The design heatmap: A simple visualization of -optimality design problems

Optimal experimental designs are often formal and specific, and not intuitively plausible to practical experimenters. However, even in theory, there often are many different possible design points providing identical or nearly identical information compared to the design points of a strictly optimal design. In practical applications, this can be used to find designs that are a compromise between mathematical optimality and practical requirements, including preferences of experimenters. For this purpose, we propose a derivative-based two-dimensional graphical representation of the design space that, given any optimal design is already known, will show which areas of the design space are relevant for good designs and how these areas relate to each other. While existing equivalence theorems already allow such an illustration in regard to the relevance of design points only, our approach also shows whether different design points contribute the same kind of information, and thus allows tweaking of designs for practical applications, especially in regard to the splitting and combining of design points. We demonstrate the approach on a toxicological trial where a -optimal design for a dose–response experiment modeled by a four-parameter log-logistic function was requested. As these designs require a prior estimate of the relevant parameters, which is difficult to obtain in a practical situation, we also discuss an adaption of our representations to the criterion of Bayesian -optimality. While we focus on -optimality, the approach is in principle applicable to different optimality criteria as well. However, much of the computational and graphical simplicity will be lost.     

Experimental design methods for bioengineering applications

Experimental design is a form of process analysis in which certain factors are selected to obtain the desired responses of interest. It may also be used for the determination of the effects of various independent factors on a dependent factor. The bioengineering discipline includes many different areas of scientific interest, and each study area is affected and governed by many different factors. Briefly analyzing the important factors and selecting an experimental design for optimization are very effective tools for the design of any bioprocess under question. This review summarizes experimental design methods that can be used to investigate various factors relating to bioengineering processes. The experimental methods generally used in bioengineering are as follows: full factorial design, fractional factorial design, Plackett–Burman design, Taguchi design, Box–Behnken design and central composite design. These design methods are briefly introduced, and then the application of these design methods to study different bioengineering processes is analyzed.     

Accurate de novo design of membrane-traversing macrocycles

1. Download : Download high-res image (230KB)
2. Download : Download full-size image

     

Experimental design for gene expression microarrays

We examine experimental design issues arising with gene expression microarray technology. Microarray experiments have multiple sources of variation, and experimental plans should ensure that effects of interest are not confounded with ancillary effects. A commonly used design is shown to violate this principle and to be generally inefficient. We explore the connection between microarray designs and classical block design and use a family of ANOVA models as a guide to choosing a design. We combine principles of good design and A-optimality to give a general set of recommendations for design with microarrays. These recommendations are illustrated in detail for one kind of experimental objective, where we also give the results of a computer search for good designs.     

Robust design and optimization of retroaldol enzymes

Enzyme catalysts of a retroaldol reaction have been generated by computational design using a motif that combines a lysine in a nonpolar environment with water-mediated stabilization of the carbinolamine hydroxyl and β-hydroxyl groups. Here, we show that the design process is robust and repeatable, with 33 new active designs constructed on 13 different protein scaffold backbones. The initial activities are not high but are increased through site-directed mutagenesis and laboratory evolution. Mutational data highlight areas for improvement in design. Different designed catalysts give different borohydride-reduced reaction intermediates, suggesting a distribution of properties of the designed enzymes that may be further explored and exploited.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

合成肽疫苗的分子设计

合成肽疫苗能克服常规疫苗的缺点,很早就被认为是动物传染病预防用的终极疫苗。然而多年的研究结果表明,合成肽疫苗免疫动物后所起的免疫保护作用并没有象人们当初设想的那样理想,同时证明了构建的合成肽疫苗的抗原性及其免疫原性要受到其自身组成及宿主免疫系统等多种因素的影响。在诱导机体产生免疫的过程中,单一的中和抗原表位是远远不够的,增加中和抗原表位的数目和引入细胞抗原表位将起到必不可少的辅助协同作用。若想提高合成肽疫苗的免疫效果,在搞清合成肽疫苗的免疫机理并在如何利用有限的抗原表位诱导强有力的免疫保护作用等方面需要做进一步深入地研究。     

Hotspot-centric de novo design of protein binders

Protein-protein interactions play critical roles in biology, and computational design of interactions could be useful in a range of applications. We describe in detail a general approach to de novo design of protein interactions based on computed, energetically optimized interaction hotspots, which was recently used to produce high-affinity binders of influenza hemagglutinin. We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the method's performance in natural-interface recapitulation. We show that the method generates binding surfaces that are more conformationally restricted than previous design methods, reducing opportunities for off-target interactions.     

Model building and testing for the change-over design

The two-period cross-over design is discussed within the framework of univariate and multivariate analysis of variance technique; the relations between both procedures are explained. It is shown that all hypotheses of interest can be tested if the design is regarded as a special case of a repeated measurement design. Some features of the n-period change-over design are explained by discussing the model and hypotheses of a three-period design.     

药物设计与同步辐射

当代药物设计是通过阐明药物与靶标相互作用的机理,对药物先导化合物进行改造和优化。利用晶体X射线衍射的方法获得药物与靶标复合物的结构,为药物设计提供最直接有力的依据。同步辐射凭借其高强度、低发散性、波长可调谐性等得天独厚的优势,实现了对药物与靶标复合物结构的高通量测定,大大提高了基于结构的药物设计效率。     

A new efficient mixture screening design for optimization of media

Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett‐Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high‐order interaction among ingredients. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

新基因起源：从自然进化到人工设计

生命体系历经40多亿年的自然进化,创造了无数丰富多彩的功能基因,保障了生命体系的传承与繁荣。然而生命体系的自然进化历程极其缓慢,新的功能基因产生需要数百万年时间,无法满足快速发展的工业生产需求。利用合成生物学技术,研究人员可以依据已知的酶催化机理和蛋白质结构进行全新的基因设计与合成,按照工业生产需求快速创造全新的蛋白质催化剂,实现各种自然界生物无法催化的生物化学反应。尽管新基因设计技术展现了激动人心的应用前景,但是目前该技术还存在设计成功率不高、酶催化活性较低、合成成本较高等科技挑战。未来随着合成生物学技术的快速发展,设计、改造、合成和筛选等技术将融合为一体,为新基因设计与创建带来全新的发展机遇。     

Multistate approaches in computational protein design

Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single-state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a "multistate" approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories-those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.     

合成生物学基因设计软件:iGEM设计综述

随着基因回路规模的扩大,和应用范围的拓展,传统的合成基因回路的设计思路面临着新的挑战。新合成基因回路构建的试验周期长,试错成本大,单纯依靠经验进行设计构建,难以迅速得到满意的结果。iGEM中软件设计比赛旨在帮助合成生物学家,更高效地完成基因回路的设计与预测。为了更好地研究iGEM软件的设计与研究方向,寻找新的设计思路和理念,综述了最近几年iGEM软件队的项目,仔细总结了每一个项目的背景、目的,设计和应用。通过对比和总结,发现这几年的iGEM软件项目从功能上可以分为以下四类:①辅助设计;②资料共享;③合作交流;④数据分析。该综述可以为今后iGEM软件设计提供思考方向,也为合成生物学的发展提供新的思路。     

基因突变的教学设计

教学设计围绕基因突变这一核心概念,设计多样化的教学活动,让学生从实例分析入手,按照认知的规律从现象到概念,从宏观到微观来归纳总结出基因突变的概念;利用环环相扣的问题、科研资料引导学生在不断地探究、思考、分析和讨论,理解基因突变特征和基因突变的意义。