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相关概念视频

Mechanical Protein Functions01:58

Mechanical Protein Functions

5.6K
Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
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Peptide Bonds02:43

Peptide Bonds

82.8K
A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
82.8K
Structural Protein Function01:56

Structural Protein Function

29.9K
Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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Structural Protein Function01:56

Structural Protein Function

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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Introduction to z Scores01:06

Introduction to z Scores

11.2K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
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相关实验视频

Updated: Jan 29, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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PepScorer::RMSD:一个改进的机器学习对蛋白质-接的评分功能.

Andrea Giuseppe Cavalli1, Giulio Vistoli1, Alessandro Pedretti1

  • 1Department of Pharmaceutical Sciences, University of Milan, I-20133 Milan, Italy.

International journal of molecular sciences
|January 28, 2026
PubMed
概括
此摘要是机器生成的。

一个新的机器学习工具PepScorer::RMSD通过准确预测结合姿势来改善类药物发现. 这提高了基于的治疗方法的虚拟查效率,为小分子提供了强大的替代方案.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.蛋白质接对接的蛋白质接.评分功能是一个得分函数.虚拟选 虚拟选 虚拟选

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 制药对小分子具有优势,但需要专门的计算工具.
  • 现有的分子对接方法在的灵活性和得分方面扎,限制了它们在药物发现中的有效性.
  • 目前的计算工具主要针对小分子进行了优化,因此需要针对基于的候选药物进行调整.

研究的目的:

  • 为了开发一种基于机器学习的新型得分函数,PepScorer::RMSD,用于在分子对接中准确的位预测.
  • 为了增强对接功率 (DP) 并提出选择能力,用于对库的虚拟选.
  • 解决当前评分函数在处理的形状灵活性方面的局限性.

主要方法:

  • 开发了PepScorer::RMSD,这是一个机器学习模型,可以预测位的根-平均-平方偏差 (RMSD).
  • 使用精选的蛋白质-复合体 (3-10氨基酸) 数据集进行模型训练和评估.
  • 对基于PLANTS的工作流进行了基准测试,将PepScorer::RMSD结合起来,与AlphaFold-Multimer预测进行比较.

主要成果:

  • PepScorer::RMSD实现了0.70的皮尔森相关性和1.77 Å的平均绝对误差.
  • 在评估组件上显示了92%的高顶-1对接功率 (DP),在外部测试组件上显示了81%.
  • 在准确性和效率方面表现优于传统的,基于ML的和现有的特异性评分功能.

结论:

  • PepScorer::RMSD显著提高了位预测和虚拟查的准确性.
  • 开发的工具和数据集为计算药物发现提供了强大的解决方案.
  • 自由可用的资源 (PepScorer::RMSD和数据集) 促进了基于的治疗方法的进一步研究.