Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein–protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure‐based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein–protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure‐based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure‐based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k_off, k_on, and K_d, respectively, in leave‐one‐out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k_on was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k_off was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design. Proteins 2010;79:720–734. © 2010 Wiley‐Liss, Inc.     

Revealing protein-protein interactions at the transcriptome scale by sequencing

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Efficient prediction of human protein-protein interactions at a global scale

Background

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.

Results

On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.

Conclusions

The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users.     

Predicting protein-protein interactions from protein domains using a set cover approach

One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, maximum specificity set cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the maximum likelihood estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi-cse.nd.edu     

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.     

Unraveling microalgal molecular interactions using evolutionary and structural bioinformatics

Microalgae are unicellular microorganisms indispensible for environmental stability and life on earth, because they produce approximately half of the atmospheric oxygen, with simultaneously feeding on the harmful greenhouse gas carbon dioxide. Using gene fusion analysis, a series of five fusion/fission events was identified, that provided the basis for critical insights to their evolutionary history. Moreover, the three-dimensional structures of both the fused and the component proteins were predicted, allowing us to envisage putative protein–protein interactions that are invaluable for the efficient usage, handling and exploitation of microalgae. Collectively, our proposed approach on the five fusion/fission alga protein events contributes towards the expansion of the microalgae knowledgebase, bridging protein evolution of the ancient microalgal species and the rapidly evolving, modern, bioinformatics field.     

Crystal structural analysis of protein-protein interactions drastically destabilized by a single mutation

The complex of barnase (bn) and barstar (bs), which has been widely studied as a model for quantitative analysis of protein-protein interactions, is significantly destabilized by a single mutation, namely, bs Asp39 --> Ala, which corresponds to a change of 7.7 kcal x mol(-1) in the free energy of binding. However, there has been no structural information available to explain such a drastic destabilization. In the present study, we determined the structure of the mutant complex at 1.58 A resolution by X-ray crystallography. The complex was similar to the wild-type complex in terms of overall and interface structures; however, the hydrogen bond network mediated by water molecules at the interface was significantly different. Several water molecules filled the cavity created by the mutation and consequently caused rearrangement of the hydrated water molecules at the interface. The water molecules were redistributed into a channel-like structure that penetrated into the complex. Furthermore, molecular dynamics simulations showed that the mutation increased the mobility of water molecules at the interface. Since such a drastic change in hydration was not observed in other mutant complexes of bn and bs, the significant destabilization of the interaction may be due to this channel-like structure of hydrated water molecules.     

Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces

MOTIVATION: Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. RESULTS: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. AVAILABILITY: http://gordion.hpc.eng.ku.edu.tr/prism CONTACT: agursoy@ku.edu.tr; okeskin@ku.edu.tr.     

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.     

Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis

Background  

Protein-protein interaction (PPI) data sets generated by high-throughput experiments are contaminated by large numbers of erroneous PPIs. Therefore, computational methods for PPI validation are necessary to improve the quality of such data sets. Against the background of the theory that most extant PPIs arose as a consequence of gene duplication, the sensitive search for homologous PPIs, i.e. for PPIs descending from a common ancestral PPI, should be a successful strategy for PPI validation.     

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background  

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.     

In planta protein-protein interactions assessed using a nanovirus-based replication and expression system

The multipartite genome of the nanovirus Faba bean necrotic yellows virus, which consists of one gene on each DNA component, was exploited to construct a series of virus-based episomal vectors designed for transient replication and gene expression in plants. This nanovirus based expression system yields high levels of protein which allows isolation of recombinant protein and protein complexes from plant tissues. As examples, we demonstrated in planta interaction between the nanovirus F-box protein Clink and SKP1, a constituent of the ubiquitin-dependent protein turnover pathway. Thus, replicative nanovirus vectors provide a simple and efficient means for in planta characterization of protein-protein interaction.     

Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2^nd-level, 3^rd-level, 4^th-level, and 5^th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.     

Kinetic and structural studies of specific protein-protein interactions in substrate catalysis by Cdc25B phosphatase

Using a combination of steady-state and single-turnover kinetics, we probe substrate association, dissociation, and chemistry for the reaction of Cdc25B phosphatase with its Cdk2-pTpY/CycA protein substrate. The rate constant for substrate association for the wild-type enzyme is 1.3 x 10(6) M(-1) s(-1). The rate constant for dissociation is slow compared to the rate constant for phosphate transfer to form the phospho-enzyme intermediate (k2 = 1.1 s(-1)), making Cdk2-pTpY/CycA a sticky substrate. Compared to the wild type, all hotspot mutants of residues at the remote docking site that specifically affect catalysis with the protein substrate (Arg488, Arg492, and Tyr497 on Cdc25B and Asp206 on Cdk2) have greatly slowed rate constants of association (70- to 4500-fold), and some mutants have decreased k2 values compared to that of the wild type. Most dramatically, R492L, despite showing no significant changes in a crystal structure at 2.0 A resolution, has an approximately 100-fold decrease in k2 compared to that of wild-type Cdc25B. The active site C473S mutant binds tightly to and dissociates slowly from Cdk2-pTpY/CycA (Kd = 10 nM, k(off) = 0.01 s(-1)). In contrast, the C473D mutant, despite showing only localized perturbations in the active site at 1.6 A resolution, has a much weaker affinity and dissociates rapidly (Kd of 2 microM, k(off) > 2 s(-1)) from the protein substrate. Overall, we demonstrate that the association of Cdc25B with its Cdk2-pTpY/CycA substrate is governed to a significant extent by the interactions of the remote hotspot residues, whereas dissociation is governed by interactions at the active site.     

Detection and localisation of protein-protein interactions in Saccharomyces cerevisiae using a split-GFP method

An alternative method for monitoring protein-protein interactions in Saccharomyces cerevisiae has been developed. It relies on the ability of two fragments of enhanced green fluorescent protein (EGFP) to reassemble and fluoresce when fused to interacting proteins. Since this fluorescence can be detected in living cells, simultaneous detection and localisation of interacting pairs is possible. DNA sequences encoding N- and C-terminal EGFP fragments flanked by sequences from the genes of interest were transformed into S. cerevisiae JPY5 cells and homologous recombination into the genome verified by PCR. The system was evaluated by testing known interacting proteins: labelling of the phosphofructokinase subunits, Pfk1p and Pfk2p, with N- and C-terminal EGFP fragments, respectively, resulted in green fluorescence in the cytoplasm. The system works in other cellular compartments: labelling of Idh1p and Idh2p (mitochondrial matrix), Sdh3p and Sdh4p (mitochondrial membrane) and Pap2p and Mtr4p (nucleus) all resulted in fluorescence in the appropriate cellular compartment.