We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

In this study, we developed two sets of iterative knowledge-based scoring functions, ITScore_pdbbind(rigid) and ITScore_pdbbind(flex), using the rigid decoy structures and flexible decoy structures that were generated based on the protein-ligand complexes in the refined set of PDBbind 2012, respectively. These two scoring functions were evaluated using the 2013 and 2014 CSAR benchmarks. The results were compared with the results of two other scoring functions, Vina scoring function and ITScore, the scoring function that we previously developed from the rigid decoy structures of a smaller set of protein-ligand complexes. A graph-based method was developed to evaluate the root-mean-square deviation (RMSD) between two conformations of the same ligand with different atom names and orders due to different file preparations, and the program is freely available. Our study showed that the two new scoring functions that were developed based on the larger training set yielded significantly improved performance on binding mode predictions. For binding affinity prediction, all the four scoring functions showed protein-dependent performances. We suggest to develop protein family-dependent scoring functions for accurate binding affinity prediction.

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.

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