MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein–Protein Network Mapping

Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein–protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with > 30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/.     

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.     

Effect of Bioregulator Isatin on Protein–Protein Interactions Involving Isatin-Binding Proteins

Isatin (2,3-dioxoindol) is an endogenous low-molecular-weight nonpeptide compound with a wide spectrum of biological and pharmacological activities. It is assumed that isatin acts through isatin-binding proteins. To date, more than a hundred of these proteins are known. Having a different structure and cellular and subcellular localization, they belong to different functional groups. Using the surface plasmon resonance technology, we found earlier that isatin affected the profile of intracellular amyloid-binding proteins and changed the stability of protein complexes in the model system. In fact, this indicates the selective effect of isatin on certain protein–protein interactions (PPI) that occur primarily with the participation of isatinbinding proteins. Therefore, we had formulated the hypothesis that isatin could be a regulator of a protein interactome. This study focuses on the verification of this assumption. Size-exclusion chromatography (SEC) profile of the rat liver tissue lysate along with mass-spectrometric protein identification has revealed 20 isatinbinding proteins that participate in the formation of the protein interactome. About 65 and 25% of them are involved in the formation of multimeric protein complexes and homo/heterodimers, respectively, and only 10% are detected as single molecules. The addition of isatin had a multidirectional effect on the profile of about half of the identified isatin-binding proteins. In some cases, the formation of protein complexes was induced, while in other cases the protein complexes were dissociated. This result confirms the hypothesis of the regulatory effect of isatin on certain PPIs. The data of this work in combination with our previous results allowed us to formulate an “interactomics image” of isatin as a bioregulator, which selectively controls both the formation and dissociation of a number of protein complexes. Two new isatin-dependent proteins were found in the work. This indicates that not all potential target proteins of the regulatory effect of isatin had been previously detected. The study of the molecular mechanisms of isatin action on PPI remains a difficult but priority task for future research.     

The Effects of Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) on Protein–Protein Interactions

Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base changes leading to a change to the amino acid sequence of the encoded protein. Many of these variants are associated with disease, so nsSNPs have been well studied, with studies looking at the effects of nsSNPs on individual proteins, for example, on stability and enzyme active sites. In recent years, the impact of nsSNPs upon protein–protein interactions has also been investigated, giving a greater insight into the mechanisms by which nsSNPs can lead to disease.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Dbf4 and Cdc7 Proteins Promote DNA Replication through Interactions with Distinct Mcm2–7 Protein Subunits

The essential cell cycle target of the Dbf4/Cdc7 kinase (DDK) is the Mcm2–7 helicase complex. Although Mcm4 has been identified as the critical DDK phosphorylation target for DNA replication, it is not well understood which of the six Mcm2–7 subunits actually mediate(s) docking of this kinase complex. We systematically examined the interaction between each Mcm2–7 subunit with Dbf4 and Cdc7 through two-hybrid and co-immunoprecipitation analyses. Strikingly different binding patterns were observed, as Dbf4 interacted most strongly with Mcm2, whereas Cdc7 displayed association with both Mcm4 and Mcm5. We identified an N-terminal Mcm2 region required for interaction with Dbf4. Cells expressing either an Mcm2 mutant lacking this docking domain (Mcm2ΔDDD) or an Mcm4 mutant lacking a previously identified DDK docking domain (Mcm4ΔDDD) displayed modest DNA replication and growth defects. In contrast, combining these two mutations resulted in synthetic lethality, suggesting that Mcm2 and Mcm4 play overlapping roles in the association of DDK with MCM rings at replication origins. Consistent with this model, growth inhibition could be induced in Mcm4ΔDDD cells through Mcm2 overexpression as a means of titrating the Dbf4-MCM ring interaction. This growth inhibition was exacerbated by exposing the cells to either hydroxyurea or methyl methanesulfonate, lending support for a DDK role in stabilizing or restarting replication forks under S phase checkpoint conditions. Finally, constitutive overexpression of each individual MCM subunit was examined, and genotoxic sensitivity was found to be specific to Mcm2 or Mcm4 overexpression, further pointing to the importance of the DDK-MCM ring interaction.     

Uncovering New Pathogen–Host Protein–Protein Interactions by Pairwise Structure Similarity

Pathogens usually evade and manipulate host-immune pathways through pathogen–host protein–protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen–host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen–host PPIs. First, a high-quality and non-redundant structure–structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue–residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen–host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen–host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen–host PPIs in the future.     

Functional Clustering of Immunoglobulin Superfamily Proteins with Protein–Protein Interaction Information Calibrated Hidden Markov Model Sequence Profiles

Secreted and cell-surface-localized members of the immunoglobulin superfamily (IgSF) play central roles in regulating adaptive and innate immune responses and are prime targets for the development of protein-based therapeutics. An essential activity of the ectodomains of these proteins is the specific recognition of cognate ligands, which are often other members of the IgSF. In this work, we provide functional insight for this important class of proteins through the development of a clustering algorithm that groups together extracellular domains of the IgSF with similar binding preferences. Information from hidden Markov model-based sequence profiles and domain architecture is calibrated against manually curated protein interaction data to define functional families of IgSF proteins. The method is able to assign 82% of the 477 extracellular IgSF protein to a functional family, while the rest are either single proteins with unique function or proteins that could not be assigned with the current technology. The functional clustering of IgSF proteins generates hypotheses regarding the identification of new cognate receptor–ligand pairs and reduces the pool of possible interacting partners to a manageable level for experimental validation.     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

Engineering Strategy and Vector Library for the Rapid Generation of Modular Light-Controlled Protein–Protein Interactions

Optogenetics enables the spatio-temporally precise control of cell and animal behavior. Many optogenetic tools are driven by light-controlled protein–protein interactions (PPIs) that are repurposed from natural light-sensitive domains (LSDs). Applying light-controlled PPIs to new target proteins is challenging because it is difficult to predict which of the many available LSDs, if any, will yield robust light regulation. As a consequence, fusion protein libraries need to be prepared and tested, but methods and platforms to facilitate this process are currently not available. Here, we developed a genetic engineering strategy and vector library for the rapid generation of light-controlled PPIs. The strategy permits fusing a target protein to multiple LSDs efficiently and in two orientations. The public and expandable library contains 29 vectors with blue, green or red light-responsive LSDs, many of which have been previously applied ex vivo and in vivo. We demonstrate the versatility of the approach and the necessity for sampling LSDs by generating light-activated caspase-9 (casp9) enzymes. Collectively, this work provides a new resource for optical regulation of a broad range of target proteins in cell and developmental biology.     

Refinement of a Quantitative Structure–Activity Relationship Model for Prediction of Cell-Penetrating Peptide Based Transfection Systems

Cell-penetrating peptide (CPP) based transfection systems (PBTS) are a promising class of drug delivery vectors. CPPs are short mainly cationic peptides capable of delivering cell non-permeant cargo to the interior of the cell. Some CPPs have the ability to form non-covalent complexes with oligonucleotides for gene therapy applications. In this study, we use quantitative structure–activity relationships (QSAR) , a statistical method based on regression data analysis. Here, an fragment QSAR (FQSAR) model is developed to predict new peptides based on standard alpha helical conformers and Assisted Model Building with Energy Refinement molecular mechanics simulations of previous peptides. These new peptides were examined for plasmid transfection efficiency and compared with their predicted biological activity. The best predicted peptides were capable of achieving plasmid transfection with significant improvement compared to the previous generation of peptides. Our results demonstrate that FQSAR model refinement is an efficient method for optimizing PBTS for improved biological activity.     

CASP11 – An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline

In silico prediction of a protein’s tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three ‘assisted’ protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.     

7 The Molecular Biology of Tryptophan Synthase: A Model for Protein–Protein Interaction

Abstract

The use of plastic produced from non-renewable resources constitutes a major environmental problem of the modern society. Polylactide polymers (PLA) have recently gained enormous attention as one possible substitution of petroleum derived polymers. A prerequisite for high quality PLA production is the provision of optically pure lactic acid, which cannot be obtained by chemical synthesis in an economical way. Microbial fermentation is therefore the commercial option to obtain lactic acid as monomer for PLA production. However, one major economic hurdle for commercial lactic acid production as basis for PLA is the costly separation procedure, which is needed to recover and purify the product from the fermentation broth. Yeasts, such as Saccharomyces cerevisiae (bakers yeast) offer themselves as production organisms because they can tolerate low pH and grow on mineral media what eases the purification of the acid. However, naturally yeasts do not produce lactic acid. By metabolic engineering, ethanol was exchanged with lactic acid as end product of fermentation. A vast amount of effort has been invested into the development of yeasts for lactic acid production since the first paper on this topic by Dequin and process insight. If pH stress is used as basis for DNA microarray analyses, in order to improve the host, what exactly is addressed? Growth? Or productivity? They might be connected, but can be negatively correlated. A better growing strain might not be a better producer. So if the question was growth, the answer might not be what was initially intended (productivity).

A major task for the future is to learn to ask the right questions – a lot of studies intended to lead to better productivity, did lead to interesting results, but NOT to better production strains.

Taking together what we learned from lactic acid production with yeasts, we see a bright future for bulk and fine chemical production with these versatile hosts.     

GalaxyDomDock: An Ab Initio Domain–domain Docking Web Server for Multi-domain Protein Structure Prediction

A significant proportion of proteins comprise multiple domains. Domain–domain docking is a tool that predicts multi-domain protein structures when individual domain structures can be accurately predicted but when domain orientations cannot be predicted accurately. GalaxyDomDock predicts an ensemble of domain orientations from given domain structures by docking. Such information would also be beneficial in elucidating the functions of proteins that have multiple states with different domain orientations. GalaxyDomDock is an ab initio domain–domain docking method based on GalaxyTongDock, a previously developed protein–protein docking method. Infeasible domain orientations for the given linker are effectively screened out from the docked conformations by a geometric filter, using the Dijkstra algorithm. In addition, domain linker conformations are predicted by adopting a loop sampling method FALC. The proposed GalaxyDomDock outperformed existing ab initio domain–domain docking methods, such as AIDA and Rosetta, in performance tests on the Rosetta benchmark set of two-domain proteins. GalaxyDomDock also performed better than or comparable to AIDA on the AIDA benchmark set of two-domain proteins and two-domain proteins containing discontinuous domains, including the benchmark set in which each domain of the set was modeled by the recent version of AlphaFold. The GalaxyDomDock web server is freely available as a part of GalaxyWEB at http://galaxy.seoklab.org/domdock.     

Energy Minimization for Tertiary Structure Prediction of Homologous Proteins: α1-purothionin and Viscotoxin A3 Models from Crambin

Abstract

Homologous proteins may fold into similar three-dimensional structures. Spectroscopic evidence suggests this is true for the cereal grain thionins, the mistletoe toxins, and for crambin, three classes of plant proteins. We have combined primary sequence homology and energy minimization to predict the structures α₁-purothionin (from Durum wheat) and viscotoxin A3 (from Viscum album, European mistletoe) from the high resolution (0.945 Å) crystal structure of crambin (from Crambe abyssinica). Our predictions will be verifiable because we have diffraction-quality crystals of α₁-purothionin whose structure we are have predicted. The potential energy minimizations for each protein were performed both with and without harmonic constraints to its initial backbone to explore the existence of local minima for the predicted proteins. Crambin was run as a control to examine the effects of the potential energy minimization on a protein with a well-known structure. Only α₁-purothionin which has one fewer residue in a turn region shows a significant difference for the two minimization paths. The results of these predictions suggest that α₁-purothionin and viscotoxin are amphipathic proteins, and this character may relate to the mechanism of action for these proteins. Both are mildly membrane-active and their amphipathic character is well suited for interaction with a lipid bilayer.