously, the importance of frames and clips is evaluated from their corresponding dissimilarity and representativeness. Then, video clips and frames are gradually selected according to their energy rank, until reaching the target length. Experimental results on SumMe dataset show that our algorithm can produce promising results compared to existing algorithms. Several video summarizations results are presented in supplementary material.

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dimension to candidates' assessment. Well-designed operative OSPE stations have high reliability and discriminating ability, complementing the evaluation of other clinical skills and domains.