Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

92
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
92
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.4K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Modeling and Similitude01:12

Modeling and Similitude

333
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
333
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.4K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

pyVIPER: a fast and scalable Python package for protein activity estimation and master regulator analysis of single-cell RNA sequencing data.

BMC bioinformatics·2026
Same author

Modeling patient variants of <i>Cnot1</i> and <i>Cdc42bpb</i> results in distinct forms of congenital diaphragmatic hernia in mice.

bioRxiv : the preprint server for biology·2026
Same author

Non-concussive head impacts sustained during American football correlate with changes in gut microbiome diversity and composition.

PloS one·2026
Same author

Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant-effect predictors.

HGG advances·2026
Same author

Expanding the phenotypic spectrum of <i>MECOM</i>-associated syndrome: rare variants are associated with syndromic pulmonary arterial hypertension.

Journal of medical genetics·2026
Same author

Protein language models trained on biophysical dynamics inform mutation effects.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Sep 12, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K

在蛋白质适应性预测上理解语言模型缩放.

Chao Hou1, Di Liu2, Aziz Zafar2

  • 1Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032.

bioRxiv : the preprint server for biology
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

蛋白质语言模型在健身预测中的表现随着尺寸的增加而下降. 最佳性能需要适度的序列概率,而不是极端值,挑战深度学习中的"越大越好"假设.

关键词:
突变效应是一种突变效应.蛋白质健身景观 蛋白质健身景观自主监督的深度培训序列概率可能性.

更多相关视频

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.9K
A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

11.0K

相关实验视频

Last Updated: Sep 12, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.9K
A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

11.0K

科学领域:

  • 计算生物学 计算生物学
  • 机器学习 机器学习
  • 蛋白质工程是指蛋白质工程.

背景情况:

  • 蛋白质语言模型估计序列概率 (p(序列)) 用于突变效应预测和蛋白质设计.
  • 较大的深度学习模型通常被认为在各种任务中表现更好.

研究的目的:

  • 为了研究蛋白质语言模型的可扩展性,以预测健康状况.
  • 了解模型大小,训练数据和随机性如何影响预测的p 序列及其与实际蛋白质适应性的关系.

主要方法:

  • 分析蛋白质语言模型在不同模型大小和训练数据集的健身预测上的表现.
  • 评估预测的p ((序列) 与同源序列中的进化模式的相关性.

主要成果:

  • 在健身预测中的模型性能下降超过一定的尺寸,与一般的深度学习趋势相反.
  • 模型大小,训练数据和随机元素可以使预测的p (序列) 偏离实际的蛋白质适应性.
  • 最佳的适应性预测发生在p(序列) 与中等水平的进化模式相匹配时;极端的可能性导致统一的预测,未能捕捉到适应性景观.

结论:

  • 较大的蛋白质模型倾向于预测更高的p 序列,可能超过最佳的中等范围并降低健身预测的准确性.
  • 结果澄清了蛋白质模型的扩展行为,以预测健康状况,并为模型开发和应用提供了实际指导方针.