Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.     

评估蛋白质相互作用可信度的生物信息学方法

随着基因组规模的高通量实验鉴定技术和计算预测方法的发展,出现了大量蛋白质相互作用数据,但大规模蛋白质相互作用数据中的较高比例的假阳性影响了相互作用数据的质量。生物信息学方法能够从已有的数据和知识出发,通过计算方法系统评估大规模蛋白质相互作用的可信度。本文从过程模型设计、数据集构建、特征选择与综合属性抽取、一些算法使用、实例概述等方面介绍了生物信息学方法评估蛋白质相互作用可信度的研究特点与进展。     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.     

Protein complex prediction: A survey

Protein complexes are one of the most important functional units for deriving biological processes within the cell. Experimental methods have provided valuable data to infer protein complexes. However, these methods have inherent limitations. Considering these limitations, many computational methods have been proposed to predict protein complexes, in the last decade. Almost all of these in-silico methods predict protein complexes from the ever-increasing protein–protein interaction (PPI) data. These computational approaches usually use the PPI data in the format of a huge protein–protein interaction network (PPIN) as input and output various sub-networks of the given PPIN as the predicted protein complexes. Some of these methods have already reached a promising efficiency in protein complex detection. Nonetheless, there are challenges in prediction of other types of protein complexes, specially sparse and small ones. New methods should further incorporate the knowledge of biological properties of proteins to improve the performance. Additionally, there are several challenges that should be considered more effectively in designing the new complex prediction algorithms in the future. This article not only reviews the history of computational protein complex prediction but also provides new insight for improvement of new methodologies. In this article, most important computational methods for protein complex prediction are evaluated and compared. In addition, some of the challenges in the reconstruction of the protein complexes are discussed. Finally, various tools for protein complex prediction and PPIN analysis as well as the current high-throughput databases are reviewed.     

Knowledge-based Fragment Binding Prediction

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE''s ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.     

Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data

With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein – DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein – DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence-digitizing representation strategies used in different types of computational methods are also summarized and discussed.     

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

Background  

Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles.     

Function prediction of uncharacterized proteins

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.     

DeepCKI:一个基于变分图自编码器预测细胞-细胞因子相互作用的生物信息学模型

细胞因子(cytokine)是一类由免疫细胞和某些非免疫细胞合成和分泌的信号分子,在免疫系统中通过结合相应受体调节细胞生长、分化和调控免疫应答。目前研究多侧重于通过实验方法检测细胞因子和受体的相互作用来研究细胞间的通讯网络,但存在实验周期长、设备要求高和成本高等不足。因此,有必要通过计算方法来加快对细胞-细胞因子相互作用(cell-cytokine interactions, CKI)的系统研究。本文提出一种基于变分图自编码器(variational graph auto-encoder, VGAE)预测细胞-细胞因子相互作用的深度学习模型——DeepCKI。该模型可有效融合蛋白质相互作用网络和不同类型的蛋白质特征,充分挖掘网络拓扑结构和节点属性中的有效信息,实现对细胞-细胞因子相互作用的高效预测。与变分自编码和深度神经网络方法相比,采用图结构设计的DeepCKI表现出了最优的预测性能。DeepCKI模型对4种不同类型细胞-细胞因子相互作用的ROC曲线下面积均高于0.8,模型具有一定的鲁棒性和有效性。预测打分排名前100的细胞-细胞因子相互作用中,有36对已被最新发表文献验证,表明该模...     

Dynamic proteomics in modeling of the living cell. Protein-protein interactions

This review is devoted to describing, summarizing, and analyzing of dynamic proteomics data obtained over the last few years and concerning the role of protein-protein interactions in modeling of the living cell. Principles of modern high-throughput experimental methods for investigation of protein-protein interactions are described. Systems biology approaches based on integrative view on cellular processes are used to analyze organization of protein interaction networks. It is proposed that finding of some proteins in different protein complexes can be explained by their multi-modular and polyfunctional properties; the different protein modules can be located in the nodes of protein interaction networks. Mathematical and computational approaches to modeling of the living cell with emphasis on molecular dynamics simulation are provided. The role of the network analysis in fundamental medicine is also briefly reviewed.     

Structure-based computational analysis of protein binding sites for function and druggability prediction

Protein binding sites are the places where molecular interactions occur. Thus, the analysis of protein binding sites is of crucial importance to understand the biological processes proteins are involved in. Herein, we focus on the computational analysis of protein binding sites and present structure-based methods that enable function prediction for orphan proteins and prediction of target druggability. We present the general ideas behind these methods, with a special emphasis on the scopes and limitations of these methods and their validation. Additionally, we present some successful applications of computational binding site analysis to emphasize the practical importance of these methods for biotechnology/bioeconomy and drug discovery.     

Similarity of phylogenetic trees as indicator of protein-protein interaction.

Deciphering the network of protein interactions that underlines cellular operations has become one of the main tasks of proteomics and computational biology. Recently, a set of bioinformatics approaches has emerged for the prediction of possible interactions by combining sequence and genomic information. Even though the initial results are very promising, the current methods are still far from perfect. We propose here a new way of discovering possible protein-protein interactions based on the comparison of the evolutionary distances between the sequences of the associated protein families, an idea based on previous observations of correspondence between the phylogenetic trees of associated proteins in systems such as ligands and receptors. Here, we extend the approach to different test sets, including the statistical evaluation of their capacity to predict protein interactions. To demonstrate the possibilities of the system to perform large-scale predictions of interactions, we present the application to a collection of more than 67 000 pairs of E.coli proteins, of which 2742 are predicted to correspond to interacting proteins.     

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

Background  

Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.     

Deep learning for protein complex structure prediction

Recent developments in the structure prediction of protein complexes have resulted in accuracies rivalling experimental methods in many cases. The high accuracy is mainly observed in dimeric complexes and other problems such as protein disorder and predicting the structure of host-pathogen interactions remain. This review highlights the foundation for current accurate structure prediction of protein complexes and possible ways to address the remaining limitations.     

Computational methods in protein structure prediction

This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.