The formose reaction (FR) autocatalytically converts simple plausibly prebiotic feedstocks into molecules of biological interest, including ribose. Autocatalysis is a hallmark of life, thus various studies have explored the formose reaction with respect to the origins of life. The FR is robust under appropriate conditions, occurring readily at low temperatures from various substrates, and has been implicated in the generation of meteoritic organic compounds. We explored the FR here using a combination of in silico modeling techniques and high resolution mass spectrometry. The models match experimental results well, and point to the FR being much more complex than previously modeled or measured, and help explain the FR’s potential to generate homochirality and primitive compartments, both of which are also hallmarks of life, before the emergence of the complex directed molecular encoding suggested by the RNA World model. These results suggest the FR requires further study with regard to the origins of life, and its importance may lie in the way it enables and coordinates emergent chemistries, rather than the particular products it generates, such as ribose.

ABSTRACT Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs, while also relying on both sequence and structural information of the PNA complexes for both train/test and also as inputs. As PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to a small training dataset. Additionally, a majority of the tools predict a single parameter such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model trained on a vast and heterogeneous dataset with 14,401 entries (from both eukaryotes and prokaryotes) of ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from the sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting the K D and ΔΔG implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/

The Large Interferometer For Exoplanets (LIFE) is a proposed space mission that enables the spectral characterization of the thermal emission of exoplanets in the solar neighborhood. The mission is designed to search for global atmospheric biosignatures on dozens of temperate terrestrial exoplanets and it will naturally investigate the diversity of other worlds. Here, we review the status of the mission concept, discuss the key mission parameters, and outline the trade-offs related to the mission's architecture. In preparation for an upcoming concept study, we define a mission baseline based on a free-formation flying constellation of a double Bracewell nulling interferometer that consists of 4 collectors and a central beam-combiner spacecraft. The interferometric baselines are between 10-600 m, and the estimated diameters of the collectors are at least 2 m (but will depend on the total achievable instrument throughput). The spectral required wavelength range is 6-16 mu m (with a goal of 4-18.5 mu m), hence cryogenic temperatures are needed both for the collectors and the beam combiners. One of the key challenges is the required deep, stable, and broad-band nulling performance while maintaining a high system throughput for the planet signal. Among many ongoing or needed technology development activities, the demonstration of the measurement principle under cryogenic conditions is fundamentally important for LIFE.

Hydrogen cyanide (HCN)-derived molecules and polymers feature in several hypotheses on the origin of life. Over half-a-century of investigations into HCN self-reactions have led to many suggestions regarding the structural nature of the products, and an even greater number of proposed polymerization pathways. A comprehensive overview of possible reactions and structures is missing. In this work, we use quantum chemical calculations to map the relative free energy of most HCN-derived molecules and polymers that have been discussed in the literature. Our computed free energies indicate that several previously considered polymerization pathways are not spontaneous and should be discarded from future consideration. Among the most thermodynamically favored products are polyaminoimidazole and adenine.

In organic synthesis, formamides are the most abundant class of compounds owing to their importance as the building blocks in synthetic and industrial organic chemistry. Formamides are also one of the most valuable intermediates produced in drug synthesis and act as precursors for the synthesis of therapeutically-relevant molecules. Here, we developed a simple, practical, and catalytic methodology for the N-formylation of a wide variety of amines by using iron as a catalyst. This protocol features an inexpensive iron catalyst, short reaction time, mild reaction conditions, good yields, easy workup, and tolerance of a wide variety of functional groups.

This article provides a comprehensive review and sequence-structure analysis of transcription regulator (TR) families, TetR and OmpR/PhoB, involved in specialized secondary metabolite (SSM) biosynthesis and resistance. Transcription regulation is a fundamental process, playing a crucial role in orchestrating gene expression to confer a survival advantage in response to frequent environmental stress conditions. This process, coupled with signal sensing, enables bacteria to respond to a diverse range of intra and extracellular signals. Thus, major bacterial signaling systems use a receptor domain to sense chemical stimuli along with an output domain responsible for transcription regulation through DNA-binding. Sensory and output domains on a single polypeptide chain (one component system, OCS) allow response to stimuli by allostery, that is, DNA-binding affinity modulation upon signal presence/absence. On the other hand, two component systems (TCSs) allow cross-talk between the sensory and output domains as they are disjoint and transmit information by phosphorelay to mount a response. In both cases, however, TRs play a central role. Biosynthesis of SSMs, which includes antibiotics, is heavily regulated by TRs as it diverts the cell's resources towards the production of these expendable compounds, which also have clinical applications. These TRs have evolved to relay information across specific signals and target genes, thus providing a rich source of unique mechanisms to explore towards addressing the rapid escalation in antimicrobial resistance (AMR). Here we focus on the TetR and OmpR family TRs, which belong to OCS and TCS, respectively. These TR families are well-known examples of regulators in secondary metabolism and are ubiquitous across different bacteria, as they also participate in a myriad of cellular processes apart from SSM biosynthesis and resistance. As a result, these families exhibit higher sequence divergence, which is also evident from our bioinformatic analysis of 158 389 and 77 437 sequences from TetR and OmpR family TRs, respectively. The analysis of both sequence and structure allowed us to identify novel motifs in addition to the known motifs responsible for TR function and its structural integrity. Understanding the diverse mechanisms employed by these TRs is essential for unraveling the biosynthesis of SSMs. This can also help exploit their regulatory role in biosynthesis for significant pharmaceutical, agricultural, and industrial applications.

Amyloid-based prions have simple structures, a wide phylogenetic distribution, and a plethora of functions in contemporary organisms, suggesting they may be an ancient phenomenon. However, this hypothesis has yet to be addressed with a systematic, computational, and experimental approach. Here we present a framework to help guide future experimental verification of candidate prions with conserved functions to understand their role in the early stages of evolution and potentially in the origins of life. We identified candidate prions in all high-quality proteomes available in UniProt computationally, assessed their phylogenomic distributions, and analyzed candidate-prion functional annotations. Of the 27 980 560 proteins scanned, 228 561 were identified as candidate prions (~0.82%). Among these candidates, there were 84 Gene Ontology (GO) terms conserved across the three domains of life. We found that candidate prions with a possible role in adaptation were particularly well-represented within this group. We discuss unifying features of candidate prions to elucidate the primeval roles of prions and their associated functions. Candidate prions annotated as transcription factors, DNA binding, and kinases are particularly well suited to generating diverse responses to changes in their environment and could allow for adaptation and population expansion into more diverse environments. We hypothesized that a relationship between these functions and candidate prions could be evolutionarily ancient, even if individual prion domains themselves are not evolutionarily conserved. Candidate prions annotated with these universally occurring functions potentially represent the oldest extant prions on Earth and are therefore excellent experimental targets.

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly -- from analytical chemistry to mathematical models -- and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem.

Astrobiology is an emerging and interdisciplinary scientific field that aims at studying and understanding life in the universe, with research topics including the origin of life and the feasibility of life existing and being detected elsewhere in the universe. In this article, we highlight the critical role that astrobiology plays toward inspiring students and the general public toward space exploration. The cross-disciplinary nature of the field brings together scientists from various backgrounds, breaking down silos that exist in today's scientific community in India. Finally, astrobiology has continuously played a pivotal role in shaping outer space exploration programs and will be an important scientific area of development for the Indian Space Research Organization as well. The formation of the Indian National Space Promotion and Authorization Centre (IN-SPACe) body and the emerging New Space ecosystem in India are discussed, as they offer unique opportunities to bring down the cost, time, and programmatic challenges traditionally faced by Astrobiology missions.

A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.

Hydrogen cyanide (HCN) is a widely available molecule in planetary and interplanetary environments.It has been observed that the polymerization of HCN can lead to the formation of nucleobases and proteins (Matthews & Minard 2006).Thus, HCN and its reactivity are considered to be very important for prebiotic chemistry.Our evaluation covers several molecules and oligomers, most of which have been discussed in the literature (Ruiz-Bermejo et al., 2021), and ranks them based on thermodynamic preference.In our study, we compute the relative energies of a series of HCN-derived materials relative to HCN in liquid water.We perform an automated search with semi-empirical methods to extract the lowest energy conformers for each compound.Our work relies on density functional theory (DFT) calculations with thermal corrections coupled to an implicit solvation model to better emulate the polymerization environments.These methodologies allow us to discuss the impact of our results at relevant environments such as that of Saturn's Moon Titan or the early Earth conditions.The most stable HCN-derived material in our set is the nucleobase adenine, computed to lie ~26 kcal mol-1 below HCN in a water solution.Our enumeration of thermodynamically plausible reaction products and the reaction routes for the abiotic formation of organic macromolecules starting from simple units of HCN offers extensive insights into the chemical and the physical limitations of suspected key prebiotic processes (

Technosignatures refer to observational manifestations of technology that could be detected through astronomical means. Most previous searches for technosignatures have focused on searches for radio signals, but many current and future observing facilities could also constrain the prevalence of some non-radio technosignatures. This search could thus benefit from broader participation by the astronomical community, as contributions to technosignature science can also take the form of negative results that provide statistically meaningful quantitative upper limits on the presence of a signal. This paper provides a synthesis of the recommendations of the 2020 TechnoClimes workshop, which was an online event intended to develop a research agenda to prioritize and guide future theoretical and observational studies technosignatures. The paper provides a high-level overview of the use of current and future missions to detect exoplanetary technosignatures at ultraviolet, optical, or infrared wavelengths, which specifically focuses on the detectability of atmospheric technosignatures, artificial surface modifications, optical beacons, space engineering and megastructures, and interstellar flight. This overview does not derive any new quantitative detection limits but is intended to provide additional science justification for the use of current and planned observing facilities as well as to inspire astronomers conducting such observations to consider the relevance of their ongoing observations to technosignature science. This synthesis also identifies possible technology gaps with the ability of current and planned missions to search for technosignatures, which suggests the need to consider technosignature science cases in the design of future mission concepts.

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.

Complex chemical reaction networks can grow exponentially in terms of the chemical diversity they generate. It is unknown whether such networks easily discover or shuttle fluxes through autocatalytic sub-networks. In general, such sub-networks may be common or rare or anywhere in between in organic chemistry. We aim to provide a map for experimental chemists studying complex organic reactions using an automated rule-based reaction generation to simulate the reactions involved in various plausible abiotic reactions proposed to account for the organic diversity observed in carbonaceous meteorites, thus providing enough data for ground-truthing of our methods. We applied graph transformation rules based on well-documented reaction mechanisms, chemical intuition and applied various constraints to the outputs, such as disallowed output structural motifs, thereby restricting them. We used isomorphism tests to match the output molecular structures to experimentally reported structures as a test of completeness of the methods in our study. The monoisotopic exact masses of the molecules in the computed reaction network product set were calculated and used to match peaks identified in high-resolution FT-ICR-MS data of the same reaction. We modeled the alkaline degradation of glucose using our workflow and found that our model was able to explain 96% of the structures reported in analytical studies (e.g., Yang and Montgomery, 1996). When the same workflow was applied to simulate formose chemistry

Motivation: Lujo Hemorrhagic Fever Virus (LUHFV) has emerged as a human pathogenic viral infectious disease with a high mortality rate. Our study aims to identify molecules with appreciable inhibition through molecular modeling against Lujo Hemorrhagic Fever Virus. These compounds can further serve as potential drug candidates for the treatment of viral hemorrhagic fever by validating them through rigorous wet laboratory studies. Methods: We identified a 3D crystal structure of the GP1 domain of the Lujo virus in combination with the first CUB domain of neuropilin-2 of 6GH8 protein using 6GH8 viral protein with a specific chain refinement that carries NRP2, VEGF165R2 genes with a homo sapiens host. We utilized our study workflow to analyze the protein-protein interaction (PPI) using STRING, predict protein bioactivity using Molinspiration, and cytotoxic effects of compounds via CLC tools. We selected the three main ligands from PubChem: Rivabirin, N-acetylcysteine, and Atorvastatin through intensive literature studies. We then analyzed the absorption, distribution, metabolism, and excretion, and toxicity (ADMET) characteristics of all three main control ligands with their similar compounds by utilizing Ambinter as default 80% similarity searching, yielding a total of 276 compounds for further validation in our study. Filtering of the 276 total compounds was done according to the Lipinsky rule. The PyRx tool and AutoDock Vina were used for performing the docking study. The top three docking binding affinity compounds with main ligands total of 12 compounds were selected for protein-ligand interaction analysis. The molecular dynamic (MD) simulation was done for the top 12 compounds of three ligands according to the docking results. Results and Conclusions: The risk posed by this Lujo virus has necessitated the design and development of the associated viral inhibitor. Ribavirin, N-acetylcysteine, and Atorvastatin were chosen as control ligands with 276 compounds being curated based on similarity search. After rigorous ADMET filtration and molecular docking, about 12 compounds were identified as potential Lujo viral inhibitors. Molecular dynamics simulation results proved the stability of the ligand-protein complex. The risk posed by this virus has necessitated the design and development of the associated viral inhibitor. The 12 identified compounds have the potential of being a target to this virus and thus should further be developed through wet laboratory studies. Acknowledgments: The authors thank Emmanuel Ifeanyi Attah and Opeyemi Isaac for their mentor capacity on this project. The authors are grateful to TheHackbio (https://thehackbio.com) for providing a virtual platform and also acknowledge DNA Compass (www.dnacompass.com), BioSeqC (www.bioseqc.com), Helix Biogen Consult (www.helixbiogenconsult.org) for their support.

The COVID19 pandemic has resulted in 1,092,342 deaths as of 14th October 2020, indicating the urgent need for a vaccine. This study highlights novel protein sequences generated by shot gun sequencing protocols that could serve as potential antigens in the development of novel subunit vaccines and through a stringent inclusion criterion, we characterized these protein sequences and predicted their 3D structures. We found distinctly antigenic sequences from the SARS-CoV-2 that have led to identification of 4 proteins that demonstrate an advantageous binding with Human leukocyte antigen-1 molecules. Results show how previously unexplored proteins may serve as better candidates for subunit vaccine development due to their high stability and immunogenicity, reinforce by their HLA-1 binding propensities and low global binding energies. This study thus takes a unique approach towards furthering the development of vaccines by employing multiple consensus strategies involved in immuno-informatics technique. ### Competing Interest Statement The authors have declared no competing interest.

Patients with melanoma and the Val600 BRAF mutation benefit from combined inhibition of BRAF and MEK, according to a new study. Selective BRAF inhibitors such as dabrafenib and MEK inhibitors such as trametinib have individually been shown to increase progression-free survival (PFS) and overall survival in Val600 BRAF-mutant melanoma. But 50% of the patients treated with BRAF or MEK inhibitors develop resistance leading to disease progression in 6–7 months. Investigators thus targeted MEK and BRAF inhibition simultaneously as a possible way to overcome this resistance to monotherapy. Correction to Lancet Oncol 2012; 13: e468Sharma SP. BRAF and MEK inhibitors in BRAF-mutant melanoma. Lancet Oncol 2012; 13: e468—In this News item, the second and third sentences of the second paragraph should have read: “Median PFS was 9·4 months (95% CI 8·6—16·7) in the combination group compared with 5·8 months (4·6–7·4) in the dabrafenib monotherapy group (hazard ratio 0·39, 95% CI 0·25–0·62; p<0·001). Compared with dabrafenib alone, combination therapy led to an increase in complete or partial responses (76% for combination therapy vs 54% for monotherapy; p=0·03), without significant differences in side-effects between groups.” These corrections have been made to the online version as of Oct 29, 2012. Full-Text PDF