2,850 research outputs found

    Entropy-driven genome organization

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    DNA and RNA polymerases active on bacterial and human genomes in the crowded environment of a cell are modeled as beads spaced along a string. Aggregation of the large polymerizing complexes increases the entropy of the system through an increase in entropy of the many small crowding molecules; this occurs despite the entropic costs of looping the intervening DNA. Results of a quantitative cost/benefit analysis are consistent with observations that active polymerases cluster into replication and transcription “factories” in both pro- and eukaryotes. We conclude that the second law of thermodynamics acts through nonspecific entropic forces between engaged polymerases to drive the self-organization of genomes into loops containing several thousands (and sometimes millions) of basepairs

    Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

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    Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics

    Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

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    A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

    Nanoporous silica-based protocells at multiple scales for designs of life and nanomedicine.

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    Various protocell models have been constructed de novo with the bottom-up approach. Here we describe a silica-based protocell composed of a nanoporous amorphous silica core encapsulated within a lipid bilayer built by self-assembly that provides for independent definition of cell interior and the surface membrane. In this review, we will first describe the essential features of this architecture and then summarize the current development of silica-based protocells at both micro- and nanoscale with diverse functionalities. As the structure of the silica is relatively static, silica-core protocells do not have the ability to change shape, but their interior structure provides a highly crowded and, in some cases, authentic scaffold upon which biomolecular components and systems could be reconstituted. In basic research, the larger protocells based on precise silica replicas of cells could be developed into geometrically realistic bioreactor platforms to enable cellular functions like coupled biochemical reactions, while in translational research smaller protocells based on mesoporous silica nanoparticles are being developed for targeted nanomedicine. Ultimately we see two different motivations for protocell research and development: (1) to emulate life in order to understand it; and (2) to use biomimicry to engineer desired cellular interactions

    ModĂ©lisation mĂ©tabolique Ă  l’échelle du gĂ©nome de la bactĂ©rie quasi-minimale Mesoplasma florum

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    Des avancĂ©es significatives au niveau de la synthĂšse et de l’assemblage de fragments d’acide dĂ©soxyribonuclĂ©ique (ADN), le support physique des fonctions cellulaires encodĂ©es dans une cellule vivante, permettent maintenant la construction de gĂ©nomes entiers. Ce progrĂšs permet d’imaginer que la conception d’organismes synthĂ©tiques deviendra routiniĂšre au cours des prochaines annĂ©es. Cette capacitĂ© promet de transformer radicalement le domaine de la biologie en formant une nouvelle discipline d’ingĂ©nierie biologique. Parmi les retombĂ©es anticipĂ©es, on note le remplacement de synthĂšses chimiques par des procĂ©dĂ©s biologiques renouvelables tels que la production de biocarburants, la synthĂšse de mĂ©dicaments microbiens, ou des approches alternatives pour le traitement des maladies. Dans ce contexte, il devient particuliĂšrement important d’arriver Ă  prĂ©dire correctement le phĂ©notype rĂ©sultant des gĂ©nomes qui seront gĂ©nĂ©rĂ©s. Pour y arriver, il convient de rĂ©duire la complexitĂ© biologique en travaillant d’abord avec les cellules les plus simples possibles. Ce type d’organisme ayant subi un processus de rĂ©duction de gĂ©nome et dont la majoritĂ© des gĂšnes sont essentiels afin de survivre en conditions dĂ©finies se nomme une cellule minimale. Le groupe phylogĂ©nĂ©tique des mollicutes, bactĂ©ries dĂ©pourvues de paroi cellulaire, contient les espĂšces vivant avec les plus petits gĂ©nomes connus Ă  ce jour. Membre de ce groupe, le pathogĂšne humain Mycoplasma genitalium possĂšde le plus petit gĂ©nome capable de croissance autonome (560kbp codant pour 482 protĂ©ines. Cependant, sa pathogĂ©nicitĂ© et sa vitesse de croissance rĂ©duite (~24h) limitent l’applicabilitĂ© de M. genitalium en biologie synthĂ©tique. Pour remĂ©dier Ă  ce problĂšme, notre laboratoire a choisi de travailler avec Mesoplasma florum dont le temps de doublement est trĂšs rapide (~32 min) et qui ne cause pas de maladies chez l’humain. Les travaux effectuĂ©s chez M. florum permettent maintenant le clonage et la transplantation de son gĂ©nome et des travaux rĂ©cents ont permis de caractĂ©riser les propriĂ©tĂ©s physico-chimiques de sa cellule ainsi que plusieurs paramĂštres biologiques. Afin de permettre la conception de gĂ©nomes synthĂ©tiques basĂ©s sur M. florum, il convient d’intĂ©grer un maximum de connaissances dans un cadre informatique structurĂ© capable de gĂ©nĂ©rer des prĂ©dictions phĂ©notypiques. Un modĂšle mĂ©tabolique Ă  l’échelle du gĂ©nome (GEM) reposant sur la mĂ©thode d’analyse des flux Ă  l’équilibre (FBA) reprĂ©sente un format particuliĂšrement intĂ©ressant pour initier ces travaux de biologie des systĂšmes. La qualitĂ© des prĂ©dictions gĂ©nĂ©rĂ©es par ce type de modĂšle est dĂ©pendante de la prĂ©cision de l’objectif Ă  atteindre. Pour simuler la croissance, les GEMs doivent satisfaire un objectif nommĂ© “fonction objective de biomasse” (BOF) qui contient l’ensemble des mĂ©tabolites nĂ©cessaires Ă  la production d’une nouvelle cellule avec des coefficients stƓchiomĂ©triques reprĂ©sentatifs de l’abondance de ces composantes dans la cellule. Pendant mon parcours de doctorat, j’ai dĂ©veloppĂ© le logiciel BOFdat qui permet la dĂ©finition d’une BOF reprĂ©sentative de la composition cellulaire spĂ©cifique Ă  une espĂšce avec les donnĂ©es expĂ©rimentales associĂ©es. Les deux premiĂšres des trois Ă©tapes de BOFdat dĂ©terminent les coefficients stoechiomĂ©triques de molĂ©cules connues pour faire partie de la composition cellulaire telles que les macromolĂ©cules principales (Ă©tape 1, ADN, ARN et protĂ©ines) et les coenzymes essentiels (Ă©tape 2). L’étape 3 de BOFdat propose une mĂ©thode non-biaisĂ©e pour dĂ©terminer les mĂ©tabolites susceptibles d’amĂ©liorer la prĂ©diction d’essentialitĂ© des gĂšnes formulĂ©e par le modĂšle. Pour ce faire, un algorithme gĂ©nĂ©tique maximise la composition de la biomasse en fonction des donnĂ©es d’essentialitĂ© expĂ©rimentales Ă  l’échelle du gĂ©nome. BOFdat a Ă©tĂ© validĂ© en reconstruisant la BOF du modĂšle iML1515 de la bactĂ©rie modĂšle Escherichia coli. L’utilisation de BOFdat a permis de rĂ©capituler le taux de croissance prĂ©dit avec la BOF originale tout en amĂ©liorant la qualitĂ© des prĂ©dictions d’essentialitĂ© de gĂšnes de iML1515. BOFdat est disponible en libre accĂšs pour quiconque dĂ©sire construire une BOF pour un modĂšle mĂ©tabolique. Ensuite, un GEM nommĂ© iJL208 a Ă©tĂ© produit et contient 208 des 676 protĂ©ines reprĂ©sentant l’ensemble du mĂ©tabolisme de M. florum. La qualitĂ© de l’annotation du gĂ©nome a d’abord Ă©tĂ© Ă©valuĂ©e en intĂ©grant l’information obtenue par trois approches bio-informatiques, rĂ©vĂ©lant que la majoritĂ© des protĂ©ines (418/676) ont une qualitĂ© suffisante pour ĂȘtre incorporĂ©es dans le modĂšle. Ensuite, les rĂ©actions ont Ă©tĂ© identifiĂ©es et rigoureusement incorporĂ©es une Ă  la fois afin de construire le rĂ©seau mĂ©tabolique de cette bactĂ©rie quasi-minimale. L’étude de la carte mĂ©tabolique reconstruite rĂ©vĂšle une dĂ©pendance prononcĂ©e pour l’import de composantes Ă  partir du milieu de culture ainsi que l’importance des mĂ©canismes de recyclage des mĂ©tabolites. Pour sa production d’énergie, M. florum est entiĂšrement dĂ©pendante de la glycolyse et ne possĂšde pas la machinerie nĂ©cessaire Ă  la respiration cellulaire. L’élaboration d’un milieu de culture semi-dĂ©fini a rĂ©duit la prĂ©sence de sucres contaminants dans le milieu de culture initial et ainsi de distinguer la croissance avec ou sans supplĂ©mentation de sucrose. Cette avancĂ©e importante a permis de mesurer les taux d’assimilation de sucrose et de production des dĂ©chets mĂ©taboliques lactate et acĂ©tate. Ces paramĂštres ont Ă©tĂ© utilisĂ©s afin de contraindre le modĂšle et de mieux comprendre la sensibilitĂ© du modĂšle Ă  une variĂ©tĂ© de paramĂštres. Aussi, la croissance de M. florum a pu ĂȘtre validĂ©e expĂ©rimentalement avec diffĂ©rents sucres. L’information contextuelle obtenue, combinĂ©e Ă  une analyse de structures tridimensionnelles de protĂ©ines clĂ©s, a permis de suggĂ©rer des hypothĂšses crĂ©dibles supportant l’assimilation de ces sucres par M. florum. Finalement, iJL208 a Ă©tĂ© utilisĂ© afin de formuler une prĂ©diction de gĂ©nome minimal pour M. florum en simulant itĂ©rativement de larges dĂ©lĂ©tions dans son gĂ©nome. Combiner l’intĂ©gration de donnĂ©es expĂ©rimentales avec les prĂ©dictions du modĂšle constitue une voie d’avenir pour la conception de gĂ©nomes synthĂ©tiques qui rejoint les capacitĂ©s techniques d’assemblage de chromosomes en biologie synthĂ©tique. Globalement, les projets rĂ©alisĂ©s au cours de mon doctorat contribuent Ă  l’avancement de la biologie des systĂšmes chez M. florum dans le but de prĂ©dire efficacement les phĂ©notypes de la souche naturelle et de variants synthĂ©tiques qui pourront ĂȘtre produits au cours des prochaines annĂ©es

    Chlamydia trachomatis protein CT009 is a structural and functional homolog to the key morphogenesis component RodZ and interacts with division septal plane localized MreB

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    This is the peer reviewed version of the following article: Kemege, K. E., Hickey, J. M., Barta, M. L., Wickstrum, J., Balwalli, N., Lovell, S., Battaile, K. P. and Hefty, P. S. (2015), Chlamydia trachomatis protein CT009 is a structural and functional homolog to the key morphogenesis component RodZ and interacts with division septal plane localized MreB. Molecular Microbiology, 95: 365–382. doi:10.1111/mmi.12855, which has been published in final form at http://doi.org/10.1111/mmi.12855. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Cell division in Chlamydiae is poorly understood as apparent homologs to most conserved bacterial cell division proteins are lacking and presence of elongation (rod shape) associated proteins indicate non-canonical mechanisms may be employed. The rod-shape determining protein MreB has been proposed as playing a unique role in chlamydial cell division. In other organisms, MreB is part of an elongation complex that requires RodZ for proper function. A recent study reported that the protein encoded by ORF CT009 interacts with MreB despite low sequence similarity to RodZ. The studies herein expand on those observations through protein structure, mutagenesis, and cellular localization analyses. Structural analysis indicated that CT009 shares high level of structural similarity to RodZ, revealing the conserved orientation of two residues critical for MreB interaction. Substitutions eliminated MreB protein interaction and partial complementation provided by CT009 in RodZ deficient E. coli. Cellular localization analysis of CT009 showed uniform membrane staining in Chlamydia. This was in contrast to the localization of MreB, which was restricted to predicted septal planes. MreB localization to septal planes provides direct experimental observation for the role of MreB in cell division and supports the hypothesis that it serves as a functional replacement for FtsZ in Chlamydia

    Chromatography-free purification strategies for large biological macromolecular complexes involving fractionated PEG precipitation and density gradients

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    A complex interplay between several biological macromolecules maintains cellular homeostasis. Generally, the demanding chemical reactions which sustain life are not performed by individual macromolecules, but rather by several proteins that together form a macromolecular complex. Understanding the functional interactions amongst subunits of these macromolecular machines is fundamental to elucidate mechanisms by which they maintain homeostasis. As the faithful function of macromolecular complexes is essential for cell survival, their mis-function leads to the development of human diseases. Furthermore, detailed mechanistic nterrogation of the function of macromolecular machines can be exploited to develop and optimize biotechnological processes. The purification of intact macromolecular complexes is an essential prerequisite for this; however, chromatographic purification schemes can induce the dissociation of subunits or the disintegration of the whole complex. Here, we discuss the development and application of chromatography-free purification strategies based on fractionated PEG precipitation and orthogonal density gradient centrifugation that overcomes existing limitations of established chromatographic purification protocols. The presented case studies illustrate the capabilities of these procedures for the purification of macromolecular complexes
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