Several long noncoding RNAs (lncRNA) are abrogated in cancer but their precise contributions to oncogenesis are still emerging. Here we report that the lncRNA MALAT1 is upregulated in hepatocellular carcinoma (HCC) and acts as a proto-oncogene through Wnt pathway activation and induction of the oncogenic splicing factor SRSF1. Induction of SRSF1 by MALAT1 modulates SRSF1 splicing targets, enhancing the production of anti-apoptotic splicing isoforms and activating the mTOR pathway by modulating the alternative splicing of S6K1. Inhibition of SRSF1 expression or mTOR activity abolishes the oncogenic properties of MALAT1, suggesting that SRSF1 induction and mTOR activation are essential for MALAT1 induced transformation. Our results reveal a mechanism by which lncRNA MALAT1 acts as a proto-oncogene in HCC, modulating oncogenic alternative splicing through SRSF1 upregulation.

We present a very efficient rigid "unbound" soft docking methodology, which is based on detection of geometric shape complementarity, allowing liberal steric clash at the interface. The method is based on local shape feature matching, avoiding the exhaustive search of the 6D transformation space. Our experiments at CAPRI rounds 1 and 2 show that although the method does not perform an exhaustive search of the 6D transformation space, the "correct" solution is never lost. However, such a solution might rank low for large proteins, because there are alternatives with significantly larger geometrically compatible interfaces. In many cases this problem can be resolved by successful a priori focusing on the vicinity of potential binding sites as well as the extension of the technique to flexible (hinge-bent) docking. This is demonstrated in the experiments performed as a lesson from our CAPRI experience.

Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson’s disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To “humanize” this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking/ER quality control and mRNA metabolism/translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control/function, metal ion transport, transcriptional regulation and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9, VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2 and EIF4G1/PARK18) were confirmed in patient iPS cell-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms, and may facilitate patient stratification for targeted therapy.

The unique properties of fullerenes have raised the interest of using them for biomedical applications. Within this framework, the interactions of fullerenes with proteins have been an exciting research target, yet little is known about how native proteins can bind fullerenes, and what is the nature of these interactions. Moreover, though some proteins have been shown to interact with fullerenes, up to date, no crystal structure of such complexes was obtained. Here we report docking studies aimed at examining the interactions of fullerene in two forms (C60 nonsubstituted fullerene and carboxyfullerene) with four proteins that are known to bind fullerene derivatives: HIV protease, fullerene-specific antibody, human serum albumin, and bovine serum albumin. Our work provides docking models with detailed binding pockets information, which closely match available experimental data. We further compare the predicted binding sites using a novel multiple binding site alignment method. A high similarity between the physicochemical properties and surface geometry was found for fullerene's binding sites of HIV protease and the human and bovine serum albumins.

We present a novel method for multiple alignment of protein structures and detection of structural motifs. To date, only a few methods are available for addressing this task. Most of them are based on a series of pairwise comparisons. In contrast, MASS (Multiple Alignment by Secondary Structures) considers all the given structures at the same time. Exploiting the secondary structure representation aids in filtering out noisy results and in making the method highly efficient and robust. MASS disregards the sequence order of the secondary structure elements. Thus, it can find non-sequential and even non-topological structural motifs. An important novel feature of MASS is subset alignment detection: It does not require that all the input molecules be aligned. Rather, MASS is capable of detecting structural motifs shared only by a subset of the molecules. Given its high efficiency and capability of detecting subset alignments, MASS is suitable for a broad range of challenging applications: It can handle large-scale protein ensembles (on the order of tens) that may be heterogeneous, noisy, topologically unrelated and contain structures of low resolution.