Algorithm selection for protein-ligand docking: strategies and analysis on ACE.

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA's parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance.

The roles of PAI-1 gene polymorphisms in atherosclerotic diseases: A systematic review and meta-analysis involving 149,908 subjects.

BACKGROUND: The roles of plasminogen activator inhibitor-1 (PAI-1) gene polymorphisms in atherosclerotic diseases were intensively analyzed, but the results of these studies were inconsistent. Therefore, we performed this study to better assess the relationship between PAI-1 genetic variations and atherosclerosis. METHODS: Eligible studies were searched in PubMed, Medline, Embase and Web of Science. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess relationship between PAI-1 polymorphisms and atherosclerotic diseases. RESULTS: Ninety-nine studies involving 62,739 cases and 87,169 controls were finally included. Significant associations with the risk of atherosclerosis were detected for the rs2227631 polymorphism in the dominant model (95% CI 0.84-1.00), for the rs1799889 polymorphism in the dominant (95% CI 1.01-1.18), recessive (95% CI 0.90-0.98) and allele (95% CI 1.01-1.12) models. Further subgroup analyses based on type of disease and ethnicity of participants suggested that the rs2227631 polymorphism was significantly associated with the risk of coronary artery disease in the dominant (95% CI 0.71-0.94) and allele (95% CI 0.80-0.94) models, whereas the rs1799889 polymorphism was significantly associated with the risk of myocardial infarction (dominant model: 95% CI 1.09-1.57; recessive model: 95% CI 0.71-0.96; allele model: 95% CI 1.05-1.28) and cerebral infarction (dominant model: 95% CI 1.68-3.51; additive model: 95% CI 0.39-0.77; allele model: 95% CI 1.23-2.00). Moreover, the rs1799889 polymorphism was also significantly correlated with the risk of atherosclerosis in both Asians (dominant model: 95% CI 1.10-1.83; allele model: 95% CI 1.03-1.41) and Caucasians (recessive model: 95% CI 0.87-0.97; allele model: 95% CI 1.01-1.12). CONCLUSION: In conclusion, our findings indicate that PAI-1 rs2227631 and rs1799889 polymorphisms may serve as genetic biomarkers of atherosclerotic diseases.

Current advance in biological production of malic acid using wild type and metabolic engineered strains.

Malic acid (2-hydroxybutanedioic acid) is a four-carbon dicarboxylic acid, which has attracted great interest due to its wide usage as a precursor of many industrially important chemicals in the food, chemicals, and pharmaceutical industries. Several mature routes for malic acid production have been developed, such as chemical synthesis, enzymatic conversion and biological fermentation. With depletion of fossil fuels and concerns regarding environmental issues, biological production of malic acid has attracted more attention, which mainly consists of three pathways, namely non-oxidative pathway, oxidative pathway and glyoxylate cycle. In recent decades, metabolic engineering of model strains, and process optimization for malic acid production have been rapidly developed. Hence, this review comprehensively introduces an overview of malic acid producers and highlight some of the successful metabolic engineering approaches.

On the Opportunities and Challenges in Microwave Medical Sensing and Imaging.

Widely used medical imaging systems in clinics currently rely on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The aforementioned technologies provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. Microwave sensing and imaging (MSI) is an alternative method based on nonionizing electromagnetic (EM) signals operating over the frequency range covering hundreds of megahertz to tens of gigahertz. The advantages of using EM signals are low health risk, low cost implementation, low operational cost, ease of use, and user friendliness. Advancements made in microelectronics, material science, and embedded systems make it possible for miniaturization and integration into portable, handheld, mobile devices with networking capability. MSI has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, cancer detection, and localization of in-body RF sources. The fundamental notion of MSI is that it exploits the tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed.