High performance transcription factor-DNA docking with GPU computing

An SVM-based method for assessment of transcription factor-DNA complex models

Background

Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models.

Results

We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models.

Conclusions

A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2538-y) contains supplementary material, which is available to authorized users.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Aurora A is a prognostic marker for breast cancer arising in BRCA2 mutation carriers

Overexpression of the Aurora A kinase has been shown to have prognostic value in breast cancer. Previously, we showed a significant association between AURKA gene amplification and BRCA2 mutation in breast cancer. The aim of this study was to assess the prognostic impact of Aurora A overexpression on breast cancer arising in BRCA2 mutation carriers. Aurora A expression was evaluated by immunohistochemistry on breast tumour tissue microarrays from 107 BRCA2 999del5 mutation carriers and 284 of sporadic origin. Prognostic value of Aurora A nuclear staining was estimated in relation to clinical markers and adjuvant treatment, using multivariate Cox's proportional hazards ratio regression model. BRCA2 wild‐type allele loss was measured by TaqMan in BRCA2 mutated tumour samples. All statistical tests were two sided. Multivariate analysis of breast cancer‐specific survival, including proliferative markers and treatment, indicated independent prognostic value of Aurora A nuclear staining for BRCA2 mutation carriers (hazards ratio = 7.06; 95% confidence interval = 1.23–40.6; p = 0.028). Poor breast cancer‐specific survival of BRCA2 mutation carriers was found to be significantly associated with combined Aurora A nuclear expression and BRCA2 wild type allele loss in tumours (p < 0.001). Multivariate analysis indicated independent prognostic value of both positive Aurora A nuclear staining (hazards ratio = 10.09; 95% confidence interval = 1.19–85.4, p = 0.034) and BRCA2 wild type allele loss (hazards ratio = 9.63; 95% confidence interval = 1.81–51.0, p = 0.008) for BRCA2 mutation carriers. Aurora A nuclear expression was found to be a significant prognostic marker for BRCA2 mutation carriers, independent of clinical parameters and adjuvant treatment. Our conclusion is that treatment benefits for BRCA2 mutation carriers and sporadic breast cancer patients with Aurora A positive tumours may be enhanced by giving attention to Aurora A targeted treatment.

Stochastic simulation of notch signaling reveals novel factors that mediate the differentiation of neural stem cells

Notch signaling controls cell fate decisions and regulates multiple biological processes, such as cell proliferation, differentiation, and apoptosis. Computational modeling of the deterministic simulation of Notch signaling has provided important insight into the possible molecular mechanisms that underlie the switch from the undifferentiated stem cell to the differentiated cell. Here, we constructed a stochastic model of a Notch signaling model containing Hes1, Notch1, RBP-Jk, Mash1, Hes6, and Delta. mRNA and protein were represented as a discrete state, and 334 reactions were employed for each biochemical reaction using a graphics processing unit-accelerated Gillespie scheme. We employed the tuning of 40 molecular mechanisms and revealed several potential mediators capable of enabling the switch from cell stemness to differentiation. These effective mediators encompass different aspects of cellular regulations, including the nuclear transport of Hes1, the degradation of mRNA (Hes1 and Notch1) and protein (Notch1), the association between RBP-Jk and Notch intracellular domain (NICD), and the cleavage efficiency of the NICD. These mechanisms overlap with many modifiers that have only recently been discovered to modulate the Notch signaling output, including microRNA action, ubiquitin-mediated proteolysis, and the competitive binding of the RBP-Jk-DNA complex. Moreover, we identified the degradation of Hes1 mRNA and nuclear transport of Hes1 as the dominant mechanisms that were capable of abolishing the cell state transition induced by other molecular mechanisms.

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.