Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials

Quantum ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). Quantum ESPRESSO stands for "opEn Source Package for Research in Electronic Structure, Simulation, and Optimization". It is freely available to researchers around the world under the terms of the GNU General Public License. Quantum ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively-parallel architectures, and a great effort being devoted to user friendliness. Quantum ESPRESSO is evolving towards a distribution of independent and inter-operable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.Comment: 36 pages, 5 figures, resubmitted to J.Phys.: Condens. Matte

Automated exploration of prebiotic chemical reaction space: progress and perspectives

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models

Modern views of ancient metabolic networks

Metabolism is a molecular, cellular, ecological and planetary phenomenon, whose fundamental principles are likely at the heart of what makes living matter different from inanimate one. Systems biology approaches developed for the quantitative analysis of metabolism at multiple scales can help understand metabolism's ancient history. In this review, we highlight work that uses network-level approaches to shed light on key innovations in ancient life, including the emergence of proto-metabolic networks, collective autocatalysis and bioenergetics coupling. Recent experiments and computational analyses have revealed new aspects of this ancient history, paving the way for the use of large datasets to further improve our understanding of life's principles and abiogenesis.https://www.sciencedirect.com/science/article/pii/S2452310017302196Published versio

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Probing protein sequences as sources for encrypted antimicrobial peptides

Starting from the premise that a wealth of potentially biologically active peptides may lurk within proteins, we describe here a methodology to identify putative antimicrobial peptides encrypted in protein sequences. Candidate peptides were identified using a new screening procedure based on physicochemical criteria to reveal matching peptides within protein databases. Fifteen such peptides, along with a range of natural antimicrobial peptides, were examined using DSC and CD to characterize their interaction with phospholipid membranes. Principal component analysis of DSC data shows that the investigated peptides group according to their effects on the main phase transition of phospholipid vesicles, and that these effects correlate both to antimicrobial activity and to the changes in peptide secondary structure. Consequently, we have been able to identify novel antimicrobial peptides from larger proteins not hitherto associated with such activity, mimicking endogenous and/or exogenous microorganism enzymatic processing of parent proteins to smaller bioactive molecules. A biotechnological application for this methodology is explored. Soybean (Glycine max) plants, transformed to include a putative antimicrobial protein fragment encoded in its own genome were tested for tolerance against Phakopsora pachyrhizi, the causative agent of the Asian soybean rust. This procedure may represent an inventive alternative to the transgenic technology, since the genetic material to be used belongs to the host organism and not to exogenous sources