Current opinion in structural biology最新文献

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Protein ligand structure prediction: From empirical to deep learning approaches

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-05 DOI: 10.1016/j.sbi.2025.102998

Guangfeng Zhou, Frank DiMaio

Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening and lead optimization. Traditional empirical approaches use explicit scoring functions and conformational search techniques to predict protein-ligand structures and binding affinities. With the recent advent of deep learning (DL) methods, DL-based models learn both the scoring function and conformational sampling by approximating the underlying data distribution from training data. In this review, we first discuss the key components of both empirical and DL-based structure prediction methods to provide a unified view. We categorize these computational methods into two main groups based on whether a template protein structure is required, and briefly overview the important methods in each category. Finally, we discuss the major challenges and opportunities, focusing on the future development of DL-based approaches.

引用次数: 0

Advances in structure-based allosteric drug design 基于结构的变构药物设计研究进展。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102974

Rui Li , Xinheng He , Chengwei Wu , Mingyu Li , Jian Zhang

The identification of allosteric binding sites forms a critical connection between structural and computational biology, substantially advancing the discovery of allosteric drugs. However, the prevailing strategies for allosteric drug development predominantly rely on high-throughput screening, which suffers from high failure rates due to a limited understanding of allosteric mechanisms. This review collects insights from case studies on allosteric mechanisms, protein structure databases and computation algorithm developments, aiming to enhance our comprehension of allostery and guide more effective allosteric drug development. A crucial element in this area is the integration of structural biology with computational biology, which is vital for translating three-dimensional structural datasets into available drug discovery knowledge. These datasets and AI algorithms underpin the establishment of the allosteric binding site identification leading to structure–activity relationships (SARs) and are fueling the development of computational algorithms tailored for allosteric proteins, thereby driving forward the field of allosteric drug discovery.

变构结合位点的鉴定在结构生物学和计算生物学之间形成了重要的联系，极大地促进了变构药物的发现。然而，抗变构药物开发的主流策略主要依赖于高通量筛选，由于对抗变构机制的了解有限，其失败率很高。本文从变构机制、蛋白质结构数据库和计算算法的发展等方面进行综述，旨在提高我们对变构的理解，并指导更有效的变构药物的开发。该领域的一个关键因素是结构生物学与计算生物学的整合，这对于将三维结构数据集转化为可用的药物发现知识至关重要。这些数据集和人工智能算法支持了变构结合位点识别的建立，从而建立了结构-活性关系（sar），并推动了为变构蛋白定制的计算算法的发展，从而推动了变构药物发现领域的发展。

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On the emergence of machine-learning methods in bottom-up coarse-graining 论自下而上粗粒度学习中机器学习方法的出现。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102972

Patrick G. Sahrmann, Gregory A. Voth

Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which ‘coarse-grain’ electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling.

机器学习方法作为一种可行的分子建模和分析方法，在计算化学界得到了极大的关注。最近在利用神经网络学习“粗粒”电子结构的原子力场方面的成功，激发了类似于化学和生物系统的热力学粗粒化的应用。在这篇综述中，我们讨论了当前使用机器学习方法来表示粗粒度力场的可行性和挑战，以及机器学习在粗粒度建模的各个方面的应用。

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Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction 具有替代折叠的蛋白质揭示了基于alphafold的蛋白质结构预测的盲点。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102973

Devlina Chakravarty , Myeongsang Lee , Lauren L. Porter

In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.

近年来，人工智能（AI）的进步已经改变了结构生物学，特别是蛋白质结构预测。虽然基于人工智能的方法，如AlphaFold (AF)，经常以高精度和置信度预测蛋白质的单一构象，但对替代折叠的预测通常是不准确的、低置信度的，或者根本无法预测。在这里，我们回顾了三个盲点，替代构象揭示了基于af的蛋白质结构预测。首先，假设构象与其训练集同源物不同的蛋白质可能被错误预测。其次，AF过度依赖其训练集来预测备选构象。第三，两两表示的退化可能导致与实验不一致的高置信度预测。这些弱点提示了更可靠地预测可选折叠的方法。

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Toward a comprehensive profiling of alternative splicing proteoform structures, interactions and functions 对选择性剪接的蛋白质形态结构，相互作用和功能的综合分析。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102979

Elodie Laine , Maria Inés Freiberger

The mRNA splicing machinery has been estimated to generate 100,000 known protein-coding transcripts for 20,000 human genes (Ensembl, Sept. 2024). However, this set is expanding with the massive and rapidly growing data coming from high-throughput technologies, particularly single-cell and long-read sequencing. Yet, the implications of splicing complexity at the protein level remain largely uncharted. In this review, we describe the current advances toward systematically assessing the contribution of alternative splicing to proteome function diversification. We discuss the potential and challenges of using artificial intelligence-based techniques in identifying alternative splicing proteoforms and characterising their structures, interactions, and functions.

据估计，mRNA剪接机制为2万个人类基因产生了10万个已知的蛋白质编码转录本（Ensembl, september 2024）。然而，随着来自高通量技术，特别是单细胞和长读测序的大量和快速增长的数据，这一集正在扩大。然而，在蛋白质水平上剪接复杂性的含义在很大程度上仍然是未知的。在这篇综述中，我们描述了目前在系统评估选择性剪接对蛋白质组功能多样化的贡献方面的进展。我们讨论了使用基于人工智能的技术在识别备选剪接蛋白形态和表征其结构、相互作用和功能方面的潜力和挑战。

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Assembly and functional mechanisms of plant NLR resistosomes 植物NLR抗性小体的组装及其功能机制。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102977

Shijia Huang , Ertong Li , Fangshuai Jia , Zhifu Han , Jijie Chai

Nucleotide-binding and leucine-rich repeat (NLR) proteins are essential intracellular immune receptors in both animal and plant kingdoms. Sensing of pathogen-derived signals induces oligomerization of NLR proteins, culminating in the formation of higher-order protein complexes known as resistosomes in plants. The NLR resistosomes play a pivotal role in mediating the plant immune response against invading pathogens. Over the past few years, our understanding of NLR biology has significantly advanced, particularly in the structural and biochemical aspects of the NLR resistosomes. Here, we highlight the recent advancements in the structural knowledge of how NLR resistosomes are activated and assembled, and how the structural knowledge provides insights into the biochemical functions of these NLR resistosomes, which converge on Ca²⁺ signals. Signaling mechanisms of the resistosomes that underpin plant immunity are also briefly discussed.

核苷酸结合蛋白和富含亮氨酸重复序列（NLR）蛋白是动物和植物界必不可少的细胞内免疫受体。感应病原体来源的信号诱导NLR蛋白的寡聚化，最终在植物中形成称为抗性小体的高阶蛋白质复合物。NLR抵抗体在介导植物对入侵病原体的免疫应答中起着关键作用。在过去的几年里，我们对NLR生物学的理解有了显著的进步，特别是在NLR抵抗体的结构和生化方面。在这里，我们强调了NLR抵抗体如何被激活和组装的结构知识的最新进展，以及结构知识如何提供对这些NLR抵抗体的生化功能的见解，这些抵抗体聚集在Ca2+信号上。本文还简要讨论了支撑植物免疫的抗性小体的信号机制。

引用次数: 0

Deep learning methods for proteome-scale interaction prediction

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102981

Min Su Yoon , Byunghyun Bae , Kunhee Kim , Hahnbeom Park , Minkyung Baek

Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are often limited by scale and complexity, driving the need for computational approaches. Deep learning has emerged as a powerful tool, enabling high-throughput, accurate predictions of protein interactions. This review highlights recent advances in deep learning methods for protein–protein and protein-ligand interaction screening, along with datasets used for model training. Despite the progress with deep learning, challenges such as data quality and validation biases remain. We also discuss the increasing importance of integrating structural information to enhance prediction accuracy and how structure-based deep learning approaches can help overcome current limitations, ultimately advancing biological research and drug discovery.

引用次数: 0

Advancing protein structure prediction beyond AlphaFold2

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102985

Sanggeun Park , Sojung Myung , Minkyung Baek

Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins with limited evolutionary data or complex molecular interactions. This review explores efforts to enhance AlphaFold2’s performance through advanced sequence search techniques and alternative approaches, including protein language models and frameworks that integrate diverse biomolecular interactions. We propose that future progress will depend on developing models grounded in fundamental physicochemical principles, offering more accurate and comprehensive predictions across a wider spectrum of biological systems.

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Editorial overview: Protein networks in health and disease 编辑概述：健康和疾病中的蛋白质网络。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102953

Elizabeth A. Komives, Gabriela Chiosis

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Different applications and differentiated libraries for crystallographic fragment screening 晶体碎片筛选的不同应用和不同文库。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102982

Jessica Watt , Mathew P. Martin , Jane A. Endicott , Martin.E.M. Noble

Macromolecular X-ray crystallography allows detection and characterisation of the binding of small, low-affinity chemical fragments. Here we review the utility of fragment screening for drug discovery, its potential for use in discovery science, as well as some of the distinct types of fragments that have been compiled into libraries.

大分子x射线晶体学允许检测和表征小的，低亲和力的化学片段的结合。在这里，我们回顾了片段筛选在药物发现中的作用，它在发现科学中的潜在应用，以及一些已经编译成文库的不同类型的片段。

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