Protein Similarity Networks Research Articles

LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.

Drug repurposing and prediction of multiple interaction types via graph embedding

BackgroundFinding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug–target links, as well as delineating the type of drug interaction, are important in drug repurposing studies.ResultsA computational drug repurposing approach was proposed to predict novel drug–target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug–drug and protein–protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers.ConclusionDT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug–target–disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.

LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.

On the origin of mitochondria: a multilayer network approach.

The endosymbiotic theory is widely accepted to explain the origin of mitochondria from a bacterial ancestor. While ample evidence supports the intimate connection of Alphaproteobacteria to the mitochondrial ancestor, pinpointing its closest relative within sampled Alphaproteobacteria is still an open evolutionary debate. Many different phylogenetic methods and approaches have been used to answer this challenging question, further compounded by the heterogeneity of sampled taxa, varying evolutionary rates of mitochondrial proteins, and the inherent biases in each method, all factors that can produce phylogenetic artifacts. By harnessing the simplicity and interpretability of protein similarity networks, herein we re-evaluated the origin of mitochondria within an enhanced multilayer framework, which is an extension and improvement of a previously developed method. We used a dataset of eight proteins found in mitochondria (N = 6 organisms) and bacteria (N = 80 organisms). The sequences were aligned and resulting identity matrices were combined to generate an eight-layer multiplex network. Each layer corresponded to a protein network, where nodes represented organisms and edges were placed following mutual sequence identity. The Multi-Newman-Girvan algorithm was applied to evaluate community structure, and bifurcation events linked to network partition allowed to trace patterns of divergence between studied taxa. In our network-based analysis, we first examined the topology of the 8-layer multiplex when mitochondrial sequences disconnected from the main alphaproteobacterial cluster. The resulting topology lent firm support toward an Alphaproteobacteria-sister placement for mitochondria, reinforcing the hypothesis that mitochondria diverged from the common ancestor of all Alphaproteobacteria. Additionally, we observed that the divergence of Rickettsiales was an early event in the evolutionary history of alphaproteobacterial clades. By leveraging complex networks methods to the challenging question of circumscribing mitochondrial origin, we suggest that the entire Alphaproteobacteria clade is the closest relative to mitochondria (Alphaproteobacterial-sister hypothesis), echoing recent findings based on different datasets and methodologies.

Novel drug-target interactions via link prediction and network embedding

BackgroundAs many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures.ResultsWe report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking.ConclusionsThe proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs.

Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding.

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins.

FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network.

As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.

Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

BackgroundAlthough targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease.ResultsIn this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms.ConclusionsThe heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.

Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

Scalable remote homology detection and fold recognition in massive protein networks.

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.

ProtDec-LTR3.0: Protein Remote Homology Detection by Incorporating Profile-Based Features Into Learning to Rank

Protein remote homology detection is one of the most challenging problems in the field of protein sequence analysis, which is an important step for both theoretical research (such as the understanding of structures and functions of proteins) and drug design. Previous studies have shown that combining different ranking methods via learning to the rank algorithm is an effective strategy for remote protein homology detection, and the performance can be further improved by the protein similarity networks. In this paper, we improved the ProtDec-LTR1.0 and ProtDec-LTR2.0 predictors by incorporating three profile-based features (Top-1-gram, Top-2-gram, and ACC) into the framework of learning to rank via feature mapping strategies. The predictive performance was further refined by the pagerank (PR) algorithm and hyperlink-induced topic search (HITS) algorithm. Finally, a predictor called ProtDec-LTR3.0 was proposed. Rigorous tests on two widely used benchmark datasets showed that the ProtDec-LTR3.0 predictor outperformed both ProtDec-LTR1.0 and ProtDec-LTR2.0, and other nine existing state-of-the-art predictors, indicating that the ProtDec-LTR3.0 is an efficient method for protein remote homology detection, and will become a useful tool for protein sequence analysis. A user-friendly web server of the ProtDec-LTR3.0 predictor was established for the convenience of users, which can be accessed at http://bliulab.net/ProtDec-LTR3.0/.

HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search.

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits, by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis.

Three-Finger Toxin Diversification in the Venoms of Cat-Eye Snakes (Colubridae: Boiga).

The Asian genus Boiga (Colubridae) is among the better studied non-front-fanged snake lineages, because their bites have minor, but noticeable, effects on humans. Furthermore, B. irregularis has gained worldwide notoriety for successfully invading Guam and other nearby islands with drastic impacts on the local bird populations. One of the factors thought to allow B. irregularis to become such a noxious pest is irditoxin, a dimeric neurotoxin composed of two three-finger toxins (3FTx) joined by a covalent bond between two newly evolved cysteines. Irditoxin is highly toxic to diapsid (birds and reptiles) prey, but roughly 1000 × less potent to synapsids (mammals). Venom plays an important role in the ecology of all species of Boiga, but it remains unknown if any species besides B. irregularis produce irditoxin-like dimeric toxins. In this study, we use transcriptomic analyses of venom glands from five species [B. cynodon, B. dendrophila dendrophila, B. d. gemmicincta, B. irregularis (Brisbane population), B. irregularis (Sulawesi population), B. nigriceps, B. trigonata] and proteomic analyses of B. d. dendrophila and a representative of the sister genus Toxicodryas blandingii to investigate the evolutionary history of 3FTx within Boiga and its close relative. We found that 92.5% of Boiga 3FTx belong to a single clade which we refer to as denmotoxin-like because of the close relation between these toxins and the monomeric denmotoxin according to phylogenetic, sequence clustering, and protein similarity network analyses. We show for the first time that species beyond B. irregularis secrete 3FTx with additional cysteines in the same position as both the A and B subunits of irditoxin. Transcripts with the characteristic mutations are found in B. d. dendrophila, B. d. gemmicincta, B. irregularis (Brisbane population), B. irregularis (Sulawesi population), and B. nigriceps. These results are confirmed by proteomic analyses that show direct evidence of dimerization within the venom of B. d. dendrophila, but not T. blandingii. Our results also suggest the possibility of novel dimeric toxins in other genera such as Telescopus and Trimorphodon. All together, this suggests that the origin of these peculiar 3FTx is far earlier than was appreciated and their evolutionary history has been complex.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks.

Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure. The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families. MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot. emoriyama2@unl.edu. Supplementary data are available at Bioinformatics online.

Constructing an integrated gene similarity network for the identification of disease genes

BackgroundDiscovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale.ResultsWe proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer’s disease and predict some novel disease genes that supported by literature.ConclusionsRWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/.

Drug–target interaction prediction by integrating multiview network data

Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results.In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.

Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

Improving Functional Annotation in the DRE-TIM Metallolyase Superfamily through Identification of Active Site Fingerprints.

Within the DRE-TIM metallolyase superfamily, members of the Claisen-like condensation (CC-like) subgroup catalyze C-C bond-forming reactions between various α-ketoacids and acetyl-coenzyme A. These reactions are important in the metabolic pathways of many bacterial pathogens and serve as engineering scaffolds for the production of long-chain alcohol biofuels. To improve functional annotation and identify sequences that might use novel substrates in the CC-like subgroup, a combination of structural modeling and multiple-sequence alignments identified active site residues on the third, fourth, and fifth β-strands of the TIM-barrel catalytic domain that are differentially conserved within the substrate-diverse enzyme families. Using α-isopropylmalate synthase and citramalate synthase from Methanococcus jannaschii (MjIPMS and MjCMS), site-directed mutagenesis was used to test the role of each identified position in substrate selectivity. Kinetic data suggest that residues at the β3-5 and β4-7 positions play a significant role in the selection of α-ketoisovalerate over pyruvate in MjIPMS. However, complementary substitutions in MjCMS fail to alter substrate specificity, suggesting residues in these positions do not contribute to substrate selectivity in this enzyme. Analysis of the kinetic data with respect to a protein similarity network for the CC-like subgroup suggests that evolutionarily distinct forms of IPMS utilize residues at the β3-5 and β4-7 positions to affect substrate selectivity while the different versions of CMS use unique architectures. Importantly, mapping the identities of residues at the β3-5 and β4-7 positions onto the protein similarity network allows for rapid annotation of probable IPMS enzymes as well as several outlier sequences that may represent novel functions in the subgroup.

Deciphering Primordial Cyanobacterial Genome Functions from Protein Network Analysis

The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1-3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6]. Relatively little is known, however, about the evolutionary history and gene content of primordial cyanobacteria [7, 8]. To address these issues, we used protein similarity networks [9], containing proteomes from 48 cyanobacteria as the test group, and reference proteomes from 84 microbes representing four distinct metabolic groups from most reducing to most oxidizing: methanogens, obligate anaerobes (nonmethanogenic), facultative aerobes, and obligate aerobes. These four metabolic groups represent extant bioinformatic proxies for ancient redox chemistries, extending from an anoxic origin through the GOE and ultimately to obligate aerobes [10-13]. Analysis of the network metric degree showed a strong relationship between cyanobacteria and obligate anaerobes, from which cyanobacteria presumably arose, for core functions that include translation, photosynthesis, energy conservation, and environmental interactions. These data were used to reconstruct primordial functions in cyanobacteria that included nine gene families involved in photosynthesis, hydrogenases, and proteins involved in defense from environmental stress. The presence of 60% of these genes in both reaction center I (RC-I) and RC-II-type bacteria may be explained by selective loss of either RC in the evolutionary history of some photosynthetic lineages. Finally, the network reveals that cyanobacteria occupy a unique position among prokaryotes as a hub between anaerobes and obligate aerobes.

Evolutionarily distinct versions of the multidomain enzyme α-isopropylmalate synthase share discrete mechanisms of V-type allosteric regulation.

Understanding the evolution of allostery in multidomain enzymes remains an important step in improving our ability to identify and exploit structure-function relationships in allosteric mechanisms. A recent protein similarity network for the DRE-TIM metallolyase superfamily indicated there are two evolutionarily distinct forms of the enzyme α-isopropylmalate synthase (IPMS) sharing approximately 20% sequence identity. IPMS from Mycobacterium tuberculosis has been extensively characterized with respect to catalysis and the mechanism of feedback regulation by l-leucine. Here, IPMS from Methanococcus jannaschii (MjIPMS) is used as a representative of the second form of the enzyme, and its catalytic and regulatory mechanism is compared with that of MtIPMS to identify any functional differences between the two forms. MjIPMS exhibits kinetic parameters similar to those of other reported IPMS enzymes and is partially inhibited by l-leucine in a V-type manner. Identical values of (D2O)kcat (3.1) were determined in the presence and absence of l-leucine, indicating the hydrolytic step is rate-determining in the absence of l-leucine and remains so in the inhibited form of the enzyme. This mechanism is identical to the mechanism identified for MtIPMS ((D2O)kcat = 3.3 ± 0.3 in the presence of l-leucine) despite product release being rate-determining in the uninhibited MtIPMS enzyme. The identification of identical regulatory mechanisms in enzymes with low sequence identity raises important evolutionary questions concerning the acquisition and divergence of multidomain allosteric enzymes and highlights the need for caution when comparing regulatory mechanisms for homologous enzymes.