用于聚合酶链式反应(PCR)引物设计的计算机程序

 本文报道了两个用于PCR引物设计的计算机程序PCRDESN和PCRDESNA。PCRDESN程序主要从以下4个方面评价用户自己设计的一对引物的质量:(1)引物内的碱基反向重复或发夹结构,(2)两个引物之间的碱基互补配对,(3)两个引物之间的同源性,(4)引物的碱基组成及特点和T_m值计算。通过用多例文献发表的及本院有关实验室提供的引物对序列的验证,确定了程序的运算参数,证明该程序能较好地检验引物对的质量和解释某些PCR实验失败的原因。PCRDESNA程序采用逐级优化的方法和比PCRDESN所选用的更严紧的引物选择参数对用户提供的核酸序列进行快速检索,以确定所有可能的和合适的引物对。     

Chemical genomics: a challenge for de novo drug design

De novo design provides an in silico toolkit for the design of novel small molecular structures to a set of specified structural constraints. With the avalanche of bioinformatics data, de novo design is ideally suited for exploring molecules that could be useful for chemical genomics. The design process involves manipulation of the input, modification of structural constraints, and further processing of the de novo generated molecules using various modular toolkits. The development of a theoretical framework for each of these stages will provide novel practical solutions to the problem of creating compounds with maximal chemical diversity. This short review describes the fundamental problems encountered in the application of novel chemical design technologies to chemical genomics by means of a formal representation. This notation helps to outline and clarify ideas and hypotheses that can then be explored using mathematical algorithms. It is only by developing this rigorous foundation that in silico design can progress in a rational way.     

SIRT1 Activation by Small Molecules: KINETIC AND BIOPHYSICAL EVIDENCE FOR DIRECT INTERACTION OF ENZYME AND ACTIVATOR?

SIRT1 is a protein deacetylase that has emerged as a therapeutic target for the development of activators to treat diseases of aging. SIRT1-activating compounds (STACs) have been developed that produce biological effects consistent with direct SIRT1 activation. At the molecular level, the mechanism by which STACs activate SIRT1 remains elusive. In the studies reported herein, the mechanism of SIRT1 activation is examined using representative compounds chosen from a collection of STACs. These studies reveal that activation of SIRT1 by STACs is strongly dependent on structural features of the peptide substrate. Significantly, and in contrast to studies reporting that peptides must bear a fluorophore for their deacetylation to be accelerated, we find that some STACs can accelerate the SIRT1-catalyzed deacetylation of specific unlabeled peptides composed only of natural amino acids. These results, together with others of this study, are at odds with a recent claim that complex formation between STACs and fluorophore-labeled peptides plays a role in the activation of SIRT1 (Pacholec, M., Chrunyk, B., Cunningham, D., Flynn, D., Griffith, D., Griffor, M., Loulakis, P., Pabst, B., Qiu, X., Stockman, B., Thanabal, V., Varghese, A., Ward, J., Withka, J., and Ahn, K. (2010) J. Biol. Chem. 285, 8340–8351). Rather, the data suggest that STACs interact directly with SIRT1 and activate SIRT1-catalyzed deacetylation through an allosteric mechanism.     

Design,synthesis and anti-influenza A virus activity of novel 2,4-disubstituted quinazoline derivatives

Four 2,4-disubstituted quinazoline series containing various amide moieties were designed and synthesized as new anti-influenza A virus agents using the strategies of bio-isosterism and scaffold hopping. Many of them exhibit potent in vitro anti-influenza A virus activity and low cytotoxicity (CC₅₀: >100 μM). Particularly, compounds 10a5 and 17a show better activity (IC₅₀: 3.70–4.19 μM) and higher selective index (SI: >27.03, >23.87, respectively) against influenza A/WSN/33 virus (H1N1), opening a new direction for quinazoline derivatives in anti-influenza A virus field.     

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Structural and Biochemical Characterization of Compounds Inhibiting Mycobacterium tuberculosis Pantothenate Kinase

Mycobacterium tuberculosis, the bacterial causative agent of tuberculosis, currently affects millions of people. The emergence of drug-resistant strains makes development of new antibiotics targeting the bacterium a global health priority. Pantothenate kinase, a key enzyme in the universal biosynthesis of the essential cofactor CoA, was targeted in this study to find new tuberculosis drugs. The biochemical characterizations of two new classes of compounds that inhibit pantothenate kinase from M. tuberculosis are described, along with crystal structures of their enzyme-inhibitor complexes. These represent the first crystal structures of this enzyme with engineered inhibitors. Both classes of compounds bind in the active site of the enzyme, overlapping with the binding sites of the natural substrate and product, pantothenate and phosphopantothenate, respectively. One class of compounds also interferes with binding of the cofactor ATP. The complexes were crystallized in two crystal forms, one of which is in a new space group for this enzyme and diffracts to the highest resolution reported for any pantothenate kinase structure. These two crystal forms allowed, for the first time, modeling of the cofactor-binding loop in both open and closed conformations. The structures also show a binding mode of ATP different from that previously reported for the M. tuberculosis enzyme but similar to that in the pantothenate kinases of other organisms.