Dataset Information

Assessing local structural perturbations in proteins.

ABSTRACT: BACKGROUND: Protein structure research often deals with the comparison of two or more structures of the same protein, for instance when handling alternative structure models for the same protein, point mutants, molecule movements, structure predictions, etc. Often the difference between structures is small, restricted to a local neighborhood, and buried in structural "noise" due to trivial differences resulting from experimental artifacts. In such cases, whole-structure comparisons by means of structure superposition may be unsatisfactory and researchers have to perform a tedious process of manually superposing different segments individually and/or use different frames of reference, chosen roughly by educated guessing. RESULTS: We have developed an algorithm to compare local structural differences between alternative structures of the same protein. We have implemented the algorithm through a computer program that performs the numerical evaluation and allows inspecting visually the results of the structure comparison. We have tested the algorithm on different kinds of model systems. Here we present the algorithm and some results to illustrate its characteristics. CONCLUSION: This program may provide an insight into the local structural changes produced in a protein structure by different interactions or modifications. It is convenient for the general user and it can be applied to standard or specific tasks on protein structure research.

SUBMITTER: Lema MA

PROVIDER: S-EPMC1239910 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

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Assessing local structural perturbations in proteins.

Lema Martin A MA Echave Julian J

BMC bioinformatics 20050913

<h4>Background</h4>Protein structure research often deals with the comparison of two or more structures of the same protein, for instance when handling alternative structure models for the same protein, point mutants, molecule movements, structure predictions, etc. Often the difference between structures is small, restricted to a local neighborhood, and buried in structural "noise" due to trivial differences resulting from experimental artifacts. In such cases, whole-structure comparisons by mea ...[more]

PMID: 16159393

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Project description:The constant increase in the number of solved protein structures is of great help in understanding the basic principles behind protein folding and evolution. 3-D structural knowledge is valuable in designing and developing methods for comparison, modelling and prediction of protein structures. These approaches for structure analysis can be directly implicated in studying protein function and for drug design. The backbone of a protein structure favours certain local conformations which include ?-helices, ?-strands and turns. Libraries of limited number of local conformations (Structural Alphabets) were developed in the past to obtain a useful categorization of backbone conformation. Protein Block (PB) is one such Structural Alphabet that gave a reasonable structure approximation of 0.42 Å. In this study, we use PB description of local structures to analyse conformations that are preferred sites for structural variations and insertions, among group of related folds. This knowledge can be utilized in improving tools for structure comparison that work by analysing local structure similarities. Conformational differences between homologous proteins are known to occur often in the regions comprising turns and loops. Interestingly, these differences are found to have specific preferences depending upon the structural classes of proteins. Such class-specific preferences are mainly seen in the all-? class with changes involving short helical conformations and hairpin turns. A test carried out on a benchmark dataset also indicates that the use of knowledge on the class specific variations can improve the performance of a PB based structure comparison approach. The preference for the indel sites also seem to be confined to a few backbone conformations involving ?-turns and helix C-caps. These are mainly associated with short loops joining the regular secondary structures that mediate a reversal in the chain direction. Rare ?-turns of type I' and II' are also identified as preferred sites for insertions.

Project description:Signals created by local perturbations are known to propagate long distances through proteins via backbone connectivity and nonbonded interactions. In the current study, signal propagation from the flexible ligand binding loop to the rest of Protein Tyrosine Phosphatase 1B (PTP1B) was investigated using frequency response techniques. Using restrained Targeted Molecular Dynamics (TMD) potential on WPD and R loops, PTP1B was driven between its crystal structure conformations at different frequencies. Propagation of the local perturbation signal was manifested via peaks at the fundamental frequency and upper harmonics of 1/f distributed spectral density of atomic variables, such as Cα atoms, dihedral angles, or polar interaction distances. Frequency of perturbation was adjusted high enough (simulation length >∼10×period of a perturbation cycle) not to be clouded by random diffusional fluctuations, and low enough (<∼0.8 ns(-1)) not to attenuate the propagating signal and enhance the contribution of the side-chains to the dissipation of the signals. Employing Discrete Fourier Transform (DFT) to TMD simulation trajectories of 16 cycles of conformational transitions at periods of 1.2 to 5 ns yielded Cα displacements consistent with those obtained from crystal structures. Identification of the perturbed atomic variables by statistical t-tests on log-log scale spectral densities revealed the extent of signal propagation in PTP1B, while phase angles of the filtered trajectories at the fundamental frequency were used to cluster collectively fluctuating elements. Hydrophobic interactions were found to have a higher contribution to signal transduction between side-chains compared to the role of polar interactions. Most of in-phase fluctuating residues on the signaling pathway were found to have high identity among PTP domains, and located over a wide region of PTP1B including the allosteric site. Due to its simplicity and efficiency, the suggested technique may find wide applications in identification of signaling pathways of different proteins.

Project description:Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. These complementary approaches together result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer. We profiled the transcriptome of human cells expressing tumor virus proteins, in order to trace pathways through which viral proteins could alter cellular states. To examine transcriptome network perturbations directly in human cells, we generated expression constructs fusing each viral ORF (open reading frame) to a tandem epitope tag and introduced each construct into IMR-90 normal human diploid fibroblasts. Total RNA was isolated from IMR-90 cells expressing viORFs and gene expression was assayed on Human Gene 1.0 ST arrays.

Dataset Information

Assessing local structural perturbations in proteins.

Publications

Assessing local structural perturbations in proteins.

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