RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-oneout cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)

We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.

We analyze the characteristics of protein-protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and noninterface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and noninterface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.

Initiation of eukaryotic chromosome replication follows a spatiotemporal program. The current model suggests that replication origins compete for a limited pool of initiation factors. However, it remains to be answered how these limiting factors are preferentially recruited to early origins. Here, we report that Dbf4 is enriched at early origins through its interaction with forkhead transcription factors Fkh1 and Fkh2. This interaction is mediated by the Dbf4 C terminus and was successfully reconstituted in vitro. An interaction-defective mutant, , phenocopies alleles in terms of origin firing. Remarkably, genome-wide replication profiles reveal that the direct fusion of the DNA-binding domain (DBD) of Fkh1 to Dbf4 restores the Fkh-dependent origin firing but interferes specifically with the pericentromeric origin activation. Furthermore, Dbf4 interacts directly with Sld3 and promotes the recruitment of downstream limiting factors. These data suggest that Fkh1 targets Dbf4 to a subset of noncentromeric origins to promote early replication in a manner that is reminiscent of the recruitment of Dbf4 to pericentromeric origins by Ctf19.

Cyclooxygenase (COX), commonly overexpressed in cancer cells, is a major lipid peroxidizing enzyme that metabolizes polyunsaturated fatty acids (ω-3s and ω-6s). The COX-catalyzed free radical peroxidation of arachidonic acid (ω-6) can produce deleterious metabolites (e.g. 2-series prostaglandins) that are implicated in cancer development. Thus, COX inhibition has been intensively investigated as a complementary therapeutic strategy for cancer. However, our previous study has demonstrated that a free radical-derived by product (8-hydroxyoctanoic acid) formed from COX-catalyzed peroxidation of dihomo-γ-linolenic acid (DGLA, the precursor of arachidonic acid) can inhibit colon cancer cell growth. We thus hypothesize that the commonly overexpressed COX in cancer (~90% of colon cancer patients) can be taken advantage to suppress cell growth by knocking down delta-5-desaturase (D5D, a key enzyme that converts DGLA to arachidonic acid). In addition, D5D knockdown along with DGLA supplement may enhance the efficacy of chemotherapeutic drugs. After knocking down D5D in HCA-7 colony 29 cells and HT-29 cells (human colon cancer cell lines with high and low COX levels, respectively), the antitumor activity of DGLA was significantly enhanced along with the formation of a threshold range (~0.5–1.0 µM) of 8-hydroxyoctanoic acid. In contrast, DGLA treatment did not inhibit cell growth when D5D was not knocked down and only limited amount of 8-hydroxyoctanoic acid was formed. D5D knockdown along with DGLA treatment also enhanced the cytotoxicities of various chemotherapeutic drugs, including 5-fluorouracil, regorafenib, and irinotecan, potentially through the activation of pro-apoptotic proteins, e.g. p53 and caspase 9. For the first time, we have demonstrated that the overexpressed COX in cancer cells can be utilized in suppressing cancer cell growth. This finding may provide a new option besides COX inhibition to optimize cancer therapy. The outcome of this translational research will guide us to develop a novel ω-6-based diet-care strategy in combination with current chemotherapy for colon cancer prevention and treatment.