126 research outputs found
Data from: Characterization of C-ring component assembly in flagellar motors from amino acid coevolution
No full textBacterial flagellar motility, an important virulence factor, is energized by a rotary motor localized within the flagellar basal body. The rotor module consists of a large framework (C-ring), composed of the FliG, FliM and FliN proteins. FliN and FliM contacts the FliG torque ring to control the direction of flagellar rotation. We report that structure-based models constrained only by residue coevolution can recover the binding interface of atomic X-ray dimer complexes with remarkable accuracy (ca. 1 Å RMSD). We propose a model for FliM-FliN heterodimerization, which agrees accurately with homologous interfaces as well as in-situ cross-linking experiments, and hence supports a proposed architecture for the lower portion of the C-ring. Furthermore, this approach allowed the identification of two discrete and interchangeable homodimerization interfaces between FliM middle domains that agree with experimental measurements and might be associated with C-ring directional switching dynamics triggered upon binding of CheY signal protein. Our findings provide structural details of complex formation at the C-ring that have been difficult to obtain with previous methodologies and clarify the architectural principle that underpins the ultra-sensitive allostery exhibited by this ring assembly that controls the clockwise (CW) or counterclockwise (CCW) rotation of flagella
Sequence co-evolutionary information is a natural partner to minimally-frustrated models of biomolecular dynamics [version 1; referees: 3 approved]
No full textExperimentally derived structural constraints have been crucial to the implementation of computational models of biomolecular dynamics. For example, not only does crystallography provide essential starting points for molecular simulations but also high-resolution structures permit for parameterization of simplified models. Since the energy landscapes for proteins and other biomolecules have been shown to be minimally frustrated and therefore funneled, these structure-based models have played a major role in understanding the mechanisms governing folding and many functions of these systems. Structural information, however, may be limited in many interesting cases. Recently, the statistical analysis of residue co-evolution in families of protein sequences has provided a complementary method of discovering residue-residue contact interactions involved in functional configurations. These functional configurations are often transient and difficult to capture experimentally. Thus, co-evolutionary information can be merged with that available for experimentally characterized low free-energy structures, in order to more fully capture the true underlying biomolecular energy landscape
Dimeric interactions and complex formation using direct coevolutionary couplings
No full textWe develop a procedure to characterize the association of protein structures into homodimers using coevolutionary couplings extracted from Direct Coupling Analysis (DCA) in combination with Structure Based Models (SBM). Identification of dimerization contacts using DCA is more challenging than intradomain contacts since direct couplings are mixed with monomeric contacts. Therefore a systematic way to extract dimerization signals has been elusive. We provide evidence that the prediction of homodimeric complexes is possible with high accuracy for all the cases we studied which have rich sequence information. For the most accurate conformations of the structurally diverse dimeric complexes studied the mean and interfacial RMSDs are 1.95Å and 1.44Å, respectively. This methodology is also able to identify distinct dimerization conformations as for the case of the family of response regulators, which dimerize upon activation. The identification of dimeric complexes can provide interesting molecular insights in the construction of large oligomeric complexes and be useful in the study of aggregation related diseases like Alzheimer’s or Parkinson’s
Revealing protein networks and gene-drug connectivity in cancer from direct information
No full textAbstractThe connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.</jats:p
Engineering Repressors with Coevolutionary Cues Facilitates Toggle Switches with a Master Reset
No full textSupplementary material is included.Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.University of Texas System Rising STARs Program [802-1053-T000674F], Welch Foundation [Grant # BP-0037].School of Natural Sciences and MathematicsErik Jonsson School of Engineering and Computer ScienceCenter for Systems Biolog
Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients With Type 2 Diabetes Mellitus
No full textThe treatment of Type 2 Diabetes Mellitus (T2DM) consists primarily of oral antidiabetic drugs (OADs) that stimulate insulin secretion, such as sulfonylureas (SUs) and reduce hepatic glucose production (e.g., biguanides), among others. The marked inter-individual differences among T2DM patients’ response to these drugs have become an issue on prescribing and dosing efficiently. In this study, fourteen polymorphisms selected from Genome-wide association studies (GWAS) were screened in 495 T2DM Mexican patients previously treated with OADs to find the relationship between the presence of these polymorphisms and response to the OADs. Then, a novel association screening method, based on global probabilities, was used to globally characterize important relationships between the drug response to OADs and genetic and clinical parameters, including polymorphisms, patient information, and type of treatment. Two polymorphisms, ABCC8-Ala1369Ser and KCNJ11-Glu23Lys, showed a significant impact on response to SUs. Heterozygous ABCC8-Ala1369Ser variant (A/C) carriers exhibited a higher response to SUs compared to homozygous ABCC8-Ala1369Ser variant (A/A) carriers (p-value = 0.029) and to homozygous wild-type genotypes (C/C) (p-value = 0.012). The homozygous KCNJ11-Glu23Lys variant (C/C) and wild-type (T/T) genotypes had a lower response to SUs compared to heterozygous (C/T) carriers (p-value = 0.039). The screening of OADs response related genetic and clinical factors could help improve the prescribing and dosing of OADs for T2DM patients and thus contribute to the design of personalized treatments
Information Theory in Molecular Evolution: From Models to Structures and Dynamics
No full textThis Special Issue collects novel contributions from scientists in the interdisciplinary field of biomolecular evolution. Works listed here use information theoretical concepts as a core but are tightly integrated with the study of molecular processes. Applications include the analysis of phylogenetic signals to elucidate biomolecular structure and function, the study and quantification of structural dynamics and allostery, as well as models of molecular interaction specificity inspired by evolutionary cues
Unraveling and Designing Biomolecular Interactions Using Direct Couplings From Global Probabilistic Models
No full textCoevolution plays a fundamental role in determining folding, structure, interactions and
functionality of proteins. Structural or functional related residues coevolve during the
evolutionary history to maintain similar structures, interactions and functional properties among
the same protein families. Direct coupling models for coevolutionary analysis have demonstrated
outstanding performances in predicting contacting residues of proteins and thereby have
commonly used to predict protein structures and interactions. In this dissertation, a global
statistical inference framework, direct coupling analysis (DCA) was used to infer coevolutionary
couplings in datasets including protein-protein interactions and protein modular-modular
compatibility. The couplings were then used to build different computational models, i.e. a
Hamiltonian energy function H(S) and compatibility score C(S). The Hamiltonian energy
function successfully predicts the specificity strength of protein-protein interactions in two
component systems and proposed a novel cross-talk model between different sets of twocomponent system (TCS), VanRS and CroRS, to explain strain specific antibiotic phenotypes in
Enterococcus faecalis. On the other hand, the C(S) score predicts the compatibility between two
protein subdomains in terms of allosteric communication and function between DNA-binding and ligand-binding modules originated from different proteins in the LacI protein family. This
model facilitates screening out functional hybrids from different LacI homologs used to engineer
and rewire the connection between signal sensing and genetic output. The compatibility score is
also able to predict the mutational effect for hybrid proteins, aiming to improve the functionality
of a hybrid protein. Moreover, the application of DCA framework was extended into nonsequence datasets, including pharmacogenomics and clinicopathological data, for the first time.
The study has demonstrated that direct coupling approach could capture important connectivities
between gene mutations and drug responses, as well as between different clinicopathological
features. The direct coupling approach provides new means in pharmacogenomics and
clinicopathological data analysis and thereby offers new insights in personalized medicine
Global Pairwise RNA Interaction Landscapes Reveal Core Features of Protein Recognition
No full textIncludes supplementary materialRNA-protein interactions permeate biology. Transcription, translation, and splicing all hinge on the recognition of structured RNA elements by RNA-binding proteins. Models of RNA-protein interactions are generally limited to short linear motifs and structures because of the vast sequence sampling required to access longer elements. Here, we develop an integrated approach that calculates global pairwise interaction scores from in vitro selection and high-throughput sequencing. We examine four RNA-binding proteins of phage, viral, and human origin. Our approach reveals regulatory motifs, discriminates between regulated and non-regulated RNAs within their native genomic context, and correctly predicts the consequence of mutational events on binding activity. We design binding elements that improve binding activity in cells and infer mutational pathways that reveal permissive versus disruptive evolutionary trajectories between regulated motifs. These coupling landscapes are broadly applicable for the discovery and characterization of protein-RNA recognition at single nucleotide resolution.National Institutes of Health grant R01NS100788.School of Natural Sciences and MathematicsCenter for Systems Biolog
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