Protein and Peptide Letters - Volume 21, Issue 8, 2014

Laccases belong to the multicopper binding protein family that catalysis the reduction of oxygen molecule to produce water. These enzymes are glycosylated proteins and have been isolated and purified from fungi, bacteria, plant, insects and lichens. The variety of commercial and industrial application of laccases has attracted much attention towards the research addressing different aspects of the protein characterization, production and fit for purpose molecule. Here we briefly discuss the purification, catalytic mechanism in light of available understanding of structure-function relationship and the tailoring side of the protein, which has been the subject of recent research. Purification strategy of laccases is a method of choice and is facilitated by increased production of the enzyme. The structure-function relationship has given insights to unfold the catalytic mechanism. Site directed mutagenesis and other modification at C-terminal end or surrounding environment of copper centres have shown promising results to fit for purpose aspect, with a lot remains to be explored in glycosylation status and its alteration.

Serine Protease inhibitors (serpins) are a super-family of proteins that controls the proteinases involved in the inflammation, complementation, coagulation and fibrinolytic pathways. Serpins are prone to conformational diseases due to a complex inhibition mechanism that involves large scale conformational change, and their susceptibility to undergo point mutations might lead to functional defects. Serpins are associated with diseases like emphysema/cirrhosis, angioedema, familial dementia, chronic obstructive bronchitis and thrombosis. Serpin polymerization based pathologies are fairly widespread and devising a cure has been difficult due to lack of clarity regarding its mechanism. Serpin can exist in various conformational states and has a variable cofactor binding ability. It has a large genome and proteome database which can be utilized to gain critical insight into their structure, mechanism and defects. Comprehensive computational studies on the serpin family is lacking, most of the work done till date is limited and deals mostly with few individual serpins. We have tried to analyze few aspect of this family using diverse computational biology tools and have shown the following: a) the importance of residue burial linked shift in the conformational stability as a major factor in increasing the polymer propensity in serpins. b) Amino acids involved in the polymerization are in general completely buried in the native conformation. c) An isozyme specific antithrombin study showed the structural basis of improved heparin binding to beta antithrombin as compared to alpha-antithrombin. d) A comprehensive cavity analysis showed its importance in inhibition and polymerizaiton and finally e) an interface analysis of various serpin protease complexes identified critical evolutionary conserved residues in exosite that determines its protease specificity. This work introduces the problem and emphasizes on the need for in-depth computational studies of serpin superfamily.

In the present study, we have evaluated the impacts of point mutations on structural and functional evolution of hypothetical proteins identical to bacterial ADP-ribosylation superfamily members using bioinformatics approaches. A combined approach of molecular modelling and dynamics was employed to generate energetically stable structures from hypothetical protein sequences. Improper energy and structural constraints of the resulted homology models were stabilized by molecular dynamic simulation and hybrid Monte Carlo approaches. Since amino acid substitutions occurring in highly mutable functional sites, catalytic activity or substrate specificity would be expected to adjust without compromising their structural stability. In silico mutagenesis studies showed that protein structural stability has not been changed upon point mutations, but functional firmness has modified unusually from virulence to avirulence. Protein variants such as BTA3V10 (Gly421→Val421), BTA3V11 (Gly421→Leu421), BTA3V17 (Gly422→Phe422) and PTS15V1 (Cys26→Met26) and PTS15V2 (Cys26→Thy26) generated from this study showed to have a fast fold rate and stable energetic structures compared to wild type proteins. Overall, structures and functional integrity of the hypothetical proteins were identical to the members in bacterial ADP-ribosylation superfamily. A catalytic activity of ADP-ribosyltransferase existing in the hypothetical proteins would determine whether virulent state or avirulent state by deleterious mutations occurring in the subdynamic space of a conserved domain.

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

Protein folding rate is a valuable clue for understanding the variations in protein folding kinetics. The ability to accurately discriminate protein folding rate change is very helpful in protein design. However, there are fewer studies on the influence of amino acid substitution to protein folding rates. In our earlier studies, we constructed a dataset of 467 mutants upon amino acid substitution and proposed novel methods for discriminating and predicting the accelerating and decelerating mutants during the folding process. This study aimed to effectively develop simple rules for discriminating accelerating mutants from decelerating ones upon single amino acid substitution. The main points of the study were to build a more general dataset F661 with 661 mutants, analyze the dataset systematically, and then implement different data mining techniques to build discrimination rules. Furthermore, the rules obtained from different methods were interpreted, evaluated, compared and integrated. The results appeared that the present approach may effectively develop simple rules from these mutants and the quality of the rules may be improved by combining the statistical and learning methods. These results suggest that the present method, as well as the rules, may advance the understanding of discriminating protein folding rate change. The details of the rules along with relevant information have been integrated and available freely at http://bioinformatics.myweb.hinet.net/rulefr.htm

Free energy landscapes, backbone flexibility and residue-residue couplings for being co-rigid or co-flexible are calculated from the minimal Distance Constraint Model (mDCM) on an exploratory dataset consisting of VL, scFv and Fab antibody fragments. Experimental heat capacity curves are reproduced markedly well, and an analysis of quantitative stability/flexibility relationships (QSFR) is applied to a representative VL domain and several complexes in the scFv and Fab forms. Global flexibility in the denatured ensemble typically decreases in the larger complexes due to domain-domain interfaces. Slight decreases in global flexibility also occur in the native state of the larger fragments, but with a concurrent large increase in correlated flexibility. Typically, a VL fragment has more co-rigid residue pairs when isolated compared to the scFv and Fab forms, where correlated flexibility appears upon complex formation. This context dependence on residue- residue couplings in the VL domain across length scales of a complex is consistent with the evolutionary hypothesis of antibody maturation. In comparing two scFv mutants with similar thermodynamic stability, local and long-ranged changes in backbone flexibility are observed. In the case of anti-p24 HIV-1 Fab, a variety of QSFR metrics were found to be atypical, which includes comparatively greater co-flexibility in the VH domain and less co-flexibility in the VL domain. Interestingly, this fragment is the only example of a polyspecific antibody in our dataset. Finally, the mDCM method is extended to cases where thermodynamic data is incomplete, enabling high throughput QSFR studies on large numbers of antibody fragments and their complexes.

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock.

Molecular function in cellular processes is governed by protein-protein interactions (PPIs) within biological networks. Selective yet specific association of these protein partners contributes to diverse functionality such as catalysis, regulation, assembly, immunity, and inhibition in a cell. Therefore, understanding the principles of protein-protein association has been of immense interest for several decades. We provide an overview of the experimental methods used to determine PPIs and the key databases archiving this information. Structural and functional information of existing protein complexes confers knowledge on the principles of PPI, based on which a classification scheme for PPIs is then introduced. Obtaining high-quality non-redundant datasets of protein complexes for interaction characterisation is an essential step towards deciphering their underlying binding principles. Analysis of physicochemical features and their documentation has enhanced our understanding of the molecular basis of protein-protein association. We describe the diverse datasets created/collected by various groups and their key findings inferring distinguishing features. The currently available interface databases and prediction servers have also been compiled.

Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. In this study we applied our prediction method to a pathway reconstruction problem of bacterial chemotaxis by using two different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.

Protein-carbohydrate interactions play important roles in several biological processes in living organisms. Understanding the recognition mechanism of protein-carbohydrate complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-carbohydrate complexes. Our method identified 3.3% of residues as binding sites in protein- carbohydrate complexes whereas the binding site residues in protein-protein, protein-RNA and protein-DNA complexes are 10.8%, 7.6% and 8.7%, respectively. In all these complexes, binding site residues are accommodated in singleresidue segments so that the neighboring residues are not involved in binding. Binding propensity analysis indicates the dominance of Trp to interact with carbohydrates through aromatic-aromatic interactions. Further, the preference of residue pairs and tripeptides interacting with carbohydrates has been delineated. The information gained in the present study will be beneficial for understanding the recognition mechanism of protein-carbohydrate complexes and for predicting the binding sites in carbohydrate binding proteins.

Nuclear receptors constitute a super family of protein hormones that serve as transcription factors. They typically reside in the cytosol and, after ligand binding, migrate to the nucleus to exert their biological action. Ligands are lipophilic, small molecules including retinoids, steroids, thyroxine, and vitamin D. Nuclear receptors being important regulators of gene expression, constitute 13% of proteins targeted by various drugs. Thus it becomes important to identify the ligand binding pockets on these proteins. Support Vector Machine (SVM) classifier was built to identify nuclear receptor ligand binding pockets. Positive dataset consisted of the ligand binding pockets of known nuclear receptor-ligand complex structures. Negative dataset consisted of ligand binding pockets of proteins other than nuclear receptors and nonligand binding pockets of nuclear receptors. SVM model yielded a 10 fold cross-validation accuracy of 96% using linear kernel. Also, it is helpful to find out the class of nuclear receptor in order to design a “class-specific” drug. In case of the multiclass nuclear receptor dataset comprising of nuclear receptors belonging to three different classes, SVM model for classification yielded an average 10-fold cross validation accuracy of 92 % for this dataset. SVM algorithm identifies and classifies nuclear receptor binding pockets with excellent accuracy. Top ranked features indicate the hydrophobic nature of ligand binding pocket of nuclear receptors. Conserved Leucine and phenylalanine residues form a distinguishing feature of these binding pockets. Along-with identification of NR binding pockets, important top ranked features are listed which would be useful in screening of possible drug molecules with NRs as molecular targets.

Dengue virus (DENV), one of the members of genus Flavivirus is emerging as a global threat to human health. It had led to the emergence of dengue fever (flu-like illness), dengue shock syndrome, and the most severe dengue hemorrhagic fever (severe dengue with bleeding abnormalities). As Dengue hemorrhage diseases are the life-threatening ones, attempts are being made worldwide to design inhibitors for DENV-2 NS2B-NS3 protease. NS2B/NS3 protease plays a vital role in the replication of dengue virus. The trypsin-like serine protease domain of NS3 contains the functional catalytic triad His-51, Asp-75, and Ser-135 in the N-terminal region. Inhibition of the NS3 protease activity is expected to prevent the propagation of dengue virus. Current drug discovery methods are largely inefficient and thus relatively ineffective in tackling the growing threat to public health presented by emerging and remerging viral pathogens. Recently, there has been a need of interest in peptides and their mimetics as potential antagonists for dengue protease because these small peptides are unlikely to invoke an immune response since they fall below the immunogenic threshold. They are often potent and display fewer toxicity issues than small-molecule compounds as a result of high specificity. This study was conducted to design peptides as enzyme inhibitors of dengue virus NS3 protease through computational approach. Crystallographic structure of dengue protease was retrieved from Protein Data Bank (PDBID: 2FOM) and docked with the peptides and the results are analyzed. From the docking studies reported in this paper, tetrapeptide (Lys-Gly-Pro-Glu), pentapeptide (Ser-Ile-Lys-Phe-Ala) and hexapeptide (Ala-Ile-Lys-Lys-Phe-Ser) with glide energy -70.0 kcal/mol, -72.2 kcal/mol and - 80.4 kcal/mol respectively show promising results which can be considered for further optimization and in vitro studies.

Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed.

Background. Obesity is a major health problem in both developed and emerging countries. Obesity is a complex disease whose etiology involves genetic factors in strong interplay with environmental determinants and lifestyle. The discovery of genetic factors and biological pathways underlying human obesity is hampered by the difficulty in controlling the genetic background of human cohorts. Animal models are then necessary to further dissect the genetics of obesity. Pig has emerged as one of the most attractive models, because of the similarity with humans in the mechanisms regulating the fat deposition. Results. We collected the genes related to obesity in humans and to fat deposition traits in pig. We localized them on both human and pig genomes, building a map useful to interpret comparative studies on obesity. We characterized the collected genes structurally and functionally with BAR+ and mapped them on KEGG pathways and on STRING protein interaction network. Conclusions. The collected set consists of 361 obesity related genes in human and pig genomes. All genes were mapped on the human genome, and 54 could not be localized on the pig genome (release 2012). Only for 3 human genes there is no counterpart in pig, confirming that this animal is a good model for human obesity studies. Obesity related genes are mostly involved in regulation and signaling processes/pathways and relevant connection emerges between obesity-related genes and diseases such as cancer and infectious diseases.

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.

Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion. We show that this technique provides an improvement over other intensity based strategies, especially for low mass precursors.