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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Conserved Binding Sites

Conserved Binding Sites

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 analyses the...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Jun 9, 2026

A Polyaniline-based Sensor of Nucleic Acids

A Polyaniline-based Sensor of Nucleic Acids

Published on: November 1, 2016

潘瑟 - - 对于核标结合,混合化和能量回归的蛋白质亲和力

Parisa Aletayeb¹, Akash Deep Biswas², Stefano Rocca¹

¹Universita degli Studi di Milano.

RNA (New York, N.Y.)

|December 3, 2025

概括

此摘要是机器生成的。

我们开发了PANTHER评分,这是一个机器学习模型,用于预测蛋白质-RNA结合的自由能量 (ΔG). 这种方法克服了数据的局限性,为生物分子研究和药物发现提供了可靠的工具.

关键词:

有约束力的自由能源.药物发现药物发现机器学习模型机器学习模型蛋白质-RNA 相互作用在RNA治疗方面,RNA疗法.

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Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

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Published on: December 6, 2017

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

Published on: September 21, 2017

相关实验视频

Last Updated: Jun 9, 2026

A Polyaniline-based Sensor of Nucleic Acids

A Polyaniline-based Sensor of Nucleic Acids

Published on: November 1, 2016

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Published on: December 6, 2017

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

Published on: September 21, 2017

科学领域:

计算生物学计算生物学
生物物理学的生物物理.
机器学习机器学习

背景情况:

蛋白质-RNA相互作用对于细胞功能至关重要.
由于数据稀缺和相互作用的复杂性,准确预测结合自由能量 (ΔG) 是具有挑战性的.

研究的目的:

为了开发一种机器学习模型,PANTHER得分,用于预测蛋白质-RNA结合的自由能量.
为了解决蛋白质-RNA相互作用研究实验数据的局限性.

主要方法:

使用了局部到全球的方法,从分子动力学模拟中得出局部相互作用能量.
机器学习模型被训练来预测局部相互作用能量,并集成到PANTHER得分中.
该模型在测试和外部应激集上进行了评估,包括110个具有实验 ΔG 的复合体.

主要成果:

随机森林回归实现了最高的预测性能,在测试组中产生了0.80的皮尔森相关系数 (r) 和1.79kcal/mol的MAE.
该模型在应力组 (r=0.64,MAE=1.63 kcal/mol) 上表现出强大的预测能力.
在基准测试中,PANTHER评分的表现优于现有的工具.

结论:

PANTHER评分是预测蛋白质-RNA结合亲缘关系的有效工具.
机器学习可以克服预测复杂的生物分子相互作用的数据限制.
这种方法通过提供准确的结合能量预测,促进生物分子研究和药物发现.