细胞周期依赖性激酶2与其抑制剂的残基特异性结合的计算分析

细胞周期依赖性激酶2(CDK2)是细胞周期调控中的关键大分子.在癌细胞中,CDK2常被过度表达,因此抑制CDK2的表达是治疗乳腺癌、白血病和淋巴瘤等多种癌症有效的方法,在分子水平上定量表征CDK2与其抑制剂之间的相互作用,可为药物开发提供更深入的蛋白质与抑制剂的相互作用机制和线索,本文采用计算丙氨酸扫描和相互作用熵方法,研究CDK2与13种抑制剂结合的微观机制,该方法得到的结合自由能与实验值之间的相关系数为0.76～0.83.计算结果揭示了这13种抑制剂中的两种结合模式,即范德华占优势和静电占优势.通过将总能量分解为每个残基的贡献,确定了结合过程中五个疏水残基为热点残基,同时发现了能够决定CDK2与抑制剂结合强度的残基.     

Raman spectroscopic analysis differentiates between breast cancer cell lines

Breast cancer incident rates are increasing in women worldwide with the highest incidence rates reported in developing countries. Major breast cancer screening approaches like mammography, ultrasound, clinical breast examination (CBE) and magnetic resonance imaging (MRI) are currently used but have their own limitations. Optical spectroscopy has attained great attention from biomedical researchers in recent years due to its non‐invasive and non‐destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. Raman spectroscopy, a vibrational spectroscopic approach, has been used to explore the chemical fingerprints of different biological tissues including normal and malignant types. This approach was used to characterize and differentiate two breast cancer and one normal breast cell lines (MDA‐MB‐436, MCF‐7 and MCF‐10A) using dispersive Raman spectroscopy. Raman spectra of the cell lines have revealed that basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and protein Raman peaks were found to differ in intensity, and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. We have shown that a combination of Raman spectroscopy and chemometrics are capable of differentiation between breast cancer cell lines. These variations may be useful in identifying new spectral markers to differentiate different subtypes of breast cancer although this needs confirmation in a larger panel of cell lines as well as clinical material. Copyright © 2015 John Wiley & Sons, Ltd.     

The Complex Structure of the Pharmacological Drug–Disease Network

The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).     

Efficient perturbation analysis of elastic network models – Application to acetylcholinesterase of T. californica

Elastic network models in their different flavors have become useful models for the dynamics and functions of biomolecular systems such as proteins and their complexes. Perturbation to the interactions occur due to randomized and fixated changes (in molecular evolution) or designed modifications of the protein structures (in bioengineering). These perturbations are modifications in the topology and the strength of the interactions modeled by the elastic network models. We discuss how a naive approach to compute properties for a large number of perturbed structures and interactions by repeated diagonalization can be replaced with an identity found in linear algebra. We argue about the computational complexity and discuss the advantages of the protocol. We apply the proposed algorithm to the acetylcholinesterase, a well-known enzyme in neurobiology, and show how one can gain insight into the “breathing dynamics” of a structural funnel necessary for the function of the protein. The computational speed-up was a 60-fold increase in this example.     

Set-Wise Differential Interaction between Copy Number Alterations and Gene Expressions of Lower-Grade Glioma Reveals Prognosis-Associated Pathways

The integrative analysis of copy number alteration (CNA) and gene expression (GE) is an essential part of cancer research considering the impact of CNAs on cancer progression and prognosis. In this research, an integrative analysis was performed with generalized differentially coexpressed gene sets (gdCoxS), which is a modification of dCoxS. In gdCoxS, set-wise interaction is measured using the correlation of sample-wise distances with Renyi’s relative entropy, which requires an estimation of sample density based on omics profiles. To capture correlations between the variables, multivariate density estimation with covariance was applied. In the simulation study, the power of gdCoxS outperformed dCoxS that did not use the correlations in the density estimation explicitly. In the analysis of the lower-grade glioma of the cancer genome atlas program (TCGA-LGG) data, the gdCoxS identified 577 pathway CNAs and GEs pairs that showed significant changes of interaction between the survival and non-survival group, while other benchmark methods detected lower numbers of such pathways. The biological implications of the significant pathways were well consistent with previous reports of the TCGA-LGG. Taken together, the gdCoxS is a useful method for an integrative analysis of CNAs and GEs.     

Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-Genetic Heterogeneity in Prostate Cancer Cells

Non-genetic heterogeneity is emerging as a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving (a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, (b) multistability in epithelial–mesenchymal transition (EMT), and (c) Notch–Delta–Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to monostability, bistability, or oscillations in the levels of AR, as well as propagation of oscillations to EMT dynamics. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients.     

Dual drug-loaded paclitaxel–thymoquinone nanoparticles for effective breast cancer therapy

The present study highlights the beneficial synergistic blend of anticancer drug paclitaxel (PTX) and thymoquinone (TQ) in MCF-7 breast cancer cells. We aimed to augment the therapeutic index of PTX using a polymeric nanoparticle system loaded with PTX and TQ. PLGA nanoparticles encapsulating the two drugs, individually or in combination, were prepared by single emulsion solvent evaporation method. The formulated nanoparticles were homogenous with an overall negative charge and their size ranging between 200 and 300 nm. Entrapment efficiency of PTX and TQ in the dual drug-loaded nanoparticles was found to be 82.4 ± 2.18 and 65.8 ± 0.45 %, respectively. The release kinetics of PTX and TQ from the nanoparticles exhibited a biphasic pattern characterised by an initial burst, followed by a gradual and continuous release. The anticancer activity of nanoparticles encapsulating both the drugs was higher as compared to the free drugs in MCF-7 breast cancer cells. The combination index for the dual drug-loaded NPs was found to be 0.688 which is indicative of synergistic interaction. Thus, here, we propose the synthesis and use of dual drug-loaded TQ and PTX NPs which exhibits enhanced anticancer activity and can additionally help to alleviate the toxic effects of PTX by lowering its effective dose.     

Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms

Most common pathologies in humans are not caused by the mutation of a single gene, rather they are complex diseases that arise due to the dynamic interaction of many genes and environmental factors. This plethora of interacting genes generates a complexity landscape that masks the real effects associated with the disease. To construct dynamic maps of gene interactions (also called genetic regulatory networks) we need to understand the interplay between thousands of genes. Several issues arise in the analysis of experimental data related to gene function: on the one hand, the nature of measurement processes generates highly noisy signals; on the other hand, there are far more variables involved (number of genes and interactions among them) than experimental samples. Another source of complexity is the highly nonlinear character of the underlying biochemical dynamics. To overcome some of these limitations, we generated an optimized method based on the implementation of a Maximum Entropy Formalism (MaxEnt) to deconvolute a genetic regulatory network based on the most probable meta-distribution of gene-gene interactions. We tested the methodology using experimental data for Papillary Thyroid Cancer (PTC) and Thyroid Goiter tissue samples. The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions. The group of observed interactions was validated by several (mostly in silico) means and sources. For the associated Papillary Thyroid Cancer Gene Regulatory Network (PTC-GRN) the majority of the nodes (genes) have very few links (interactions) whereas a small number of nodes are highly connected. PTC-GRN is also characterized by high clustering coefficients and network heterogeneity. These properties have been recognized as characteristic of topological robustness, and they have been largely described in relation to biological networks. A number of biological validity outcomes are discussed with regard to both the inferred model and the PTC.     

分子动力学模拟研究方解石表面润湿性反转机理

利用分子动力学模拟技术从分子尺度探究方解石表面润湿性反转机理.首先,研究方解石表面润湿性反转过程;而后,从原油分子-方解石表面与原油分子-原油分子/水分子相互作用两个方面系统揭示方解石表面润湿性反转机理.结果:(1)水分子能够驱离方解石表面弱吸附的非极性分子造成润湿性的改变,但不能驱离强吸附的极性分子使润湿性反转难以实现;(2)原油分子极性越强与方解石表面相互作用越强,极性分子与方解石表面之间主要为静电力,非极性分子与方解石表面之间主要为范德华力;(3)原油分子极性越相近分子之间的相互作用越强,分子极性相差越大分子之间的相互作用越弱.非极性分子之间主要是范德华力,极性分子之间主要是静电力;(4)原油分子在方解石表面和水分子的共同作用下形成乙酸-吡啶-水-甲苯-己烷的稳定吸附序列.本研究为靶向提高采收率技术的设计与应用提供理论基础.     

Predicting disease-related genes by topological similarity in human protein-protein interaction network

Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.     

Bosonic superfluid-insulator transition in continuous space

We investigate the zero-temperature phase diagram of interacting Bose gases in the presence of a simple cubic optical lattice, going beyond the regime where the mapping to the single-band Bose-Hubbard model is reliable. Our computational approach is a new hybrid quantum Monte?Carlo method which combines algorithms used to simulate homogeneous quantum fluids in continuous space with those used for discrete lattice models of strongly correlated systems. We determine the critical interaction strength and optical lattice intensity where the superfluid-to-insulator transition takes place, considering also the regime of shallow optical lattices and strong interatomic interactions. The implications of our findings for the supersolid state of matter are discussed.     

Dynamic force spectroscopy of protein-DNA interactions by unzipping DNA

We demonstrate the first site-specific single-molecule characterization of the prominent activation barrier for the disruption of a protein-DNA binding complex. We achieved this new capability by combining dynamic force spectroscopy with unzipping force analysis of protein association and used the combination to investigate restriction enzyme binding to specific DNA sites. Analysis revealed lifetimes and interaction distances for three protein-DNA interactions. This new method is able to distinguish protein-DNA binding complexes on a site-specific, single-molecule basis.     

An integrated approach for the systematic evaluation of polymeric nanoparticles in healthy and diseased organisms

Innovative strategies that utilize nanoparticles (NPs) for a better delivery of drugs and to improve their therapeutic efficacy have been widely studied in many clinical fields, including oncology. To develop safe and reliable devices able to reach their therapeutic target, a hierarchical characterization of NP interactions with biological fluids, cells, and whole organisms is fundamental. Unfortunately, this aspect is often neglected and the development of standardized characterization methods would be of fundamental help to better elucidate the potentials of nanomaterials, even before the loading of the drugs. Here, we propose a multimodal in vitro/in vivo/ex vivo platform aimed at evaluating these interactions for the selection of the most promising NPs among a wide series of materials. To set the system, we used non-degradable fluorescent poly(methyl-methacrylate) NPs of different sizes (50, 100, and 200 nm) and surface charges (positive and negative). First we studied NP stability in biological fluids. Then, we evaluated NP interaction with two cell lines of triple-negative breast cancer (TNBC), 4T1, and MDA-MB231.1833, respectively. We found that NPs internalize in TNBC cells depending on their physico-chemical properties without toxic effects. Finally, we studied NP biodistribution in terms of tissue migration and progressive clearance in breast-cancer bearing mice. The use of highly stable poly(methyl-methacrylate) NPs enabled us to track them for a long time in cells and animals. The application of this platform to other nanomaterials could provide innovative suggestions for the development of a systematic method of characterization to select the most reliable nanodrug candidates for biomedical applications.     

Length,protein–protein interactions,and complexity

The evolutionary reason for the increase in gene length from archaea to prokaryotes to eukaryotes observed in large-scale genome sequencing efforts has been unclear. We propose here that the increasing complexity of protein–protein interactions has driven the selection of longer proteins, as they are more able to distinguish among a larger number of distinct interactions due to their greater average surface area. Annotated protein sequences available from the SWISS-PROT database were analyzed for 13 eukaryotes, eight bacteria, and two archaea species. The number of subcellular locations to which each protein is associated is used as a measure of the number of interactions to which a protein participates. Two databases of yeast protein–protein interactions were used as another measure of the number of interactions to which each S. cerevisiae protein participates. Protein length is shown to correlate with both number of subcellular locations to which a protein is associated and number of interactions as measured by yeast two-hybrid experiments. Protein length is also shown to correlate with the probability that the protein is encoded by an essential gene. Interestingly, average protein length and number of subcellular locations are not significantly different between all human proteins and protein targets of known, marketed drugs. Increased protein length appears to be a significant mechanism by which the increasing complexity of protein–protein interaction networks is accommodated within the natural evolution of species. Consideration of protein length may be a valuable tool in drug design, one that predicts different strategies for inhibiting interactions in aberrant and normal pathways.     

TAaCGH Suite for Detecting Cancer—Specific Copy Number Changes Using Topological Signatures

Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basal subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.     

Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast

Dynamic contrast-enhanced 2D MR imaging of the breast has shown high sensitivity and specificity for the detection and characterization of breast lesions. We investigated the ability of a dynamic fast 3D MR imaging technique that repeatedly scans the whole breast in 44-s intervals without an interscan delay time to obtain similar sensitivity and specificity as 2D imaging. Fifty-six patients scheduled for breast biopsy were entered into the study, and 83 lesions detected by 3D dynamic scanning were biopsied. Dynamic 3D contrast-enhanced breast imaging with subtraction detected and correctly classified all 23 cancers, and 44 of the 60 benign lesions yielding a sensitivity of 100%, a specificity of 73%, and a 100% predictive negative value. The enhancement profiles of metastatic lymph nodes were similar to those of primary cancer. This technique allowed detection of multifocal and multicentric lesions and did not require a priori knowledge of lesion location. These results indicate that dynamic contrast-enhanced 3D MRI of the whole breast is a useful and economically feasible method for staging breast cancer, providing a comprehensive noninvasive method for total evaluation of the breast and axilla in patients considering breast conservation surgery or lumpectomy.     

Nonlinear pressure dependence of the interaction potential of dense protein solutions

The influence of pressure on the structure and protein-protein interaction potential of dense protein solutions was studied and analyzed using small-angle x-ray scattering in combination with a liquid state theoretical approach. The structural as well as the interaction parameters of dense lysozyme solutions are affected by pressure in a nonlinear way. The structural properties of water lead to a modification of the protein-protein interactions below 4?kbar, which might have significant consequences for the stability of proteins in extreme natural environments.     

Coarse-grain reconstruction of genetic networks from expression levels

In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at a coarse-grain level from high throughput gene expression data. The method provides the a posteriori probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to choose those genes that will be perturbed in subsequent experiments in order to refine our knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by in numero experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as recovering a small subnetwork of interest by performing perturbation over selected genes.