蛋白质结构预测进展

蛋白质的序列决定结构,结构决定功能。新一代准确的蛋白质结构预测工具为结构生物学、结构生物信息学、药物研发和生命科学等许多领域带来了全新的机遇与挑战,单链蛋白质结构预测的准确率达到与试验方法相媲美的水平。本综述概述了蛋白质结构预测领域的理论基础、发展历程与最新进展,讨论了大量预测的蛋白质结构和基于人工智能的方法如何影响实验结构生物学,最后,分析了当前蛋白质结构预测领域仍未解决的问题以及未来的研究方向。     

Understanding and predicting protein assemblies with 3D structures

Protein interactions are central to most biological processes, and are currently the subject of great interest. Yet despite the many recently developed methods for interaction discovery, little attention has been paid to one of the best sources of data: complexes of known three-dimensional (3D) structure. Here we discuss how such complexes can be used to study and predict protein interactions and complexes, and to interrogate interaction networks proposed by methods such as two-hybrid screens or affinity purifications.     

Unraveling aquaporin interaction partners

Background

Insight into protein–protein interactions (PPIs) is highly desirable in order to understand the physiology of cellular events. This understanding is one of the challenges in biochemistry and molecular biology today, especially for eukaryotic membrane proteins where hurdles of production, purification and structural determination must be passed.

Scope of review

We have explored the common strategies used to find medically relevant interaction partners of aquaporins (AQPs). The most frequently used methods to detect direct contact, yeast two-hybrid interaction assay and co-precipitation, are described together with interactions specifically found for the selected targets AQP0, AQP2, AQP4 and AQP5.

Major conclusions

The vast majority of interactions involve the aquaporin C-terminus and the characteristics of the interaction partners are strikingly diverse. While the well-established methods for PPIs are robust, a novel approach like bimolecular fluorescence complementation (BiFC) is attractive for screening many conditions as well as transient interactions. The ultimate goal is structural evaluation of protein complexes in order to get mechanistic insight into how proteins communicate at a molecular level.

General significance

What we learn from the human aquaporin field in terms of method development and communication between proteins can be of major use for any integral membrane protein of eukaryotic origin. This article is part of a Special Issue entitled Aquaporins.     

RNA structure: Merging chemistry and genomics for a holistic perspective

The advent of deep sequencing technology has unexpectedly advanced our structural understanding of molecules composed of nucleic acids. A significant amount of progress has been made recently extrapolating the chemical methods to probe RNA structure into sequencing methods. Herein we review some of the canonical methods to analyze RNA structure, and then we outline how these have been used to probe the structure of many RNAs in parallel. The key is the transformation of structural biology problems into sequencing problems, whereby sequencing power can be interpreted to understand nucleic acid proximity, nucleic acid conformation, or nucleic acid‐protein interactions. Utilizing such technologies in this way has the promise to provide novel structural insights into the mechanisms that control normal cellular physiology and provide insight into how structure could be perturbed in disease.     

Structural genomics—Impact on biomedicine and drug discovery

The field of structural genomics emerged as one of many 'omics disciplines more than a decade ago, and a multitude of large scale initiatives have been launched across the world. Development and implementation of methods for high-throughput structural biology represents a common denominator among different structural genomics programs. From another perspective a distinction between “biology-driven” versus “structure-driven” approaches can be made. This review outlines the general themes of structural genomics, its achievements and its impact on biomedicine and drug discovery. The growing number of high resolution structures of known and potential drug target proteins is expected to have tremendous value for future drug discovery programs. Moreover, the availability of large numbers of purified proteins enables generation of tool reagents, such as chemical probes and antibodies, to further explore protein function in the cell.     

Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations

Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597–609 ). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.     

Interaction between hepatitis C virus proteins and host cell factors

Since the discovery of the hepatitis C virus (HCV) as the causative agent of non-A, non-B hepatitis, significant effort has been devoted to understanding this important pathogen. Despite the difficulty in culturing this virus efficiently, much is known about the organization of the viral genome and the functions of many of the viral proteins. Through the use of surrogate expression systems combined with cellular fractionation, pull-down experiments and yeast two-hybrid screens, numerous interactions between hepatitis C virus proteins and cellular components have been identified. The relevance of many of these interactions to hepatitis C biology remains to be demonstrated. This review discusses recent developments in this area of HCV research.     

Targeting of extracellular protein–protein interactions with macrocyclic peptides

Targeting of extracellular protein–protein interactions (PPI) is emerging as a major application for de novo discovered macrocyclic peptides. Modern discovery platforms can routinely identify macrocyclic peptide ligands capable of highly selective modulation of extracellular signaling pathways; amenability to chemical synthesis and natural modularity of peptides additionally provides an avenue for their further structural elaboration, while the challenge of cell internalization can be minimized. Here, we discuss the recent progress in targeting extracellular PPIs with macrocyclic peptides by focusing on a number of recent case studies. We analyze the scope and potential limitations of the discovery systems in identifying functional macrocyclic ligands. We also highlight the recent technical advancements allowing for a more streamlined discovery pipeline and our brief perspective in this field.     

On the Potential of Machine Learning to Examine the Relationship Between Sequence,Structure, Dynamics and Function of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs—and intrinsically disordered regions (IDRs) interspersed between folded domains—are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.     

Using relaxation dispersion NMR spectroscopy to determine structures of excited,invisible protein states

Currently the main focus of structural biology is the determination of static three-dimensional representations of biomolecules that for the most part correspond to low energy (ground state) conformations. However, it is becoming increasingly well recognized that higher energy structures often play important roles in function as well. Because these conformers are populated to only low levels and are often only transiently formed their study is not amenable to many of the tools of structural biology. In this perspective we discuss the role of CPMG-based relaxation dispersion NMR spectroscopy in characterizing these low populated, invisible states. It is shown that robust methods for measuring both backbone chemical shifts and residual anisotropic interactions in the excited state are in place and that these data provide valuable restraints for structural studies of invisible conformers.