Large-scale prediction of protein-protein interactions from structures

Background  

The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins.     

How reliably can we predict the reliability of protein structure predictions?

Background  

Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.     

ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists

Background  

New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.     

AMYPdb: A database dedicated to amyloid precursor proteins

Background  

Misfolding and aggregation of proteins into ordered fibrillar structures is associated with a number of severe pathologies, including Alzheimer's disease, prion diseases, and type II diabetes. The rapid accumulation of knowledge about the sequences and structures of these proteins allows using of in silico methods to investigate the molecular mechanisms of their abnormal conformational changes and assembly. However, such an approach requires the collection of accurate data, which are inconveniently dispersed among several generalist databases.     

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Background  

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.     

Improving the accuracy of template-based predictions by mixing and matching between initial models

Background  

Comparative modeling is a technique to predict the three dimensional structure of a given protein sequence based primarily on its alignment to one or more proteins with experimentally determined structures. A major bottleneck of current comparative modeling methods is the lack of methods to accurately refine a starting initial model so that it approaches the resolution of the corresponding experimental structure. We investigate the effectiveness of a graph-theoretic clique finding approach to solve this problem.     

A data management system for structural genomics

Background  

Structural genomics (SG) projects aim to determine thousands of protein structures by the development of high-throughput techniques for all steps of the experimental structure determination pipeline. Crucial to the success of such endeavours is the careful tracking and archiving of experimental and external data on protein targets.     

Modeling of loops in proteins: a multi-method approach

Background  

Template-target sequence alignment and loop modeling are key components of protein comparative modeling. Short loops can be predicted with high accuracy using structural fragments from other, not necessairly homologous proteins, or by various minimization methods. For longer loops multiscale approaches employing coarse-grained de novo modeling techniques should be more effective.     

Scoring functions and enrichment: a case study on Hsp90

Background  

The need for fast and accurate scoring functions has been driven by the increased use of in silico virtual screening twinned with high-throughput screening as a method to rapidly identify potential candidates in the early stages of drug development. We examine the ability of some the most common scoring functions (GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus) to discriminate correctly and efficiently between active and non-active compounds among a library of ~3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90).     

Evaluation of an <Emphasis Type="Italic">in silico</Emphasis> predicted specific and immunogenic antigen from the OmcB protein for the serodiagnosis of <Emphasis Type="Italic">Chlamydia trachomatis</Emphasis> infections

Background  

The OmcB protein is one of the most immunogenic proteins in C. trachomatis and C. pneumoniae infections. This protein is highly conserved leading to serum cross reactivity between the various chlamydial species. Since previous studies based on recombinant proteins failed to identify a species specific immune response against the OmcB protein, this study evaluated an in silico predicted specific and immunogenic antigen from the OmcB protein for the serodiagnosis of C. trachomatis infections.     

Ab initio modeling of small proteins by iterative TASSER simulations

Background  

Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.     

Genome-scale metabolic reconstruction and <Emphasis Type="Italic">in silico</Emphasis> analysis of methylotrophic yeast <Emphasis Type="Italic">Pichia pastoris</Emphasis> for strain improvement

Background  

Pichia pastoris has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive in silico model of P. pastoris can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.     

Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field

Background  

Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.     

Interaction profile-based protein classification of death domain

Background

The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.

Results

We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.

Conclusion

We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

     

Functional Annotation,Genome Organization and Phylogeny of the Grapevine (<Emphasis Type="Italic">Vitis vinifera</Emphasis>) Terpene Synthase Gene Family Based on Genome Assembly,FLcDNA Cloning,and Enzyme Assays

Background  

Terpenoids are among the most important constituents of grape flavour and wine bouquet, and serve as useful metabolite markers in viticulture and enology. Based on the initial 8-fold sequencing of a nearly homozygous Pinot noir inbred line, 89 putative terpenoid synthase genes (VvTPS) were predicted by in silico analysis of the grapevine (Vitis vinifera) genome assembly [1]. The finding of this very large VvTPS family, combined with the importance of terpenoid metabolism for the organoleptic properties of grapevine berries and finished wines, prompted a detailed examination of this gene family at the genomic level as well as an investigation into VvTPS biochemical functions.     

Raw starch–degrading α‐amylase from Bacillus aquimaris MKSC 6.2: isolation and expression of the gene,bioinformatics and biochemical characterization of the recombinant enzyme

Aims

The aims were to isolate a raw starch–degrading α‐amylase gene baqA from Bacillus aquimaris MKSC 6.2, and to characterize the gene product through in silico study and its expression in Escherichia coli.

Methods and Results

A 1539 complete open reading frame of a starch–degrading α‐amylase gene baqA from B. aquimaris MKSC 6·2 has been determined by employing PCR and inverse PCR techniques. Bioinformatics analysis revealed that B. aquimaris MKSC 6.2 α‐amylase (BaqA) has no starch‐binding domain, and together with a few putative α‐amylases from bacilli may establish a novel GH13 subfamily most closely related to GH13_1. Two consecutive tryptophans (Trp201 and Trp202, BaqA numbering) were identified as a sequence fingerprint of this novel GH13 subfamily. Escherichia coli cells produced the recombinant BaqA protein as inclusion bodies. The refolded recombinant BaqA protein degraded raw cassava and corn starches, but exhibited no activity with soluble starch.

Conclusions

A novel raw starch–degrading B. aquimaris MKSC 6.2 α‐amylase BaqA is proposed to be a member of new GH13 subfamily.

Significance and Impact of the Study

This study has contributed to the overall knowledge and understanding of amylolytic enzymes that are able to bind and digest raw starch directly.     

Reconstruction of ancestral protein sequences and its applications

Background  

Modern-day proteins were selected during long evolutionary history as descendants of ancient life forms. In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference.     

Experimental annotation of the human pathogen <Emphasis Type="Italic">Candida albicans</Emphasis> coding and noncoding transcribed regions using high-resolution tiling arrays

Background  

Compared to other model organisms and despite the clinical relevance of the pathogenic yeast Candida albicans, no comprehensive analysis has been done to provide experimental support of its in silico-based genome annotation.     

NERVE: New Enhanced Reverse Vaccinology Environment

Background  

Since a milestone work on Neisseria meningitidis B, Reverse Vaccinology has strongly enhanced the identification of vaccine candidates by replacing several experimental tasks using in silico prediction steps. These steps have allowed scientists to face the selection of antigens from the predicted proteome of pathogens, for which cell culture is difficult or impossible, saving time and money. However, this good example of bioinformatics-driven immunology can be further developed by improving in silico steps and implementing biologist-friendly tools.     

SVRMHC prediction server for MHC-binding peptides

Background  

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.