Entropy-driven genome organization

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.

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

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone

Cryo-EM structures of herpes simplex virus type 1 portal vertex and packaged genome.

Herpesviruses are enveloped viruses that are prevalent in the human population and are responsible for diverse pathologies, including cold sores, birth defects and cancers. They are characterized by a highly pressurized pseudo-icosahedral capsid-with triangulation number (T) equal to 16-encapsidating a tightly packed double-stranded DNA (dsDNA) genome1-3. A key process in the herpesvirus life cycle involves the recruitment of an ATP-driven terminase to a unique portal vertex to recognize, package and cleave concatemeric dsDNA, ultimately giving rise to a pressurized, genome-containing virion4,5. Although this process has been studied in dsDNA phages6-9-with which herpesviruses bear some similarities-a lack of high-resolution in situ structures of genome-packaging machinery has prevented the elucidation of how these multi-step reactions, which require close coordination among multiple actors, occur in an integrated environment. To better define the structural basis of genome packaging and organization in herpes simplex virus type 1 (HSV-1), we developed sequential localized classification and symmetry relaxation methods to process cryo-electron microscopy (cryo-EM) images of HSV-1 virions, which enabled us to decouple and reconstruct hetero-symmetric and asymmetric elements within the pseudo-icosahedral capsid. Here we present in situ structures of the unique portal vertex, genomic termini and ordered dsDNA coils in the capsid spooled around a disordered dsDNA core. We identify tentacle-like helices and a globular complex capping the portal vertex that is not observed in phages, indicative of herpesvirus-specific adaptations in the DNA-packaging process. Finally, our atomic models of portal vertex elements reveal how the fivefold-related capsid accommodates symmetry mismatch imparted by the dodecameric portal-a longstanding mystery in icosahedral viruses-and inform possible DNA-sequence recognition and headful-sensing pathways involved in genome packaging. This work showcases how to resolve symmetry-mismatched elements in a large eukaryotic virus and provides insights into the mechanisms of herpesvirus genome packaging

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

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.

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

Modélisation métabolique à l’échelle du génome de la bactérie quasi-minimale Mesoplasma florum

Des avancées significatives au niveau de la synthèse et de l’assemblage de fragments d’acide désoxyribonucléique (ADN), le support physique des fonctions cellulaires encodées dans une cellule vivante, permettent maintenant la construction de génomes entiers. Ce progrès permet d’imaginer que la conception d’organismes synthétiques deviendra routinière au cours des prochaines années. Cette capacité promet de transformer radicalement le domaine de la biologie en formant une nouvelle discipline d’ingénierie biologique. Parmi les retombées anticipées, on note le remplacement de synthèses chimiques par des procédés biologiques renouvelables tels que la production de biocarburants, la synthèse de médicaments microbiens, ou des approches alternatives pour le traitement des maladies. Dans ce contexte, il devient particulièrement important d’arriver à prédire correctement le phénotype résultant des génomes qui seront générés. Pour y arriver, il convient de réduire la complexité biologique en travaillant d’abord avec les cellules les plus simples possibles. Ce type d’organisme ayant subi un processus de réduction de génome et dont la majorité des gènes sont essentiels afin de survivre en conditions définies se nomme une cellule minimale. Le groupe phylogénétique des mollicutes, bactéries dépourvues de paroi cellulaire, contient les espèces vivant avec les plus petits génomes connus à ce jour. Membre de ce groupe, le pathogène humain Mycoplasma genitalium possède le plus petit génome capable de croissance autonome (560kbp codant pour 482 protéines. Cependant, sa pathogénicité et sa vitesse de croissance réduite (~24h) limitent l’applicabilité de M. genitalium en biologie synthétique. Pour remédier à ce problème, notre laboratoire a choisi de travailler avec Mesoplasma florum dont le temps de doublement est très rapide (~32 min) et qui ne cause pas de maladies chez l’humain. Les travaux effectués chez M. florum permettent maintenant le clonage et la transplantation de son génome et des travaux récents ont permis de caractériser les propriétés physico-chimiques de sa cellule ainsi que plusieurs paramètres biologiques. Afin de permettre la conception de génomes synthétiques basés sur M. florum, il convient d’intégrer un maximum de connaissances dans un cadre informatique structuré capable de générer des prédictions phénotypiques. Un modèle métabolique à l’échelle du génome (GEM) reposant sur la méthode d’analyse des flux à l’équilibre (FBA) représente un format particulièrement intéressant pour initier ces travaux de biologie des systèmes. La qualité des prédictions générées par ce type de modèle est dépendante de la précision de l’objectif à atteindre. Pour simuler la croissance, les GEMs doivent satisfaire un objectif nommé “fonction objective de biomasse” (BOF) qui contient l’ensemble des métabolites nécessaires à la production d’une nouvelle cellule avec des coefficients stœchiométriques représentatifs de l’abondance de ces composantes dans la cellule. Pendant mon parcours de doctorat, j’ai développé le logiciel BOFdat qui permet la définition d’une BOF représentative de la composition cellulaire spécifique à une espèce avec les données expérimentales associées. Les deux premières des trois étapes de BOFdat déterminent les coefficients stoechiométriques de molécules connues pour faire partie de la composition cellulaire telles que les macromolécules principales (étape 1, ADN, ARN et protéines) et les coenzymes essentiels (étape 2). L’étape 3 de BOFdat propose une méthode non-biaisée pour déterminer les métabolites susceptibles d’améliorer la prédiction d’essentialité des gènes formulée par le modèle. Pour ce faire, un algorithme génétique maximise la composition de la biomasse en fonction des données d’essentialité expérimentales à l’échelle du génome. BOFdat a été validé en reconstruisant la BOF du modèle iML1515 de la bactérie modèle Escherichia coli. L’utilisation de BOFdat a permis de récapituler le taux de croissance prédit avec la BOF originale tout en améliorant la qualité des prédictions d’essentialité de gènes de iML1515. BOFdat est disponible en libre accès pour quiconque désire construire une BOF pour un modèle métabolique. Ensuite, un GEM nommé iJL208 a été produit et contient 208 des 676 protéines représentant l’ensemble du métabolisme de M. florum. La qualité de l’annotation du génome a d’abord été évaluée en intégrant l’information obtenue par trois approches bio-informatiques, révélant que la majorité des protéines (418/676) ont une qualité suffisante pour être incorporées dans le modèle. Ensuite, les réactions ont été identifiées et rigoureusement incorporées une à la fois afin de construire le réseau métabolique de cette bactérie quasi-minimale. L’étude de la carte métabolique reconstruite révèle une dépendance prononcée pour l’import de composantes à partir du milieu de culture ainsi que l’importance des mécanismes de recyclage des métabolites. Pour sa production d’énergie, M. florum est entièrement dépendante de la glycolyse et ne possède pas la machinerie nécessaire à la respiration cellulaire. L’élaboration d’un milieu de culture semi-défini a réduit la présence de sucres contaminants dans le milieu de culture initial et ainsi de distinguer la croissance avec ou sans supplémentation de sucrose. Cette avancée importante a permis de mesurer les taux d’assimilation de sucrose et de production des déchets métaboliques lactate et acétate. Ces paramètres ont été utilisés afin de contraindre le modèle et de mieux comprendre la sensibilité du modèle à une variété de paramètres. Aussi, la croissance de M. florum a pu être validée expérimentalement avec différents sucres. L’information contextuelle obtenue, combinée à une analyse de structures tridimensionnelles de protéines clés, a permis de suggérer des hypothèses crédibles supportant l’assimilation de ces sucres par M. florum. Finalement, iJL208 a été utilisé afin de formuler une prédiction de génome minimal pour M. florum en simulant itérativement de larges délétions dans son génome. Combiner l’intégration de données expérimentales avec les prédictions du modèle constitue une voie d’avenir pour la conception de génomes synthétiques qui rejoint les capacités techniques d’assemblage de chromosomes en biologie synthétique. Globalement, les projets réalisés au cours de mon doctorat contribuent à l’avancement de la biologie des systèmes chez M. florum dans le but de prédire efficacement les phénotypes de la souche naturelle et de variants synthétiques qui pourront être produits au cours des prochaines années

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

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

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