Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

Cytochrome P450 17A1 (CYP17A1) is associated in the steroid hormone biosynthesis in human. As cell proliferation of prostate cancer in response to androgen steroid, an inhibition of CYP17A1 becomes an alternative approach to inhibit biosynthesis of androgen and support treatment of prostate cancer. However, biology-driven inhibitor development of prostate cancer is poorly elucidated. The aims of this study are to address structural differences at atomic-level between CYP17A1 and inhibitors i.e., abiraterone and TOK-001, and further investigate the effect of point mutation of CYP17A1 on the active site stability and the local interactions that are hydrophobic interaction and hydrogen bonding throughout molecular dynamics (MD) simulation. After performing multiple comparisons among four different complexes across CYP17A1 and inhibitors, interestingly TOK-001 oriented toward the active pocket and formed larger volume with I-helix of CYP17A1 than abiraterone, whereas abiraterone showed tighter binding and more active site stability. Considering on the effect of hydrophobic interaction and hydrogen bonding between abiraterone and CYP17A1, the key residues of Phe114, Ile371, Val482, and Asn202 were identified. This contributes into tight binding interactions; however abiraterone is effectively weakened along with the global conformation mobility increased in A105L mutation. Surprisingly, overall conformation of the CYP17A1 remained stable when bound to TOK-001. This basic knowledge can guide future experiments on design of efficient inhibitors for CYP17A1, which provides theoretical basis of androgen-dependent disease therapy.

Beyond comparative genomics, we identified 85 sugar transporter genes in Cordyceps militaris, clustering into nine subfamilies as sequence- and phylogenetic-based functional classification, presuming the versatile capability of the fungal growths on a range of sugars. Further analysis of the global gene expression patterns of C. militaris showed 123 genes were significantly expressed across the sucrose, glucose, and xylose cultures. The sugar transporters specific for pentose were then identified by gene-set enrichment analysis. Of them, the putative pentose transporter, CCM_06358 gene, was highest expressed in the xylose culture, and its functional role in xylose transport was discovered by the analysis of conserved structural motifs. In addition, a battery of molecular modeling methods, including homology modeling, transport pathway analysis, residue interaction network combined with molecular mechanics Poisson–Boltzmann surface area simulation (MM-PBSA), was implemented for probing the structure and function of the selected pentose transporter (CCM_06358) as a representative of sugar transportome in C. militaris. Considering the network bottlenecks and structural organizations, we further identified key amino acids (Phe38 and Trp441) and their interactions with other residues, contributing the xylose transport function, as verified by binding free energy calculation. The strategy used herein generated remarkably valuable biological information, which is applicable for the study of sugar transportome and the structure engineering of targeted transporter proteins that might link to the production of bioactive compounds derived from xylose metabolism, such as cordycepin.

Background. Central precocious puberty (CPP) is defined by gonadotropin-dependent development of secondary sexual characteristics before the age of 8 years in girls and 9 years in boys. MKRN3 and DLK1 are two genes, disease-causing variants of which have recently been discovered to cause idiopathic CPP. Methods. We screened 173 Chinese patients (9 males and 164 females; 9 familial and 164 sporadic) with ICPP and 43 patients (9 males and 34 females; 3 familial and 40 sporadic) with early puberty for variants in MKRN3. We also screened 19 patients with ICPP and early puberty for variants of DLK1 (17 males and 2 females; 5 familial and 14 sporadic). Results. We identified four novel missense variants of MKRN3, c.1138G > A (p.Glu380Lys), c.1420T > A (p.Leu474Met), c.673C > G (p.Leu225Val), and c.1071C > G (p.Ile357Met) in two sporadic cases and three familial cases. According to ACMG standards, two MKRN3 variant (p.Glu380Lys and p.Ile357Met) are likely pathogenic, and two others are of uncertain significance. We also performed bioinformatic analysis to evaluate the impact of variants on MKRN3 protein structures, which showed that Ile357Met locates at the zinc-binding region (C3HC4 RING finger motif), while Glu380Lys is spatially extremely close to the C3HC4 RING finger, MKRN-specific Cys-His domain, and the third C3H1 zinc-finger motif region. Per Glu380Lys, Glu with negative charges has been changed into Lys with positive charges, which may affect the hydrogen bond formation between amino acids and the stability of the local structure, thus affecting the binding of zinc iron to MKRN3 protein. Besides, we did not identify any variants of DLK1 gene in our patients. Conclusions. In this study, we report four novel MKRN3 variants in patients with ICPP. Moreover, we did not find any variants of DLK1 gene. Variants of MKRN3 are relatively uncommon in Chinese ICPP patients.

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations inALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis ofALPLmutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of theALPLmutations on protein dynamics and function. Statistical analysis of molecular features computed for theALPLmutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested thatALPLmutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects ofALPLmutations and role of allostery in the pathogenesis of HPPs.