evo3D:用于结构信息化的进化分析的通用框架,在R中实现.
1Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
bioRxiv : the preprint server for biology
|December 22, 2025
概括
通过考虑蛋白质的3D结构来增强进化分析. evo3D R包为结构知情进化分析提供了灵活的框架,揭示了线性方法遗漏的保存空间社区.
科学领域:
- 分子进化分子进化
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 传统的进化分析侧重于线性蛋白序列,往往缺乏基于关键结构的选择压力.
- 以前的结构信息化方法使用固定的3D窗口,限制了分析灵活性和范围.
- 需要一个通用的框架来将蛋白质结构纳入进化研究.
研究的目的:
- 介绍evo3D,一个通用的R包,用于结构知情的进化分析.
- 为了能够灵活地分析3D蛋白质结构中的进化模式.
- 通过支持各种统计和结构复杂性的方法来克服以前方法的局限性.
主要方法:
- 开发了evo3D R包,这是一个结构知情进化分析的通用框架.
- 实现了结构信息多重序列对齐子集 (空间单元型) 的提取.
- 提供灵活的空间窗口 (固定的数量,固定的距离) 和分析模式 (残留,密码),适用于单体,多元和接口.
主要成果:
- 证明了evo3D在识别C型肝炎病毒E1/E2复合体中保存的空间社区中的实用性,这在线性方法中错过了.
- 使用 Chikungunya 病毒 E1/E2 组件评估了 evo3D 的可扩展性.
- 证实了evo3D在3D结构上下文中简化进化模式评估的能力.
结论:
- evo3D为结构知情进化遗传分析提供了正式和可访问的框架.
- 该方案消除了技术障碍,使其在分子进化研究中得到更广泛的应用.
- evo3D 便于在3D结构环境中直接评估进化模式.
更多相关视频
09:23Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
Published on: August 16, 2017
8.5K
07:09A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
10.3K
相关概念视频
Evolutionary Relationships through Genome Comparisons
6.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.8K
Introduction to R
4.1K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.1K
Statistical Analysis: Overview
14.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis
225
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
225
Introduction To Survival Analysis
710
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
710
Biostatistics: Overview
687
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
687
