When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data -- from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels and at each level, we offer a set of selection and filtering operations enabling the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.
Most of the currently available computational tools for protein-protein interactions are focusing on protein pairs and a comprehensible overview was published by Huang @cite_4 . Some of the existing approaches, such as ArDock @cite_29 , already combine the computational method with a basic visual representation of the predictions. There are even some solutions, such as DockingShop @cite_28 , which are enabling the user to interactively design an initial configuration for a protein docking prediction process through a molecular graphics interface.
@cite_28 @cite_29 @cite_4
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The molecular docking problem is to determine how molecules interact with other molecules and plays a key role in understanding how cells function. DockingShop is an integrated environment far interactively steering molecular docking by navigating a ligand or protein to the receptor's estimated binding site. This tool provides a graphical interface for molecular modeling featuring real-time visual guides, interactive manipulation, navigation, optimization, and dynamic visualization enabling users to apply their biological knowledge to steer the docking process. Protein–protein docking is attracting increasing attention in drug discovery research targeting protein–protein interactions, owing to its potential in predicting protein–protein interactions and identifying ‘hot spot’ residues at the protein–protein interface. Given the relative lack of information about binding sites and the fact that proteins are generally larger than ligand, the search algorithms and evaluation methods for protein–protein docking differ somewhat from those for protein–ligand docking and, hence, require different research strategies. Here, we review the basic concepts, principles and advances of current search strategies and evaluation methods for protein–protein docking. We also discuss the current challenges and limitations, as well as future directions, of established approaches. Background The development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step. Motivation: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interaction between two monomer proteins. The correct prediction of interface residue pairs from two monomer proteins is still an open question and has great significance for practical experimental applications in the life sciences. We hope to build a method for the prediction of interface residue pairs that is suitable for those applications. Results: Here, we developed a novel deep network architecture called the multi-layered Long-Short Term Memory networks (LSTMs) approach for the prediction of protein interface residue pairs. Firstly, we created three new descriptions and used other six worked characterizations to describe an amino acid, then we employed these features to discriminate between interface residue pairs and non-interface residue pairs. Secondly, we used two thresholds to select residue pairs that are more likely to be interface residue pairs. Furthermore, this step increases the proportion of interface residue pairs and reduces the influence of imbalanced data. Thirdly, we built deep network architectures based on Long-Short Term Memory networks algorithm to organize and refine the prediction of interface residue pairs by employing features mentioned above. We trained the deep networks on dimers in the unbound state in the international Protein-protein Docking Benchmark version 3.0. The updated data sets in the versions 4.0 and 5.0 were used as the validation set and test set respectively. For our best model, the accuracy rate was over 62 when we chose the top 0.2 pairs of every dimer in the test set as predictions, which will be very helpful for the understanding of protein-protein interaction mechanisms and for guidance in biological experiments. Many biological processes are governed by large assemblies of protein molecules. However, it is often very difficult to determine the three-dimensional structures of these assemblies using experimental biophysical techniques. Hence there is a need to develop computational approaches to fill this gap. This article presents an ant colony optimization approach to predict the structure of large multi-component protein complexes. Starting from pair-wise docking predictions, a multi-graph consisting of vertices representing the component proteins and edges representing candidate interactions is constructed. This allows the assembly problem to be expressed in terms of searching for a minimum weight spanning tree. However, because the problem remains highly combinatorial, the search space cannot be enumerated exhaustively and therefore heuristic optimisation techniques must be used. The utility of the ant colony based approach is demonstrated by re-assembling known protein complexes from the Protein Data Bank. The algorithm is able to identify near-native solutions for five of the six cases tested. This demonstrates that the ant colony approach provides a useful way to deal with the highly combinatorial multi-component protein assembly problem. The tertiary structures of protein complexes provide a crucial insight about the molecular mechanisms that regulate their functions and assembly. However, solving protein complex structures by experimental methods is often more difficult than single protein structures. Here, we have developed a novel computational multiple protein docking algorithm, Multi-LZerD, that builds models of multimeric complexes by effectively reusing pairwise docking predictions of component proteins. A genetic algorithm is applied to explore the conformational space followed by a structure refinement procedure. Benchmark on eleven hetero-multimeric complexes resulted in near native conformations for all but one of them (a root mean square deviation smaller than 2.5A). We also show that our method copes with unbound docking cases well, outperforming the methodology that can be directly compared to our approach. Multi-LZerD was able to predict near native structures for multimeric complexes of various topologies. Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical functional interaction network for Halobacterium, and an interface to detailed stochastic kinetic gene regulatory models. Motivation: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. Results: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. Availability: Softwares are available upon request. Proteins play a significant role in virtually all biological processes, with motions of interest occurring on the timescale of pico- to nano-seconds. Existing experimental techniques are not able to reliably provide the level of detail required for studying the exact mechanisms of the molecular motion and the underlying structure-function relationship, essential for effective drug screening and design. Theoretical models and computational tools are instrumental for gaining better mechanistic understanding and predictive power. We focus on “hinge” proteins, which exhibit a rotational movement of one region of the protein relative to another; this motion is similar to that allowed by revolute joints. By applying rigidity theoretic techniques that analyze the protein's geometric structure, we predict a relative axis of motion between a given pair of domains. To the best of our knowledge, this is the first computational method to predict such an axis based on a single conformational state. The most closely related approaches for predicting hinges either use a single conformation to identify the residues permitting the motion (Stonehinge, HingeProt), or require two conformations as input (DynDom). Our results show that rigidity theory can be applied to analyze proteins and accurately predict information that may elucidate conformational changes tied to protein function. Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein–ligand binding and structural data enables the use of deep machine learning techniques for protein–ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein–ligand interaction. A CNN scoring function automatically learns the key features of protein–ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening. Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about proteinprotein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems. The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases. Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential. Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli. Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction. Analyzing the structure and function of proteins is a key part of understanding biology at the molecular and cellular level. In addition, a major engineering challenge is to design new proteins in a principled and methodical way. Current computational modeling methods for protein design are slow and often require human oversight and intervention. Here, we apply Generative Adversarial Networks (GANs) to the task of generating protein structures, toward application in fast de novo protein design. We encode protein structures in terms of pairwise distances between alpha-carbons on the protein backbone, which eliminates the need for the generative model to learn translational and rotational symmetries. We then introduce a convex formulation of corruption-robust 3D structure recovery to fold the protein structures from generated pairwise distance maps, and solve these problems using the Alternating Direction Method of Multipliers. We test the effectiveness of our models by predicting completions of corrupted protein structures and show that the method is capable of quickly producing biochemically viable solutions. This paper presents an efficient and novel computational protein prediction methodology called kineto-static compliance method. Successive kineto-static fold compliance is a methodology for predicting a protein molecule's motion under the effect of an interatomic force field without the need for molecular-dynamic simulation. Instead, the chain complies under the kineto-static effect of the force field in such a manner that each rotatable joint changes by an amount proportional to the effective torque on that joint. This process successively iterates until all of the joint torques have converged to a minimum. This configuration is equivalent to a stable, globally optimized potential energy state of the system or, in other words, the final conformation of the protein. This methodology is implemented in a computer software package named PROTOFOLD. In this paper, we have used PROTOFOLD to predict the final conformation of a small peptide chain segment, an alpha helix, and the Triponin protein chains from a denatured configuration. The results show that torques in each joint are minimized to values very close to zero, which demonstrates the method's effectiveness for protein conformation prediction. Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction prediction. For improving the prediction performance, in this paper we propose a novel set of features to represent a protein pair using their annotated Gene Ontology (GO) terms, including their ancestors. In our approach, a protein pair is treated as a document (bag of words), where the terms annotating the two proteins represent the words. Feature value of each word is calculated using information content of the corresponding term multiplied by a coefficient, which represents the weight of that term inside a document (i.e., a protein pair). We have tested the performance of the classifier using the proposed feature on different well known data sets of different species like S. cerevisiae, H. Sapiens, E. Coli, and D. melanogaster . We compare it with the other GO based feature representation technique, and demonstrate its competitive performance. Background Molecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins. A realistic computer simulation of protein folding requires a comprehensive account of interaction energetics, placing a substantial demand on processing power. This paper presents an improved computational framework for protein folding software package PROTOFOLD, that enables efficient computation of solvation free energy effects in addition to Coulombic and van der Waals interactions. Efficient data structures have been utilized to speed-up the sequential running times from O(n2) to O(n), n being the number of atoms. It turns out, however, that an accurate evaluation of molecular surface areas characterizing the solvation effects imposes a computational bottleneck to the entire simulation. Massive computational power offered by Graphics Processing Units (GPU) was exploited to develop a simple and efficient Single-Instruction Multiple-Thread (SIMT) algorithm for the latter step. The running times were monitored for different steps of the folding simulation for different molecular sizes. Significant performance improvements were observed, yielding promising results where numerous iterative runs are needed.© 2013 ASME