SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information

A Transfer Learning-Based Approach for Lysine Propionylation Prediction

Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.

Screening from the world's largest TCM database against H1N1 virus

The swine influenza virus (H1N1) 2009 pandemic highlights the importance of having effective anti-viral strategies. Recently, oseltamivir (Tamiflu) resistant influenza viruses are identified; which further emphasizes the urgency in developing new antiviral agents. In influenza virus replication cycle, viral surface glycoprotein, hemagglutinin, is responsible for viral entry into host cells. Hence, a potentially effective antiviral strategy is to inhibit viral entry mechanism. To develop novel antiviral agent that inhibits viral entry, we analyzed 20,000 traditional Chinese medicine (TCM) ingredients in hemagglutinin subtype H1 sialic acid binding site found on H1N1 virus. We then performed molecular dynamics simulations to investigate receptor-ligand interaction of the candidates obtained from docking. Here, we report three TCM derivatives that have high binding affinities to H1 sialic acid binding site residues based on structure-based calculations. The top three derivatives, xylopine_2, rosmaricine_14 and rosmaricine_15, all have an amine group that interact with Glu83 and a pyridinium group that interact with Asp103. Molecular dynamics simulations show that these derivatives form strong hydrogen bonding with Glu83 but interact transiently with Asp103. We therefore suggest that an enhanced hemagglutinin inhibitor, based on our scaffold, should be designed to bind both Glu83 and Asp103 with high affinity.