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Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/., Author summary Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering. Very few methods have been developed for predicting the effects of mutations on protein-DNA binding affinity. Here we report a new computational method, PRedicts the Effects of single Mutations on Protein-DNA Interactions (PremPDI). The core of the PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms that makes the PremPDI algorithm efficient and being fast enough to handle large number of cases. The performance of the PremPDI protocol was tested against experimentally determined binding free energy changes of 219 mutations from 49 protein-DNA complexes and yields very good correlation coefficient. The PremPDI webserver is available to the community at http://lilab.jysw.suda.edu.cn/research/PremPDI/.

Background Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. Results To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. Conclusions In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease-miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic. In this study, we take advantage of known disease-miRNA associations combined with a large number of experimentally validated miRNA-target associations, and further develop a novel disease-miRNA-target heterogeneous network for identifying disease-related miRNAs. The leave-one-out cross validation experiment and several statistical measures demonstrate that our method can effectively identify potential disease-related miRNAs. Furthermore, the good predictive performance of 15 common diseases and the manually confirmed analyses of the top 30 candidates of hepatocellular carcinoma, ovarian neoplasms and breast neoplasms further provide convincing evidence of the practical ability of our method. The source code implemented by our method is freely available at: .

Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm’s robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail.

Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms.

Protein phosphorylation catalyzed by kinases plays essential roles in various intracellular processes. With an increasing number of phosphorylation sites verified experimentally by high-throughput technologies and assigned as substrates of specific kinases, prediction of potential kinase–substrate relations (KSRs) attracts increasing attention. Although a large number of computational methods have been designed, most of them only focus on local protein sequence information. A few KSR prediction approaches integrate protein–protein interaction and protein sequence information into existing machine learning algorithms at the cost of high feature dimensions or reduced sensitivity. In this work, we introduce two novel heterogeneous networks, HetNet-PPI and HetNet-SEQ, by incorporating PPI and similarity of protein sequences into the kinase–substrate heterogeneous networks, respectively. Based on these two heterogeneous networks, we further propose two new KSR prediction methods, HeteSim-PPI and HeteSim-SEQ, by adopting the HeteSim algorithm, which is recently proposed for relevance measure in heterogeneous information networks. Comprehensive evaluation results of the two methods show that similarity of protein sequences is more effective in improving KSR prediction performance as HeteSim-SEQ outperforms HeteSim-PPI in most cases. Further comparison results demonstrate that HeteSim-SEQ is superior to existing methods including BDT, SVM and iGPS, suggesting the effectiveness of the proposed network-based method in predicting potential KSRs.