Analysis on conservation of disulphide bonds and their structural features in homologous protein domain families

International audienceBackground: Disulphide bridges are well known to play key roles in stability, folding and functions of proteins. Introduction or deletion of disulphides by site-directed mutagenesis have produced varying effects on stability and folding depending upon the protein and location of disulphide in the 3-D structure. Given the lack of complete understanding it is worthwhile to learn from an analysis of extent of conservation of disulphides in homologous proteins. We have also addressed the question of what structural interactions replaces a disulphide in a homologue in another homologue.Results: Using a dataset involving 34,752 pairwise comparisons of homologous protein domains corresponding to 300 protein domain families of known 3-D structures, we provide a comprehensive analysis of extent of conservation of disulphide bridges and their structural features. We report that only 54% of all the disulphide bonds compared between the homologous pairs are conserved, even if, a small fraction of the non-conserved disulphides do include cytoplasmic proteins. Also, only about one fourth of the distinct disulphides are conserved in all the members in protein families. We note that while conservation of disulphide is common in many families, disulphide bond mutations are quite prevalent. Interestingly, we note that there is no clear relationship between sequence identity between two homologous proteins and disulphide bond conservation. Our analysis on structural features at the sites where cysteines forming disulphide in one homologue are replaced by non-Cys residues show that the elimination of a disulphide in a homologue need not always result in stabilizing interactions between equivalent residues.Conclusion: We observe that in the homologous proteins, disulphide bonds are conserved only to a modest extent. Very interestingly, we note that extent of conservation of disulphide in homologous proteins is unrelated to the overall sequence identity between homologues. The non-conserved disulphides are often associated with variable structural features that were recruited to be associated with differentiation or specialisation of protein function

DISULFIND: a disulfide bonding state and cysteine connectivity prediction server

DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most likely connectivity patterns. The server is available at

Modelling multiple disulphide loop containing polypeptides by random conformation generation. The test cases of α-conotoxin GI and edothelin I

A general procedure for arriving at 3-D models of disulphiderich olypeptide systems based on the covalent cross-link constraints has been developed. The procedure, which has been coded as a computer program, RANMOD, assigns a large number of random, permitted backbone conformations to the polypeptide and identifies stereochemically acceptable structures as plausible models based on strainless disulphide bridge modelling. Disulphide bond modelling is performed using the procedure MODIP developed earlier, in connection with the choice of suitable sites where disulphide bonds could be engineered in proteins (Sowdhamini,R., Srinivasan,N., Shoichet,B., Santi,D.V., Ramakrishnan,C. and Balaram,P. (1989) Protein Engng, 3, 95-103). The method RANMOD has been tested on small disulphide loops and the structures compared against preferred backbone conformations derived from an analysis of putative disulphide subdatabase and model calculations. RANMOD has been applied to disulphiderich peptides and found to give rise to several stereochemically acceptable structures. The results obtained on the modelling of two test cases, a-conotoxin GI and endothelin I, are presented. Available NMR data suggest that such small systems exhibit conformational heterogeneity in solution. Hence, this approach for obtaining several distinct models is particularly attractive for the study of conformational excursions

Prediction of Oxidation States of Cysteines and Disulphide Connectivity

Knowledge on cysteine oxidation state and disulfide bond connectivity is of great importance to protein chemistry and 3-D structures. This research is aimed at finding the most relevant features in prediction of cysteines oxidation states and the disulfide bonds connectivity of proteins. Models predicting the oxidation states of cysteines are developed with machine learning techniques such as Support Vector Machines (SVMs) and Associative Neural Networks (ASNNs). A record high prediction accuracy of oxidation state, 95%, is achieved by incorporating the oxidation states of N-terminus cysteines, flanking sequences of cysteines and global information on the protein chain (number of cysteines, length of the chain and amino acids composition of the chain etc.) into the SVM encoding. This is 5% higher than the current methods. This indicates to us that the oxidation states of amino terminal cysteines infer the oxidation states of other cysteines in the same protein chain. Satisfactory prediction results are also obtained with the newer and more inclusive SPX dataset, especially for chains with higher number of cysteines. Compared to literature methods, our approach is a one-step prediction system, which is easier to implement and use. A side by side comparison of SVM and ASNN is conducted. Results indicated that SVM outperform ASNN on this particular problem. For the prediction of correct pairings of cysteines to form disulfide bonds, we first study disulfide connectivity by calculating the local interaction potentials between the flanking sequences of the cysteine pairs. The obtained interaction potential is further adjusted by the coefficients related to the binding motif of enzymes during disulfide formation and also by the linear distance between the cysteine pairs. Finally, maximized weight matching algorithm is applied and performance of the interaction potentials evaluated. Overall prediction accuracy is unsatisfactory compared with the literature. SVM is used to predict the disulfide connectivity with the assumption that oxidation states of cysteines on the protein are known. Information on binding region during disulfide formation, distance between cysteine pairs, global information of the protein chain and the flanking sequences around the cysteine pairs are included in the SVM encoding. Prediction results illustrate the advantage of using possible anchor region information

Protein structure prediction and modelling

The prediction of protein structures from their amino acid sequence alone is a very challenging problem. Using the variety of methods available, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis uses many of the widely available methods for the prediction and modelling of protein structures and proposes some new ideas for aiding the process. A new method for measuring the buriedness (or exposure) of residues is discussed which may lead to a potential way of assessing proteins' individual amino acid placement and whether they have a standard profile. This may become useful in assessing predicted models. Threading analysis and modelling of structures for the Critical Assessment of Techniques for Protein Structure Prediction (CASP2) highlights inaccuracies in the current state of protein prediction, particularly with the alignment predictions of sequence on structure. An in depth analysis of the placement of gaps within a multiple sequence threading method is discussed, with ideas for the improvement of threading predictions by the construction of an improved gap penalty. A threading based homology model was constructed with an RMSD of 6.2A, showing how combinations of methods can give usable results. Using a distance geometry method, DRAGON, the ab initio prediction of a protein (NK Lysin) for the CASP2 assessment was achieved with an accuracy of 4.6Å. This highlighted several ideas in disulphide prediction and a novel method for predicting which cysteine residues might form disulphide bonds in proteins. Using a combination of all the methods, with some like threading and homology modelling proving inadequate, an ab initio model of the N-terminal domain of a GPCR was built based on secondary structure and predictions of disulphide bonds. Use of multiple sequences in comparing sequences to structures in threading should give enough information to enable the improvements required before threading can be-come a major way of building homology models. Furthermore, with the ability to predict disulphide bonds: restraints can be placed when building models, ab initio or otherwise

Dinosolve: A Protein Disulfide Bonding Prediction Server Using Context-Based Features to Enhance Prediction Accuracy

Background: Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins. Methods: In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction. Results: The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%. Conclusions: Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named Dinosolve available at: http://hpcr.cs.odu.edu/dinosolve

An in silico approach to the ß-defensin structure-activity problem

ß-defensins are a family of cationic, cysteine-rich antimicrobial peptide (AMP) components of the innate immune response to infection. They are expressed both inducibly and constitutively within vertebrates, insects and plants and antimicrobial action is observed against (both gram positive and gram negative) bacteria and a subset of enveloped viruses. The antimicrobial phenomenon is thought to result from membrane permeablisation that depends on key, electrostatic binding events between defensin and pathogen cell surface. This thesis tackles, in silico, two components of this structure-activity problem: That of rationally predicting ß-defensin structure, and that of elucidating the first (presumed) binding events between ß-defensin and pathogen cell surface. Preliminary results suggest that successful in silico folding requires a mobile disulphide bond strategy to circumvent kinetic trapping of intermediate states, and that the mechanism of pathogenic binding involves a complex interplay of hydrogen bonding, as well as productive electrostatic interactions

Bioinformatic studies of small disulphide-rich proteins (SDPs)

Ph.DDOCTOR OF PHILOSOPH

Prediction of Factors Determining Changes in Stability in Protein Mutants

Analysing the factors behind protein stability is a key research topic in molecular biology and has direct implications on protein structure prediction and protein-protein docking solutions. Protein stability upon point mutations were analysed using a distance dependant pair potential representing mainly through-space interactions and torsion angle potential representing neighbouring effects as a basic statistical mechanical setup for the analysis. The synergetic effect of accessible surface area and secondary structure preferences was used as a classifier for the potentials. Various principles underlying the protein structure description must also be studied carefully to efficiently understand the relationships between sequence, structure and function. Mean force potentials from atom interactions and main torsion angles were used by different investigators to evaluate the protein structure, stability and protein-protein interactions. In recent experiments, these were also used in the prediction of protein function and enzyme catalysis. Five different atom classification models with interactions in different distance ranges were selected to check their ability to effectively describe the protein environment. Furthermore, torsion angle potentials were also derived in addition to atom potentials so that orientational information of amino acids can be included to the model. The five atom classification models that are used for atom potentials include the following: a basic five (basic5) atom model (C aliphatic, C aromatic, H, O, N), amino acid C[alpha]atoms (C[alpha]20), Li-Nussinov atom model (LN24), SATIS model (SA28) and Melo and Feytmans atom model (MF40). Carbon atoms with aromatic and aliphatic nature exhibit significantly different chemical and functional behaviour and they were considered separately in the basic5 atom model with N, O and S. Li and Nussinov defined 24 different amino acid atom types using the polarity and hydrophobicity of atoms. Similarly, SATIS is also a protocol for the definition and automatic assignment of atom types and the classification of atoms. The free energy values (ddG and ddGH2O values from thermal and chemical denaturation) of unfolding from point mutation experiments were used as an experimental measure of protein stability. In future, this method can also be extended to evaluate other structure descriptors. It has already been reported that the measured free energy changes between wild type and mutant proteins can be predicted using statistical potentials. But, these models lack good prediction efficiency and reliability to predict protein mutant stability in future. A dataset of 4024 non-redundant structures was used to derive the atom interactions and torsion angles phi and psi, after running DSSP for the whole dataset. For torsion potentials, the bins were normalised with a standard procedure using the circular Gaussian function for phi and psi having the bivariate normal distribution. Results were validated based on the correlation observed between the experimental ddG and predicted ddG values. Prediction accuracy of being correctly predicted as stabilising or destabilising was also observed. Results show that the Melo and Feytmens atom model predicts the protein stability to a maximum extent, since it showed a correlation coefficient of 0.85 with 85.31% of 1536 mutations correctly predicted to be either stabilising or destabilising. SA28, LN24, C[alpha]20 and basic5 atom models showed a correlation coefficient of 0.82, 0.78, 0.76 and 0.55 respectively. Later, statistical stepwise regression methods were used to optimise the number of atoms used for the model. Effect of torsion angle potentials with and without the Gaussian apodisation was compared. This shows that the amino acids adapt perturbed torsion angle conformations in partially buried beta sheets than the other structural elements. For the final prediction model, two datasets of point mutations were taken for the comparison of theoretically predicted stabilising energy values with experimental ddG and ddGH2O from thermal and chemical denaturation experiments respectively. These include 1538 and 1581 mutations respectively and contain 101 proteins that share wide range of sequence identity. Results were carefully evaluated with a wide range of statistical tests. Results show a maximum correlation of 0.87 between predicted and experimental ddG values and a prediction accuracy of 85.3% (stabilising or destabilising) for all mutations together. A correlation of 0.77 each for the test dataset of split-sample validation and k-fold cross validation tests was obtained and a correlation of 0.70 was shown by the jack-knife test. A similar model was implemented and the results were analysed for mutations with ddGH2O. A correlation of 0.79 was observed with a prediction efficiency of 85.03%. A web tool has been developed to use this prediction model (www.biotool.uni-koeln.de)

Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene

ReaxFF provides a method to model reactive chemical systems in large-scale molecular dynamics simulations. Here, we developed ReaxFF parameters for phosphorus and hydrogen to give a good description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. The potential parameters were obtained by conducting unbiased global optimization with respect to a set of reference data generated by extensive ab initio calculations. We extend ReaxFF by adding a 60{\deg} correction term which significantly improves the description of phosphorus clusters. Emphasis has been put on obtaining a good description of mechanical response of black phosphorene with different types of defects. Compared to nonreactive SW potential [1], ReaxFF for P/H systems provides a huge improvement in describing the mechanical properties the pristine and defected black phosphorene and the thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is observed that single vacancies weaken the black phosphorene more than double vacancies with higher formation energy. Our results also show that mechanical response of black phosphorene is more sensitive to defects for the zigzag direction than for the armchair direction. Since ReaxFF allows straightforward extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF parameters for P/H systems build a solid foundation for the reactive force field description of heterogeneous P systems, including P-containing 2D van der Waals heterostructures, oxides, etc