Diversity of Monomers in Nonribosomal Peptides: towards the Prediction of Origin and Biological Activity

Nonribosomal peptides (NRPs) are molecules produced by microorganisms that have a broad spectrum of biological activities and pharmaceutical applications (e.g., antibiotic, immunomodulating, and antitumor activities). One particularity of the NRPs is the biodiversity of their monomers, extending far beyond the 20 proteogenic amino acid residues. Norine, a comprehensive database of NRPs, allowed us to review for the first time the main characteristics of the NRPs and especially their monomer biodiversity. Our analysis highlighted a significant similarity relationship between NRPs synthesized by bacteria and those isolated from metazoa, especially from sponges, supporting the hypothesis that some NRPs isolated from sponges are actually synthesized by symbiotic bacteria rather than by the sponges themselves. A comparison of peptide monomeric compositions as a function of biological activity showed that some monomers are specific to a class of activities. An analysis of the monomer compositions of peptide products predicted from genomic information (metagenomics and high-throughput genome sequencing) or of new peptides detected by mass spectrometry analysis applied to a culture supernatant can provide indications of the origin of a peptide and/or its biological activity.Nonribosomal peptides (NRPs) are molecules produced by microorganisms and synthesized by huge multienzymatic complexes (38, 41), called nonribosomal peptide synthetases (NRPSs). These megaenzymes are organized into modules, one for each amino acid to be built into the peptide product. This is accomplished by division of each catalytic step into specialized semiautonomous domains. The basic set of domains (adenylation, thiolation, and condensation) within a module can be extended by substrate-modifying domains, including domains for substrate epimerization, β hydroxylation, N methylation, and heterocyclic ring formation. The peptide release is catalyzed by a thioesterase domain which can also, in many cases, be involved in an intramolecular reaction leading to a cyclic or partially cyclic peptide or, in fewer cases, in the oligomerization of peptide units (iterative biosynthesis). NRPs show a broad spectrum of biological activities and pharmaceutical applications. They can harbor antimicrobial, immunomodulator, or antitumor activities. Cyclosporine (5), an immunosuppressant drug widely used in organ transplantation, daptomycin (60) (marketed in the United States under the trade name Cubicin), used in the treatment of certain infections caused by Gram-positive bacteria, aminoadipyl-cysteinyl-valine (ACV)-tripeptide, which is the precursor of cephalosporin and penicillin (29), the most famous antibiotic, and also bleomycin (57), used in the treatment of several cancers, are some common examples of NRPs of high therapeutic importance. Two main structural traits distinguish these peptides from ribosomally synthesized peptides: first, their primary structure is more frequently cyclic (partially or totally) branched or polycyclic rather than linear and, second, the biodiversity of monomers incorporated in NRPs goes far beyond the 20 proteogenic amino acids residues. NRP monomers include modified versions of the proteogenic amino acids (e.g., methylated, hydroxylated, and d-forms) but also other monomers, such as, for example, 2-aminoisobutyric acid (Aib), hydroxyphenylglycine (Hpg), and 2,3-dihydroxybenzoic acid (diOH-Bz). However, essential characteristics of this diversity and its relationship with biological functions and producing organisms have been poorly understood until now.The development of the Norine database, the first resource entirely dedicated to NRPs (8, 9), filled this gap. Based on Norine data, we performed the first large-scale analysis of about a thousand peptides which represent a total coverage of more than 10,000 monomer occurrences, revealing the presence of as many as 500 different monomer types. A data-mining analysis of the monomeric compositions of NRPs allowed us to reveal a strong relationship between certain monomeric characteristics of NRPs and their biological function and producing organism. In addition to providing a comprehensive overview of monomeric biodiversity in NRPs, this work demonstrated (i) a dissimilarity of structural properties between bacterial and fungal NRPs; (ii) a significant relationship between NRPs synthesized by bacteria and those isolated from metazoa, especially from sponges, supporting the hypothesis that the peptides isolated from sponges are in reality synthesized by symbiotic bacteria rather than by the sponges themselves; and (iii) a certain monomer specificity to a class of biological activities. Those observations are supported by successful statistical predictions of biological activities of NRPs based on their monomeric compositions.     

Fast and accurate protein substructure searching with simulated annealing and GPUs

Background  

Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.     

SUPFAM: A database of sequence superfamilies of protein domains

Background  

SUPFAM database is a compilation of superfamily relationships between protein domain families of either known or unknown 3-D structure. In SUPFAM, sequence families from Pfam and structural families from SCOP are associated, using profile matching, to result in sequence superfamilies of known structure. Subsequently all-against-all family profile matches are made to deduce a list of new potential superfamilies of yet unknown structure.     

Support Vector Machines for predicting protein structural class

Background  

We apply a new machine learning method, the so-called Support Vector Machine method, to predict the protein structural class. Support Vector Machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure.     

A manually curated database of tetrapod mitochondrially encoded tRNA sequences and secondary structures

Background  

Mitochondrial tRNAs have been the subject of study for structural biologists interested in their secondary structure characteristics, evolutionary biologists have researched patterns of compensatory and structural evolution and medical studies have been directed towards understanding the basis of human disease. However, an up to date, manually curated database of mitochondrially encoded tRNAs from higher animals is currently not available.     

Exploring protein structural dissimilarity to facilitate structure classification

Background  

Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs).     

Life cycle assessment of the manufacture of lactide and PLA biopolymers from sugarcane in Thailand

Background  

l-lactide is the monomer for the polymer poly-l-lactic acid (PLLA). PLLA can be made from renewable resources, and is used in an increasing amount of applications. The biopolymer PLLA is one type of polymer of the family of polylactic acids (PLAs). Purac produces l-lactide and d-lactide, and supports partners with know-how to produce their own PLA from lactide. This life cycle assessment (LCA) study supporting market development presents the eco-profile of lactides and PLA biopolymers.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

Detecting internally symmetric protein structures

Background  

Many functional proteins have a symmetric structure. Most of these are multimeric complexes, which are made of non-symmetric monomers arranged in a symmetric manner. However, there are also a large number of proteins that have a symmetric structure in the monomeric state. These internally symmetric proteins are interesting objects from the point of view of their folding, function, and evolution. Most algorithms that detect the internally symmetric proteins depend on finding repeating units of similar structure and do not use the symmetry information.     

Improving the accuracy of protein secondary structure prediction using structural alignment

Background  

The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high.     

Domain-based small molecule binding site annotation

Background  

Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites.     

PSPP: A Protein Structure Prediction Pipeline for Computing Clusters

Background

Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user''s own high-performance computing cluster.

Methodology/Principal Findings

The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP) fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML) formats. So far, the pipeline has been used to study viral and bacterial proteomes.

Conclusions

The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform resource-intensive ab initio structure prediction.     

Practical and reliable FRET/FLIM pair of fluorescent proteins

Background  

In spite of a great number of monomeric fluorescent proteins developed in the recent years, the reported fluorescent protein-based FRET pairs are still characterized by a number of disadvantageous features, complicating their use as reporters in cell biology and for high-throughput cell-based screenings.     

Protein structure search and local structure characterization

Background  

Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.     

Statistical analysis and molecular dynamics simulations of ambivalent <Emphasis Type="Italic">α</Emphasis> -helices

Background  

Analysis of known protein structures reveals that identical sequence fragments in proteins can adopt different secondary structure conformations. The extent of this conformational diversity is influenced by various factors like the intrinsic sequence propensity, sequence context and other environmental factors such as pH, site directed mutations or alteration of the binding ligands. Understanding the mechanism by which the environment affects the structural ambivalence of these peptides has potential implications for protein design and reliable local structure prediction algorithms. Identification of the structurally ambivalent sequence fragments and determining the rules which dictate their conformational preferences play an important role in understanding the conformational changes observed in misfolding diseases. However, a systematic classification of their intrinsic sequence patterns or a statistical analysis of their properties and sequence context in relation to the origin of their structural diversity have largely remained unexplored.     

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.     

Constraint Logic Programming approach to protein structure prediction

Background  

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.     

SPRIT: Identifying horizontal gene transfer in rooted phylogenetic trees

Background  

Phylogenetic trees based on sequences from a set of taxa can be incongruent due to horizontal gene transfer (HGT). By identifying the HGT events, we can reconcile the gene trees and derive a taxon tree that adequately represents the species' evolutionary history. One HGT can be represented by a rooted Subtree Prune and Regraft (RSPR) operation and the number of RSPRs separating two trees corresponds to the minimum number of HGT events. Identifying the minimum number of RSPRs separating two trees is NP-hard, but the problem can be reduced to fixed parameter tractable. A number of heuristic and two exact approaches to identifying the minimum number of RSPRs have been proposed. This is the first implementation delivering an exact solution as well as the intermediate trees connecting the input trees.     

The LabelHash algorithm for substructure matching

Background  

There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity.     

Identification of putative domain linkers by a neural network – application to a large sequence database

Background  

The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences).