Inter-residue Contacts Research Articles

CONS-COCOMAPS: a novel tool to measure and visualize the conservation of inter-residue contacts in multiple docking solutions

BackgroundThe development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step.MethodsAll the programs under CONS-COCOMAPS have been written in python, taking advantage of python libraries such as SciPy and Matplotlib. CONS-COCOMAPS is freely available as a web tool at the URL:http://www.molnac.unisa.it/BioTools/conscocomaps/.ResultsHere we presented CONS-COCOMAPS, a novel tool to easily measure and visualize the consensus in multiple docking solutions. CONS-COCOMAPS uses the conservation of inter-residue contacts as an estimate of the similarity between different docking solutions. To visualize the conservation, CONS-COCOMAPS uses intermolecular contact maps.ConclusionsThe application of CONS-COCOMAPS to test-cases taken from recent CAPRI rounds has shown that it is very efficient in highlighting even a very weak consensus that often is biologically meaningful.

Backbone assignment of perdeuterated proteins using long-range H/C-dipolar transfers

For micro-crystalline proteins, solid-state nuclear magnetic resonance spectroscopy of perdeuterated samples can provide spectra of unprecedented quality. Apart from allowing to detect sparsely introduced protons and thereby increasing the effective resolution for a series of sophisticated techniques, deuteration can provide extraordinary coherence lifetimes--obtainable for all involved nuclei virtually without decoupling and enabling the use of scalar magnetization transfers. Unfortunately, for fibrillar or membrane-embedded proteins, significantly shorter transverse relaxation times have been encountered as compared to micro-crystalline proteins despite an identical sample preparation, calling for alternative strategies for resonance assignment. In this work we propose an approach towards sequential assignment of perdeuterated proteins based on long-range (1)H/(13)C Cross Polarization transfers. This strategy gives rise to H/N-separated correlations involving C(α), C(β), and CO chemical shifts of both, intra- and interresidual contacts, and thus connecting adjacent residues independent of transverse relaxation times.

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

A protocol for computer-based protein structure and function prediction.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Conformational flexibility and the mechanisms of allosteric transitions in topologically similar proteins

Conformational flexibility plays a central role in allosteric transition of proteins. In this paper, we extend the analysis of our previous study [S. Tripathi and J. J. Portman, Proc. Natl. Acad. Sci. U.S.A. 106, 2104 (2009)] to investigate how relatively minor structural changes of the meta-stable states can significantly influence the conformational flexibility and allosteric transition mechanism. We use the allosteric transitions of the domains of calmodulin as an example system to highlight the relationship between the transition mechanism and the inter-residue contacts present in the meta-stable states. In particular, we focus on the origin of transient local unfolding (cracking), a mechanism that can lower free energy barriers of allosteric transitions, in terms of the inter-residue contacts of the meta-stable states and the pattern of local strain that develops during the transition. We find that the magnitude of the local strain in the protein is not the sole factor determining whether a region will ultimately crack during the transition. These results emphasize that the residue interactions found exclusively in one of the two meta-stable states is the key in understanding the mechanism of allosteric conformational change.

Comparative Analysis of Threshold and Tessellation Methods for Determining Protein Contacts

The 3D structure of a protein is the main physical support of a protein's biological function; 3D protein folds are primarily maintained through interactions between amino acids. Inter-residue contacts are essential for the stability of protein folds. Therefore, many methodologies in the fields of structure analysis, structure prediction, and structure-function relationships are based on residue contacts. The present study provides a comparative analysis of two approaches for determining contacts: the classical distance-threshold method and an application of Laguerre, or weighted Voronoi tessellation. First, we examined mean contact distributions and their dependence on residue volumes, accessibility and hydrophobicity. In general, the different methods gave concordant results, although the method based on Cα distances showed significant discrepancies with the all-atom tessellation method. We also analyzed preferential contacts between all amino acid species and studied the influence of protein chain length, the proximity of the residues along the sequence, and the secondary structure environment. Interestingly, the discrepancies between methods were occasionally large enough to substantially change the relative preferences of some contacts. Finally, a case study on disulfide bridges demonstrated the importance of the structural environment in determining contacts from tessellation. In conclusion, the tessellation method is more accurate because of its fine adaptation to local protein topology, with far-reaching implications for most contact-based prediction methods of protein folding.

Sequence and structural analysis of two designed proteins with 88% identity adopting different folds

Protein folding is a natural phenomenon by which a sequence of amino acids folds into a unique functional three-dimensional structure. Although the sequence code that governs folding remains a mystery, one can identify key inter-residue contacts responsible for a given topology. In nature, there are many pairs of proteins of a given length that share little or no sequence identity. Similarly, there are many proteins that share a common topology but lack significant evidence of homology. In order to tackle this problem, protein engineering studies have been used to determine the minimal number of amino acid residues that codes for a particular fold. In recent years, the coupling of theoretical models and experiments in the study of protein folding has resulted in providing some fruitful clues. He et al. have designed two proteins with 88% sequence identity, which adopt different folds and functions. In this work, we have systematically analysed these two proteins by performing pentapeptide search, secondary structure predictions, variation in inter-residue interactions and residue-residue pair preferences, surrounding hydrophobicity computations, conformational switching and energy computations. We conclude that the local secondary structural preference of the two designed proteins at the Nand C-terminal ends to adopt either coil or strand conformation may be a crucial factor in adopting the different folds. Early on during the process of folding, both proteins may choose different energetically favourable pathways to attain the different folds.

Using Entropy Maximization to Understand the Determinants of Structural Dynamics beyond Native Contact Topology

Comparison of elastic network model predictions with experimental data has provided important insights on the dominant role of the network of inter-residue contacts in defining the global dynamics of proteins. Most of these studies have focused on interpreting the mean-square fluctuations of residues, or deriving the most collective, or softest, modes of motions that are known to be insensitive to structural and energetic details. However, with increasing structural data, we are in a position to perform a more critical assessment of the structure-dynamics relations in proteins, and gain a deeper understanding of the major determinants of not only the mean-square fluctuations and lowest frequency modes, but the covariance or the cross-correlations between residue fluctuations and the shapes of higher modes. A systematic study of a large set of NMR-determined proteins is analyzed using a novel method based on entropy maximization to demonstrate that the next level of refinement in the elastic network model description of proteins ought to take into consideration properties such as contact order (or sequential separation between contacting residues) and the secondary structure types of the interacting residues, whereas the types of amino acids do not play a critical role. Most importantly, an optimal description of observed cross-correlations requires the inclusion of destabilizing, as opposed to exclusively stabilizing, interactions, stipulating the functional significance of local frustration in imparting native-like dynamics. This study provides us with a deeper understanding of the structural basis of experimentally observed behavior, and opens the way to the development of more accurate models for exploring protein dynamics.

FoldGPCR: structure prediction protocol for the transmembrane domain of G protein-coupled receptors from class A.

Building reliable structural models of G protein-coupled receptors (GPCRs) is a difficult task because of the paucity of suitable templates, low sequence identity, and the wide variety of ligand specificities within the superfamily. Template-based modeling is known to be the most successful method for protein structure prediction. However, refinement of homology models within 1-3 A C alpha RMSD of the native structure remains a major challenge. Here, we address this problem by developing a novel protocol (foldGPCR) for modeling the transmembrane (TM) region of GPCRs in complex with a ligand, aimed to accurately model the structural divergence between the template and target in the TM helices. The protocol is based on predicted conserved inter-residue contacts between the template and target, and exploits an all-atom implicit membrane force field. The placement of the ligand in the binding pocket is guided by biochemical data. The foldGPCR protocol is implemented by a stepwise hierarchical approach, in which the TM helical bundle and the ligand are assembled by simulated annealing trials in the first step, and the receptor-ligand complex is refined with replica exchange sampling in the second step. The protocol is applied to model the human beta(2)-adrenergic receptor (beta(2)AR) bound to carazolol, using contacts derived from the template structure of bovine rhodopsin. Comparison with the X-ray crystal structure of the beta(2)AR shows that our protocol is particularly successful in accurately capturing helix backbone irregularities and helix-helix packing interactions that distinguish rhodopsin from beta(2)AR.

Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) adopt heterogeneous ensembles of conformations under physiological conditions. Understanding the relationship between amino acid sequence and conformational ensembles of IDPs can help clarify the role of disorder in physiological function. Recent studies revealed that polar IDPs favor collapsed ensembles in water despite the absence of hydrophobic groups--a result that holds for polypeptide backbones as well. By studying highly charged polypeptides, a different archetype of IDPs, we assess how charge content modulates the intrinsic preference of polypeptide backbones for collapsed structures. We characterized conformational ensembles for a set of protamines in aqueous milieus using molecular simulations and fluorescence measurements. Protamines are arginine-rich IDPs involved in the condensation of chromatin during spermatogenesis. Simulations based on the ABSINTH implicit solvation model predict the existence of a globule-to-coil transition, with net charge per residue serving as the discriminating order parameter. The transition is supported by quantitative agreement between simulation and experiment. Local conformational preferences partially explain the observed trends of polymeric properties. Our results lead to the proposal of a schematic protein phase diagram that should enable prediction of polymeric attributes for IDP conformational ensembles using easily calculated physicochemical properties of amino acid sequences. Although sequence composition allows the prediction of polymeric properties, interresidue contact preferences of protamines with similar polymeric attributes suggest that certain details of conformational ensembles depend on the sequence. This provides a plausible mechanism for specificity in the functions of IDPs.

Substrate-mediated Stabilization of a Tetrameric Drug Target Reveals Achilles Heel in Anthrax

Bacillus anthracis is a gram-positive spore-forming bacterium that causes anthrax. With the increased threat of anthrax in biowarfare, there is an urgent need to characterize new antimicrobial targets from B. anthracis. One such target is dihydrodipicolinate synthase (DHDPS), which catalyzes the committed step in the pathway yielding meso-diaminopimelate and lysine. In this study, we employed CD spectroscopy to demonstrate that the thermostability of DHDPS from B. anthracis (Ba-DHDPS) is significantly enhanced in the presence of the substrate, pyruvate. Analytical ultracentrifugation studies show that the tetramer-dimer dissociation constant of the enzyme is 3-fold tighter in the presence of pyruvate compared with the apo form. To examine the significance of this substrate-mediated stabilization phenomenon, a dimeric mutant of Ba-DHDPS (L170E/G191E) was generated and shown to have markedly reduced activity compared with the wild-type tetramer. This demonstrates that the substrate, pyruvate, stabilizes the active form of the enzyme. We next determined the high resolution (2.15 A) crystal structure of Ba-DHDPS in complex with pyruvate (3HIJ) and compared this to the apo structure (1XL9). Structural analyses show that there is a significant (91 A(2)) increase in buried surface area at the tetramerization interface of the pyruvate-bound structure. This study describes a new mechanism for stabilization of the active oligomeric form of an antibiotic target from B. anthracis and reveals an "Achilles heel" that can be exploited in structure-based drug design.

Graphical Causal Modeling of Protein Structural and Dynamical Features

The causal relationship between protein structural features and conformational dynamics is difficult to isolate experimentally because even seemingly small perturbations, such as point mutations, can simultaneously alter many physical properties of proteins. Using molecular dynamics simulation trajectories for a series of point mutations at a single solvent-exposed position on the protein GB1, for which experimental NMR spin relaxation data also are available [1], effects of various types of inter-residue interactions are isolated via a graph-theory-based approach to causal modeling [2] previously applied in the biological sciences mainly to functional MRI studies [3] and genomics [4]. This approach produces directed acyclic graphs (DAGs) in which protein structural features such as hydrogen bonds, inter-residue contacts, and order parameters are encoded as nodes; the presence of an edge in the graph implies a causal relationship between features and the directionality of the edge implies the direction of causation.[1] Mayer, KL et al. (2003). Nat. Struct. Biol. 10: 962-965.[2] Pearl, J. (2000). Cambridge University Press, London; Pearl, J. (2009). Statistics Surveys. 3:96-146; Spirtes et al. (2000). MIT Press, Cambridge.[3] Eichler, M. (2005). Phil. Trans. R. Soc. B. 360(1457): 953-967.[4] Maathius, MH et al. (2009). Ann. Stat. 37(6A): 3133-3164.

Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers

BackgroundPrediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions.ResultsIn this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC.ConclusionsClassifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy.

Refinement of the long‐range order parameter in predicting folding rates of two‐state proteins

Long-range order (LRO) is one of the most successful descriptors in relating the three-dimensional structures of proteins with their folding rates. LRO highlights the importance of long-range contacts (residues that are far in sequence and closer in the 3D structure) in determining the folding rates of proteins across all structural classes of proteins. In this work, we have updated the data set of two-state folding proteins to examine the robustness of LRO parameter and to assess whether any refinements are required in defining the computation of LRO. LRO shows a better correlation (r = -0.85) for the increased dataset with a very small difference in distance cut-off compared to the old data set and reinforces the robustness of the parameter. When the dataset was grouped into three major structural classes, slight refinement of the parameter (distance of separation in space and sequence) gave better correlations. The corresponding correlation for the three structural classes are r = -0.92; sequence separation 23; spatial distance cut-off 5.5 A for all alpha structural class, r = -0.84; sequence separation 43; spatial distance cut-off 7 A for all beta structural class and r = -0.82; sequence separation 8; spatial distance cut-off 8 A for mixed class proteins. It is envisaged that during the process of protein folding, formation of long-range contacts beyond the above sequence separation limits may play a key role in determining the folding rates of proteins, and this aspect is discussed in the light of experimental studies on the formation of interresidue contacts and end-to-end loops in unfolded polypeptide chains.

The N Terminus of Connexin37 Contains an α-Helix That Is Required for Channel Function

The cytoplasmic N-terminal domain of connexins has been implicated in multiple aspects of gap junction function, including connexin trafficking/assembly and channel gating. A synthetic peptide corresponding to the first 23 amino acids of human connexin37 was prepared, and circular dichroism and nuclear magnetic resonance studies showed that this N-terminal peptide was predominantly alpha-helical between glycine 5 and glutamate 16. The importance of this structure for localization of the protein at appositional membranes and channel function was tested by expression of site-directed mutants of connexin37 in which amino acids leucine 10 and glutamine 15 were replaced with prolines or alanines. Wild type connexin37 and both substitution mutants localized to appositional membranes between transfected HeLa cells. The proline mutant did not allow intercellular transfer of microinjected neurobiotin; the alanine mutant allowed transfer, but less extensively than wild type connexin37. When expressed alone in Xenopus oocytes, wild type connexin37 produced hemichannel currents, but neither of the double substitution mutants produced detectable currents. The proline mutant (but not the alanine mutant) inhibited co-expressed wild type connexin37. Taken together, our data suggest that the alpha-helical structure of the connexin37 N terminus may be dispensable for protein localization, but it is required for channel and hemichannel function.

Universal partitioning of the hierarchical fold network of 50-residue segments in proteins

BackgroundSeveral studies have demonstrated that protein fold space is structured hierarchically and that power-law statistics are satisfied in relation between the numbers of protein families and protein folds (or superfamilies). We examined the internal structure and statistics in the fold space of 50 amino-acid residue segments taken from various protein folds. We used inter-residue contact patterns to measure the tertiary structural similarity among segments. Using this similarity measure, the segments were classified into a number (Kc) of clusters. We examined various Kc values for the clustering. The special resolution to differentiate the segment tertiary structures increases with increasing Kc. Furthermore, we constructed networks by linking structurally similar clusters.ResultsThe network was partitioned persistently into four regions for Kc ≥ 1000. This main partitioning is consistent with results of earlier studies, where similar partitioning was reported in classifying protein domain structures. Furthermore, the network was partitioned naturally into several dozens of sub-networks (i.e., communities). Therefore, intra-sub-network clusters were mutually connected with numerous links, although inter-sub-network ones were rarely done with few links. For Kc ≥ 1000, the major sub-networks were about 40; the contents of the major sub-networks were conserved. This sub-partitioning is a novel finding, suggesting that the network is structured hierarchically: Segments construct a cluster, clusters form a sub-network, and sub-networks constitute a region. Additionally, the network was characterized by non-power-law statistics, which is also a novel finding.ConclusionMain findings are: (1) The universe of 50 residue segments found here was characterized by non-power-law statistics. Therefore, the universe differs from those ever reported for the protein domains. (2) The 50-residue segments were partitioned persistently and universally into some dozens (ca. 40) of major sub-networks, irrespective of the number of clusters. (3) These major sub-networks encompassed 90% of all segments. Consequently, the protein tertiary structure is constructed using the dozens of elements (sub-networks).

Improving consensus contact prediction via server correlation reduction

BackgroundProtein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them.ResultsIn this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively.ConclusionReducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Investigating the Origins of Fractional ψ-values in Protein Folding Transition States

ψ-analysis has been used to characterize the inter-residue contacts that define the structure of the transition state ensemble (TSE) for three protein systems. ψ-values identify the degree to which an engineered bi-Histidine metal ion binding site is formed in the TSE. Values of zero or one indicate that the site is fully unfolded-like or native-like, respectively, while fractional values reflect a partial recovery of the binding-induced stabilization in the TSE. This method has been applied to three proteins, protein A (Baxa et al., 2008), Ubiquitin (Ub) (Krantz et al., 2004), and acyl-phosphotase (Pandit et al., 2006). In all three cases, the TSE captures ∼70% of the respective native-state topology, as quantified by the relative contact order (RCO) metric. In light of the proposed “70% rule”, a re-evaluation of the TSE of many small proteins, especially those characterized as polarized by mutational f-analysis, must be considered. While this “70% rule” is believed to be a general feature of most small proteins, the potential origin of fractional ψ-values remains to be investigated. All-atom Langevin dynamics (LD) simulations of TS models of Ub are performed with distance constraints on residue pairs having experimentally-determined ψ-values of unity. An analysis of the trajectories indicates that the fractional ψ-values of sites adjacent to unity sites tend to reflect distorted site geometries, while the residues for more distal, fractional values indicate that the sites are able to sample configurations where they are unfolded-like. Nevertheless, the simulations indicate that the unity ψ-values alone are sufficient to generate a TSE with a highly native-like topology. Furthermore, the calculation of f-values based on sidechain-sidechain contacts made in the TSE indicate that experimental f-values can dramatically under-report the amount of structure present even for highly buried residues.

ψ-Constrained Simulations of Protein Folding Transition States: Implications for Calculating ϕ

ψ-analysis has been used to identify interresidue contacts in the transition state ensemble (TSE) of ubiquitin and other proteins. The magnitude of ψ depends on the degree to which an inserted bihistidine (biHis) metal ion binding site is formed in the TSE. A ψ equal to zero or one indicates that the biHis site is absent or fully native-like, respectively, while a fractional ψ implies that in the TSE, the biHis site recovers only part of the binding-induced stabilization of the native state. All-atom Langevin dynamics simulations of the TSE are performed with restrictions imposed only on the distances between the pairs of residues with experimentally determined ψ of unity. When a site with a fractional ψ lies adjacent to a site with ψ = 1, the fractional ψ generally signifies that the “fractional site” has a distorted geometry in the TSE. When a fractional site is distal to the sites with ψ = 1, however, the histidines sample configurations in which the site is absent. The simulations indicate that the ψ = 1 sites by themselves can be used to generate a well-defined TSE having near-native topology. ϕ values calculated from the TS simulations exhibit mixed agreement with the experimental values. The origin and implication of the disparities are discussed.

Inferring Boundary Information of Discontinuous-Domain Proteins

Wetlaufer introduced the classification of domains into continuous and discontinuous. Continuous domains form from a single-chain segment and discontinuous domains are composed of two or more chain segments. Richardson identified approximately 100 domains in her review. Her assignment was based on the concepts that the domain would be independently stable and/or could undergo rigid-body-like movements with respect to the entire protein. There are now several instances where structurally similar domains occur in different proteins in the absence of noticeable sequence similarity. Possibly, the most notable of such domains is the trios-phosphate isomerase (TIM) barrel. With the increase in the number of known sequences, computer algorithms are required to identify the discontinuous domain of an unknown protein chain in order to determine its structure and function. We have developed a novel algorithm for discontinuous-domain boundary prediction based on a machine learning algorithm and interresidue contact interactions values. We have used 415 proteins, including 100 discontinuous-domain chains for training. There is no method available that is designed solely on a sequence based for the prediction of discontinuous domain. DomainDiscovery performed significantly well compared to the structure-based methods like structural classification of proteins (SCOP), class, architecture, topology and homologous superfamily (CATH), and DOMain MAKer (DOMAK).