De novo inference of protein function from coarse-grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 mu s coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. (C) 2014 Wiley Periodicals, Inc.

Confirmation of Data Mining Based Predictions of Protein Function

King, R. D. and Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7), 1110-1118

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Contribution of redox status to hepatitis C virus E2 envelope protein function and antigenicity

Disulfide bonding contributes to the function and antigenicity of many viral envelope glycoproteins. We assessed here its significance for the hepatitis C virus E2 envelope protein and a counterpart deleted for hypervariable region-1 (HVR1). All 18 cysteine residues of the antigens were involved in disulfides. Chemical reduction of up to half of these disulfides was compatible with anti-E2 monoclonal antibody reaction, CD81 receptor binding, and viral entry, whereas complete reduction abrogated these properties. The addition of 5,5'-dithiobis-2-nitrobenzoic acid had no effect on viral entry. Thus, E2 function is only weakly dependent on its redox status, and cell entry does not require redox catalysts, in contrast to a number of enveloped viruses. Because E2 is a major neutralizing antibody target, we examined the effect of disulfide bonding on E2 antigenicity. We show that reduction of three disulfides, as well as deletion of HVR1, improved antibody binding for half of the patient sera tested, whereas it had no effect on the remainder. Small scale immunization of mice with reduced E2 antigens greatly improved serum reactivity with reduced forms of E2 when compared with immunization using native E2, whereas deletion of HVR1 only marginally affected the ability of the serum to bind the redox intermediates. Immunization with reduced E2 also showed an improved neutralizing antibody response, suggesting that potential epitopes are masked on the disulfide-bonded antigen and that mild reduction may increase the breadth of the antibody response. Although E2 function is surprisingly independent of its redox status, its disulfide bonds mask antigenic domains. E2 redox manipulation may contribute to improved vaccine design.

Computational chemistry for elucidating protein function: energetics and dynamics of myoglobin-ligand systems

State-of-the-art computational methodologies are used to investigate the energetics and dynamics of photodissociated CO and NO in myoglobin (Mb···CO and Mb···NO). This includes the combination of molecular dynamics, ab initio MD, free energy sampling, and effective dynamics methods to compare the results with studies using X-ray crystallography and ultrafast spectroscopy metho ds. It is shown that modern simulation techniques along with careful description of the intermolecular interactions can give quantitative agreement with experiments on complex molecular systems. Based on this agreement predictions for as yet uncharacterized species can be made.

CO migration in native and mutant myoglobin: atomistic simulations for the understanding of protein function

Molecular dynamics simulations of the events after the photodissociation of CO in the myoglobin mutant L29F in which leucine is replaced by phenylalanine are reported. Using both classical and mixed quantum-classical molecular dynamics calculations, we observed the rapid motion of CO away from the distal heme pocket to other regions of the protein, in agreement with recent experimental results. The experimentally observed and calculated infrared spectra of CO after dissociation are also in good agreement. We compared the results with data from simulations of WT myoglobin. As the time resolution of experimental techniques is increased, theoretical methods and models can be validated at the atomic scale by direct comparison with experiment.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

A critical role for the short intracellular C terminus in receptor activity-modifying protein function

Receptor activity-modifying proteins (RAMPs) interact with and modify the behavior of the calcitonin receptor (CTR) and calcitonin receptor-like receptor (CLR). We have examined the contribution of the short intracellular C terminus, using constructs that delete the last eight amino acids of each RAMP. C-Terminal deletion of individual RAMPs had little effect on the signaling profile induced when complexed with CLR in COS-7 or human embryonic kidney (HEK)293 cells. Likewise, confocal microscopy revealed each of the mutant RAMPs translocated hemagglutinin-tagged CLR to the cell surface. In contrast, a pronounced effect of RAMP C-terminal truncation was seen for RAMP/CTRa complexes, studied in COS-7 cells, with significant attenuation of amylin receptor phenotype induction that was stronger for RAMP1 and -2 than RAMP3. The loss of amylin binding upon C-terminal deletion could be partially recovered with overexpression of Gαs, suggesting an impact of the RAMP C terminus on coupling of G proteins to the receptor complex. In HEK293 cells the c-Myc-RAMP1 C-terminal deletion mutant showed high receptor-independent cell surface expression; however, this construct showed low cell surface expression when expressed alone in COS-7 cells, indicating interaction of RAMPs with other cellular components via the C terminus. This mutant also had reduced cell surface expression when coexpressed with CTR. Thus, this study reveals important functionality of the RAMP C-terminal domain and identifies key differences in the role of the RAMP C terminus for CTR versus CLR-based receptors.

An iterative approach of protein function prediction

Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.

Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.

Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value.

Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Progressive clustering based method for protein function prediction

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.

Predicting protein function using multiple kernels

High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple K ernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.

Studies of OPA1 pathogenic mechanisms in Dominant Optic Atrophy and novel protein function in mitochondrial DNA stability

The mitochondrion is an essential cytoplasmic organelle that provides most of the energy necessary for eukaryotic cell physiology. Mitochondrial structure and functions are maintained by proteins of both mitochondrial and nuclear origin. These organelles are organized in an extended network that dynamically fuses and divides. Mitochondrial morphology results from the equilibrium between fusion and fission processes, controlled by a family of “mitochondria-shaping” proteins. It is becoming clear that defects in mitochondrial dynamics can impair mitochondrial respiration, morphology and motility, leading to apoptotic cell death in vitro and more or less severe neurodegenerative disorders in vivo in humans. Mutations in OPA1, a nuclear encoded mitochondrial protein, cause autosomal Dominant Optic Atrophy (DOA), a heterogeneous blinding disease characterized by retinal ganglion cell degeneration leading to optic neuropathy (Delettre et al., 2000; Alexander et al., 2000). OPA1 is a mitochondrial dynamin-related guanosine triphosphatase (GTPase) protein involved in mitochondrial network dynamics, cytochrome c storage and apoptosis. This protein is anchored or associated on the inner mitochondrial membrane facing the intermembrane space. Eight OPA1 isoforms resulting from alternative splicing combinations of exon 4, 4b and 5b have been described (Delettre et al., 2001). These variants greatly vary among diverse organs and the presence of specific isoforms has been associated with various mitochondrial functions. The different spliced exons encode domains included in the amino-terminal region and contribute to determine OPA1 functions (Olichon et al., 2006). It has been shown that exon 4, that is conserved throughout evolution, confers functions to OPA1 involved in maintenance of the mitochondrial membrane potential and in the fusion of the network. Conversely, exon 4b and exon 5b, which are vertebrate specific, are involved in regulation of cytochrome c release from mitochondria, and activation of apoptosis, a process restricted to vertebrates (Olichon et al., 2007). While Mgm1p has been identified thanks to its role in mtDNA maintenance, it is only recently that OPA1 has been linked to mtDNA stability. Missense mutations in OPA1 cause accumulation of multiple deletions in skeletal muscle. The syndrome associated to these mutations (DOA-1 plus) is complex, consisting of a combination of dominant optic atrophy, progressive external ophtalmoplegia, peripheral neuropathy, ataxia and deafness (Amati- Bonneau et al., 2008; Hudson et al., 2008). OPA1 is the fifth gene associated with mtDNA “breakage syndrome” together with ANT1, PolG1-2 and TYMP (Spinazzola et al., 2009). In this thesis we show for the first time that specific OPA1 isoforms associated to exon 4b are important for mtDNA stability, by anchoring the nucleoids to the inner mitochondrial membrane. Our results clearly demonstrate that OPA1 isoforms including exon 4b are intimately associated to the maintenance of the mitochondrial genome, as their silencing leads to mtDNA depletion. The mechanism leading to mtDNA loss is associated with replication inhibition in cells where exon 4b containing isoforms were down-regulated. Furthermore silencing of exon 4b associated isoforms is responsible for alteration in mtDNA-nucleoids distribution in the mitochondrial network. In this study it was evidenced that OPA1 exon 4b isoform is cleaved to provide a 10kd peptide embedded in the inner membrane by a second transmembrane domain, that seems to be crucial for mitochondrial genome maintenance and does correspond to the second transmembrane domain of the yeasts orthologue encoded by MGM1 or Msp1, which is also mandatory for this process (Diot et al., 2009; Herlan et al., 2003). Furthermore in this thesis we show that the NT-OPA1-exon 4b peptide co-immuno-precipitates with mtDNA and specifically interacts with two major components of the mitochondrial nucleoids: the polymerase gamma and Tfam. Thus, from these experiments the conclusion is that NT-OPA1- exon 4b peptide contributes to the nucleoid anchoring in the inner mitochondrial membrane, a process that is required for the initiation of mtDNA replication and for the distribution of nucleoids along the network. These data provide new crucial insights in understanding the mechanism involved in maintenance of mtDNA integrity, because they clearly demonstrate that, besides genes implicated in mtDNA replications (i.e. polymerase gamma, Tfam, twinkle and genes involved in the nucleotide pool metabolism), OPA1 and mitochondrial membrane dynamics play also an important role. Noticeably, the effect on mtDNA is different depending on the specific OPA1 isoforms down-regulated, suggesting the involvement of two different combined mechanisms. Over two hundred OPA1 mutations, spread throughout the coding region of the gene, have been described to date, including substitutions, deletions or insertions. Some mutations are predicted to generate a truncated protein inducing haploinsufficiency, whereas the missense nucleotide substitutions result in aminoacidic changes which affect conserved positions of the OPA1 protein. So far, the functional consequences of OPA1 mutations in cells from DOA patients are poorly understood. Phosphorus MR spectroscopy in patients with the c.2708delTTAG deletion revealed a defect in oxidative phosphorylation in muscles (Lodi et al., 2004). An energetic impairment has been also show in fibroblasts with the severe OPA1 R445H mutation (Amati-Bonneau et al., 2005). It has been previously reported by our group that OPA1 mutations leading to haploinsufficiency are associated in fibroblasts to an oxidative phosphorylation dysfunction, mainly involving the respiratory complex I (Zanna et al., 2008). In this study we have evaluated the energetic efficiency of a panel of skin fibroblasts derived from DOA patients, five fibroblast cell lines with OPA1 mutations causing haploinsufficiency (DOA-H) and two cell lines bearing mis-sense aminoacidic substitutions (DOA-AA), and compared with control fibroblasts. Although both types of DOA fibroblasts maintained a similar ATP content when incubated in a glucose-free medium, i.e. when forced to utilize the oxidative phosphorylation only to produce ATP, the mitochondrial ATP synthesis through complex I, measured in digitonin-permeabilized cells, was significantly reduced in cells with OPA1 haploinsufficiency only, whereas it was similar to controls in cells with the missense substitutions. Furthermore, evaluation of the mitochondrial membrane potential (DYm) in the two fibroblast lines DOA-AA and in two DOA-H fibroblasts, namely those bearing the c.2819-2A>C mutation and the c.2708delTTAG microdeletion, revealed an anomalous depolarizing response to oligomycin in DOA-H cell lines only. This finding clearly supports the hypothesis that these mutations cause a significant alteration in the respiratory chain function, which can be unmasked only when the operation of the ATP synthase is prevented. Noticeably, oligomycin-induced depolarization in these cells was almost completely prevented by preincubation with cyclosporin A, a well known inhibitor of the permeability transition pore (PTP). This results is very important because it suggests for the first time that the voltage threshold for PTP opening is altered in DOA-H fibroblasts. Although this issue has not yet been addressed in the present study, several are the mechanisms that have been proposed to lead to PTP deregulation, including in particular increased reactive oxygen species production and alteration of Ca2+ homeostasis, whose role in DOA fibroblasts PTP opening is currently under investigation. Identification of the mechanisms leading to altered threshold for PTP regulation will help our understanding of the pathophysiology of DOA, but also provide a strategy for therapeutic intervention.