Protein Functional Sites Research Articles

Cadmium-zinc exchange and their binary relationship in the structure of Zn-related proteins: a mini review.

Research on cadmium-zinc exchange in proteins is important for understanding one of the main sources of Cd's biological toxicity. Because of the similar properties of these two elements, most living organisms must prevent Cd from replacing Zn in Zn-requiring proteins in order for those proteins to function normally. Recent structural studies of a variety of proteins associated with the versatile physiological functions of Zn have revealed widespread instances of Cd-Zn exchange in proteins of a large number of living organisms. Ongoing work is focused on discovering the structural mechanisms of Cd-Zn exchange and the potentially diverse roles of Cd at Zn functional sites in proteins. This research is a prerequisite to understanding the evolution of Cd-tolerant species (e.g., Cd hyperaccumulating plants) and to the engineering of optimal strategies for protecting the public heath against Cd pollution.

Redox proteomics: from bench to bedside.

In general protein posttranslation modifications (PTMs) involve the covalent addition of functional groups or molecules to specific amino acid residues in proteins. These modifications include phosphorylation, glycosylation, S-nitrosylation, acetylation, lipidation, among others (Angew Chem Int Ed Engl 44(45):7342-7372, 2005). Although other amino acids can undergo different kinds of oxidative posttranslational modifications (oxPTMs) (Exp Gerontol 36(9):1495-1502, 2001), in this chapter oxPTM will be considered specifically related to Cysteine oxidation, and redox proteomics here is translated as a comprehensive investigation of oxPTMs, in biological systems, using diverse technical approaches. Protein Cysteine residues are not the only amino acid that can be target for oxidative modifications in proteins (Exp Gerontol 36(9):1495-1502, 2001; Biochim Biophys Acta 1814(12):1785-1795, 2011), but certainly it is among the most reactive amino acid (Nature 468(7325):790-795, 2010). Interestingly, it is one of the least abundant amino acid, but it often occurs in the functional sites of proteins (J Mol Biol 404(5):902-916, 2010). In addition, the majority of the Cysteine oxidations are reversible, indicating potential regulatory mechanism of proteins. The global analysis of oxPTMs has been increasingly recognized as an important area of proteomics, because not only maps protein caused by reactive oxygen species (ROS) and reactive nitrogen species (RNS), but also explores protein modulation involving ROS/RNS. Furthermore, the tools and strategies to study this type oxidation are also very abundant and developed, offering high degree of accuracy on the results. As a consequence, the redox proteomics field focuses very much on analyzing Cysteine oxidation in proteins under several experimental conditions and diseases states. Therefore, the identification and localization of oxPTMs within cellular milieu became critical to understand redox regulation of proteins in physiological and pathological conditions, and consequently an important information to develop better strategies for treatment and prevention of diseases associated with oxidative stress.There is a wide range of techniques available to investigate oxPTMs, including gel-based and non-gel-based separation approaches to be combined with sophisticated methods of detection, identification, and quantification of these modifications. The strategies and approaches to study oxPTMs and the respective applications related to physiological and pathological conditions will be discussed in more detail in this chapter.

A donor–acceptor pair for the real time study of vibrational energy transfer in proteins

Vibrational energy transfer (VET) is believed to play an important role in protein function. Theoretical studies predict highly directional, anisotropic VET in proteins. Distinct energy transfer pathways which connect distant functional sites in proteins have been proposed by simulations, indicating a function in allosteric communication. Experimental evidence for such pathways, however, is lacking. In small molecules, ultrafast vibrational pump-probe spectroscopy has been used to investigate VET between different parts of a molecule in great detail. Here, we address the requirements for extending this powerful approach to proteins and present a protein-compatible donor-acceptor pair for the real time investigation of VET. This VET pair consists of two non-native amino acids, β-(1-azulenyl)-alanine and azidohomoalanine, which can be positioned site-specifically and are found to be very well suited for spectroscopic studies of VET. Important for the study of proteins, co-translational incorporation of each of the amino acids has been demonstrated before using mutually independent approaches of protein engineering. We investigated the performance of the proposed VET pair in a model peptide which is designed to contain additional characteristic vibrational modes frequently used in infrared spectroscopy of proteins. Despite a larger inter-residue distance, we find that our VET acceptor generates a major signal that is easily observed compared to the other vibrational modes in the congested parts of the spectrum. We find sufficient signal size at concentrations compatible with proteins and over distances that will allow tracking of energy flow along predicted transfer pathways.

The mutational landscape of phosphorylation signaling in cancer

Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction.

Research on the Prediction of Protein Functional Sites Based on the Dimension Reduction Algorithm

Knowing the ProteinRNA interactions often provides useful clues for finding its biological function in a biological system. Dimension reduction method is one of most famous machine learning tools. Some researchers have begun to explore dimension reduction method for computer vision problems. Few such attempts have been made for classification of high-dimensional protein data sets. Here, dimensionality reduction algorithm is introduced to predict the protein-RNA interaction sites. Our jackknife test results indicate that it is very promising to use the dimensionality reduction approaches to cope with complicated problems in biological systems, such as predicting the protein-RNA interaction sites.

Computational Design of an Unnatural Amino Acid Dependent Metalloprotein with Atomic Level Accuracy

Genetically encoded unnatural amino acids could facilitate the design of proteins and enzymes of novel function, but correctly specifying sites of incorporation and the identities and orientations of surrounding residues represents a formidable challenge. Computational design methods have been used to identify optimal locations for functional sites in proteins and design the surrounding residues but have not incorporated unnatural amino acids in this process. We extended the Rosetta design methodology to design metalloproteins in which the amino acid (2,2'-bipyridin-5yl)alanine (Bpy-Ala) is a primary ligand of a bound metal ion. Following initial results that indicated the importance of buttressing the Bpy-Ala amino acid, we designed a buried metal binding site with octahedral coordination geometry consisting of Bpy-Ala, two protein-based metal ligands, and two metal-bound water molecules. Experimental characterization revealed a Bpy-Ala-mediated metalloprotein with the ability to bind divalent cations including Co(2+), Zn(2+), Fe(2+), and Ni(2+), with a Kd for Zn(2+) of ∼40 pM. X-ray crystal structures of the designed protein bound to Co(2+) and Ni(2+) have RMSDs to the design model of 0.9 and 1.0 Å respectively over all atoms in the binding site.

DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches

Accurate prediction of DNA-binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning- and template-based methods. Our machine learning-based method was based on the probabilistic combination of a structure-based and a sequence-based predictor, both of which were implemented using support vector machines algorithms. The former included our well-designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA-binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template-based method depended on structural alignment and utilized the template structure from known protein-DNA complexes to infer DNA-binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high-quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state-of-art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/.

STRUCTURE-BASED METHODS FOR COMPUTATIONAL PROTEIN FUNCTIONAL SITE PREDICTION

Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details.

Computational identification and analysis of arsenate reductase protein in Cronobacter sakazakii ATCC BAA-894 suggests potential microorganism for reducing arsenate

This study focuses a bioinformatics-based prediction of arsC gene product arsenate reductase (ArsC) protein in Cronobacter sakazakii BAA-894 strain. A protein structure-based study encloses three-dimensional structural modeling of target ArsC protein, was carried out by homology modeling method. Ultimately, the detection of active binding regions was carried out for characterization of functional sites in protein. The ten probable ligand binding sites were predicted for target protein structure and highlighted the common binding residues between target and template protein. It has been first time identified that modeled ArsC protein structure in C. sakazakii was structurally and functionally similar to well-characterized ArsC protein of Escherichia coli because of having same structural motifs and fold with similar protein topology and function. Investigation revealed that ArsC from C. sakazakii can play significant role during arsenic resistance and potential microorganism for bioremediation of arsenic toxicity.

SNVDis: A Proteome-wide Analysis Service for Evaluating nsSNVs in Protein Functional Sites and Pathways

Amino acid changes due to non-synonymous variation are included as annotations for individual proteins in UniProtKB/Swiss-Prot and RefSeq which present biological data in a protein- or gene-centric fashion. Unfortunately, proteome-wide analysis of non-synonymous single-nucleotide variations (nsSNVs) is not easy to perform because information on nsSNVs and functionally important sites are not well integrated both within and between databases and their search engines. We have developed SNVDis that allows evaluation of proteome-wide nsSNV distribution in functional sites, domains and pathways. More specifically, we have integrated human-specific data from major variation databases (UniProtKB, dbSNP and COSMIC), comprehensive sequence feature annotation from UniProtKB, Pfam, RefSeq, Conserved Domain Database (CDD) and pathway information from Protein ANalysis THrough Evolutionary Relationships (PANTHER) and mapped all of them in a uniform and comprehensive way to the human reference proteome provided by UniProtKB/Swiss-Prot. Integrated information of active sites, pathways, binding sites, domains, which are extracted from a number of different sources, provides a detailed overview of how nsSNVs are distributed over the human proteome and pathways and how they intersect with functional sites of proteins. Additionally, it is possible to find out whether there is an over- or under-representation of nsSNVs in specific domains, pathways or user-defined protein lists. The underlying datasets are updated once every 3months. SNVDis is freely available at http://hive.biochemistry.gwu.edu/tool/snvdis.

Distribution of amino acids in functional sites of proteins with high melting temperature.

The stability of proteins in its native state has an important implication on its function and evolution. The functional site analysis may lead to better understanding of how these amino acid distributions influence the melting temperature of proteins. It has been reported that increasing the fraction of hydrophobic contacts in a protein tends to raise melting temperature; increasing the fraction of repulsive charge contacts decrease the melting temperature and consistent with a destabilizing effect. The role of amino acid distribution as hydrophobic, charged and polar residues in proteins and mainly in its functional sites has been studied. Due to limited data availability, redundancy check and controlled environment parameters, the study was carried out with ten single chain-wild proteins having melting temperature above 80°C at pH 7. The analysis depicts that, the entire protein, hydrophobic residues are more frequent in single chain proteins and charged residues are more frequent in multi-chains proteins. In functional sites of these proteins, hydrophobic and charged residues are equally frequent in single chain proteins and charged residues are very high in multi-chains proteins. But, the polar residue distribution remains same for both single chain and multi-chain proteins and its functional sites.

New and continuing developments at PROSITE

PROSITE (http://prosite.expasy.org/) consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule a collection of rules, which increases the discriminatory power of these profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. PROSITE signatures, together with ProRule, are used for the annotation of domains and features of UniProtKB/Swiss-Prot entries. Here, we describe recent developments that allow users to perform whole-proteome annotation as well as a number of filtering options that can be combined to perform powerful targeted searches for biological discovery. The latest version of PROSITE (release 20.85, of 30 August 2012) contains 1308 patterns, 1039 profiles and 1041 ProRules.

Computational Prediction of Important Regions in Protein Sequences [Life Sciences

This column summarizes the main approaches for predicting protein functional sites from sequence information. Extracting functional regions from linear protein sequences has many similarities with prototypical problems in signal processing, and some of the methodologies used are taken from this discipline.

Literature mining of protein-residue associations with graph rules learned through distant supervision

BackgroundWe propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model.ResultsThe performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods.ConclusionsThe primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

Web-Based Computational System for Protein-Protein Interaction Inference

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of <TEX>$10^{-5}$</TEX>, <TEX>$10^{-3}$</TEX> respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

Structure-based rebuilding of coevolutionary information reveals functional modules in rhodopsin structure

Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. We present a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into a protein structure network. A path-based network model is adapted to describe information transfer between residues, and the statistical significance is estimated by network shuffling. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues. In addition, coevolutionary coupling is statistically controlled to remove the structural effects. When applied to the rhodopsin structure, the SCMA method identified a much higher percentage of functional residues than the typical coevolution score (61% vs. 22%). In addition, statistically significant residues are used to construct the coevolved residue–residue subnetwork. The network has one highly connected node (retinal bound Lys296), indicating that Lys296 can induce and regulate most other coevolved residues in a variety of locations. The coevolved network consists of a few modular clusters which have distinct functional roles. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Text mining improves prediction of protein functional sites.

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

1PT181 Free energy decomposition and functional sites of proteins(The 50th Annual Meeting of the Biophysical Society of Japan)

1PT181 Free energy decomposition and functional sites of proteins(The 50th Annual Meeting of the Biophysical Society of Japan)

Development and Application of Multidimensional HPLC Mapping Method for O-linked Oligosaccharides.

Glycosylation improves the solubility and stability of proteins, contributes to the structural integrity of protein functional sites, and mediates biomolecular recognition events involved in cell-cell communications and viral infections. The first step toward understanding the molecular mechanisms underlying these carbohydrate functionalities is a detailed characterization of glycan structures. Recently developed glycomic approaches have enabled comprehensive analyses of N-glycosylation profiles in a quantitative manner. However, there are only a few reports describing detailed O-glycosylation profiles primarily because of the lack of a widespread standard method to identify O-glycan structures. Here, we developed an HPLC mapping method for detailed identification of O-glycans including neutral, sialylated, and sulfated oligosaccharides. Furthermore, using this method, we were able to quantitatively identify isomeric products from an in vitro reaction catalyzed by N-acetylglucosamine-6O-sulfotransferases and obtain O-glycosylation profiles of serum IgA as a model glycoprotein.

SitEx: a computer system for analysis of projections of protein functional sites on eukaryotic genes

Search of interrelationships between the structural–functional protein organization and exon structure of encoding gene provides insights into issues concerned with the function, origin and evolution of genes and proteins. The functions of proteins and their domains are defined mostly by functional sites. The relation of the exon–intron structure of the gene to the protein functional sites has been little studied. Development of resources containing data on projections of protein functional sites on eukaryotic genes is needed. We have developed SitEx, a database that contains information on functional site amino acid positions in the exon structure of encoding gene. SitEx is integrated with the BLAST and 3DExonScan programs. BLAST is used for searching sequence similarity between the query protein and polypeptides encoded by single exons stored in SitEx. The 3DExonScan program is used for searching for structural similarity of the given protein with these polypeptides using superimpositions. The developed computer system allows users to analyze the coding features of functional sites by taking into account the exon structure of the gene, to detect the exons involved in shuffling in protein evolution, also to design protein-engineering experiments. SitEx is accessible at http://www-bionet.sscc.ru/sitex/. Currently, it contains information about 9994 functional sites presented in 2021 proteins described in proteomes of 17 organisms.