Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians.

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.

Uso del análisis comparativo del riesgo de extinción para priorizar la reevaluación de los anfibios en la Lista Roja de la UICN Resumen El análisis del riesgo de extinción de una especie con base en la Lista Roja (LR) de la Unión Internacional para la Conservación de la Naturaleza (UICN) es clave para guiar las políticas de conservación y reducir la pérdida de la biodiversidad. Sin embargo, este proceso demanda recursos y requiere de actualizaciones continuas, lo que se complica conforme se añaden especies nuevas a la LR. Los métodos automáticos, como los análisis comparativos usados para predecir la categoría de la especie en la LR, pueden ser una alternativa eficiente para mantener actualizados los análisis. Usamos a los anfibios como grupo de estudio para predecir cuáles especies tienen mayor probabilidad de cambiar de categoría en la LR y que, por lo tanto, se debería priorizar su reevaluación. Usamos las características biológicas de la especie, las variables ambientales e indicadores climáticos y del cambio de uso de suelo como predictores de la categoría en la LR. Elaboramos una predicción de ensamble de la categoría en la LR de la UICN para cada especie mediante la combinación de cuatro algoritmos diferentes: modelos de vínculo acumulativo, menor número de cuadros filogenéticos generalizados, bosques aleatorios y redes neurales. Con la comparación entre las categorías de la LR y la predicción de ensamble y con considerar la incertidumbre entre los algoritmos identificamos especies que deberían ser prioridad para futuras reevaluaciones con base en el desfase entre los valores predichos y los observados. Las variables de predicción más importantes entre los modelos fueron el tamaño de la distribución de la especie y su configuración espacial, las características biológicas, el cambio climático y el cambio de uso de suelo. Comparamos nuestra propuesta de índice de priorización y los cambios predichos en la LR con las reevaluaciones independientes de la LR de la UICN y descubrimos un buen desempeño tanto para la priorización como para la direccionalidad predicha de los cambios en las categorías de la LR. El modelo de ensamble de la categoría de la LR esa una herramienta prometedora para priorizar la reevaluación de las especies a la vez que considera la incertidumbre del modelo. Esta estrategia puede generalizarse para aplicarse a todos los taxones de la LR de la UICN y a los análisis regionales y nacionales. También podría mejorar la asignación de los recursos humanos y económicos limitados disponibles para mantener actualizada la LR de la UICN.

Modelling the probability of meeting IUCN Red List criteria to support reassessments.

Comparative extinction risk analysis-which predicts species extinction risk from correlation with traits or geographical characteristics-has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

A standard approach for including climate change responses in IUCN Red List assessments.

The International Union for Conservation of Nature (IUCN) Red List is a central tool for extinction risk monitoring and influences global biodiversity policy and action. But, to be effective, it is crucial that it consistently accounts for each driver of extinction. Climate change is rapidly becoming a key extinction driver, but consideration of climate change information remains challenging for the IUCN. Several methods can be used to predict species' future decline, but they often fail to provide estimates of the symptoms of endangerment used by IUCN. We devised a standardized method to measure climate change impact in terms of change in habitat quality to inform criterion A3 on future population reduction. Using terrestrial nonvolant tetrapods as a case study, we measured this impact as the difference between the current and the future species climatic niche, defined based on current and future bioclimatic variables under alternative model algorithms, dispersal scenarios, emission scenarios, and climate models. Our models identified 171 species (13% out of those analyzed) for which their current red-list category could worsen under criterion A3 if they cannot disperse beyond their current range in the future. Categories for 14 species (1.5%) could worsen if maximum dispersal is possible. Although ours is a simulation exercise and not a formal red-list assessment, our results suggest that considering climate change impacts may reduce misclassification and strengthen consistency and comprehensiveness of IUCN Red List assessments.

Una estrategia estándar para incluir las respuestas al cambio climático en las evaluaciones de la Lista Roja de la UICN Resumen La Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (UICN) es una herramienta central para el monitoreo del riesgo de extinción e influye sobre las acciones y políticas para la biodiversidad. Para que esta herramienta sea efectiva, es crucial que tenga en cuenta de manera regular cada factor de extinción. El cambio climático se está convirtiendo rápidamente en un factor de extinción importante, pero considerar información sobre este factor todavía es un reto para la UICN. Se pueden usar varios métodos para predecir la declinación de una especie en el futuro, pero generalmente fallan en proporcionar estimaciones de los síntomas del peligro usados por la UICN. Diseñamos un método estandarizado para medir el impacto del cambio climático en términos del cambio en la calidad del hábitat para informar el criterio A3 sobre la reducción futura de las poblaciones. Usamos a los tetrápodos terrestres no voladores como estudio de caso para medir este impacto como la diferencia entre el nicho climático actual y futuro de las especies, definido con base en las variables bioclimáticas actuales y futuras con algoritmos de modelos alternativos, escenarios de dispersión y emisión y modelos climáticos. Nuestros modelos identificaron 171 especies (13% de las especies analizadas) para las que su categoría actual en la lista roja podría empeorar bajo el criterio A3 si no logran dispersarse más allá de su distribución actual en el futuro. Las categorías para 14 especies (1.5%) podrían empeorar si es posible la dispersión máxima. Aunque realizamos una simulación y no una evaluación formal para listas rojas, nuestros resultados sugieren que considerar los impactos del cambio climático podría reducir la clasificación incorrecta y fortalecer la coherencia y exhaustividad de las evaluaciones de la Lista Roja de la UICN.

Prioritizing the reassessment of data-deficient species on the IUCN Red List.

Despite being central to the implementation of conservation policies, the usefulness of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species is hampered by the 14% of species classified as data-deficient (DD) because information to evaluate these species' extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. Robust methods are needed to identify which DD species are more likely to be reclassified in one of the data-sufficient IUCN Red List categories. We devised a reproducible method to help red-list assessors prioritize reassessment of DD species and tested it with 6887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). For each DD species in these groups, we calculated its probability of being classified in a data-sufficient category if reassessed today from covariates measuring available knowledge (e.g., number of occurrence records or published articles available), knowledge proxies (e.g., remoteness of the range), and species characteristics (e.g., nocturnality); calculated change in such probability since last assessment from the increase in available knowledge (e.g., new occurrence records); and determined whether the species might qualify as threatened based on recent rate of habitat loss determined from global land-cover maps. We identified 1907 species with a probability of being reassessed in a data-sufficient category of >0.5; 624 species for which this probability increased by >0.25 since last assessment; and 77 species that could be reassessed as near threatened or threatened based on habitat loss. Combining these 3 elements, our results provided a list of species likely to be data-sufficient such that the comprehensiveness and representativeness of the IUCN Red List can be improved.

Priorización de la reevaluación de las especies con datos deficientes en la Lista Roja de la UICN Resumen No obstante que es fundamental para la implementación de políticas de conservación, la utilidad de la Lista Roja de Especies Amenazadas de la Unión Internacional para la Conservación de la Naturaleza (UICN) está limitada por el 14% de especies clasificadas con datos deficientes (DD) debido a que la información para evaluar el riesgo de extinción de estas especies no existía cuando fueron evaluadas la última vez o porque los evaluadores no consideraron la incertidumbre apropiadamente. Se requieren métodos robustos para identificar las especies DD con mayor probabilidad de ser reclasificadas en alguna de las categorías en la Lista Roja UICN con datos suficientes. Diseñamos un método reproducible para ayudar a que los evaluadores de la lista roja prioricen la reevaluación de especies DD y lo probamos con 6,887 especies DD de mamíferos, reptiles, anfibios, peces y Odonata (libélulas y caballitos del diablo). Para cada una de las especies DD en estos grupos, calculamos la probabilidad de ser clasificadas en una categoría con datos suficientes si fuera reevaluada hoy a partir de covariables que miden el conocimiento disponible (e.g., número de registros de ocurrencia o artículos publicados disponibles), sustitutos de conocimiento (e.g., extensión del rango de distribución) y características de la especie ((e.g., nocturnidad); calculamos el cambio en tal probabilidad desde la última reevaluación a partir del incremento en el conocimiento disponible (e.g., registros de ocurrencia nuevos); y determinamos si las especies podrían calificar como amenazadas con base en pérdidas de hábitat recientes a partir de mapas globales de cobertura de suelo recientes. Identificamos 1,907 especies con una probabilidad >0.5 de ser reclasificados en una categoría con datos suficientes; 624 especies cuya probabilidad aumentó en >0.25 desde la última evaluación, y 77 especies que podrían ser reclasificadas como casi en peligro con base en la pérdida de hábitat. Combinando estos 3 elementos, nuestros resultados proporcionaron una lista de especies probablemente con datos suficientes de tal modo que la exhaustividad y la representatividad de la Lista Roja de la UICN pueden ser mejoradas.

Benchmark Structures and Conformational Landscapes of Amino Acids in the Gas Phase: A Joint Venture of Machine Learning, Quantum Chemistry, and Rotational Spectroscopy.

The accurate characterization of prototypical bricks of life can strongly benefit from the integration of high resolution spectroscopy and quantum mechanical computations. We have selected a number of representative amino acids (glycine, alanine, serine, cysteine, threonine, aspartic acid and asparagine) to validate a new computational setup rooted in quantum-chemical computations of increasing accuracy guided by machine learning tools. Together with low-lying energy minima, the barriers ruling their interconversion are evaluated in order to unravel possible fast relaxation paths. Vibrational and thermal effects are also included in order to estimate relative free energies at the temperature of interest in the experiment. The spectroscopic parameters of all the most stable conformers predicted by this computational strategy, which do not have low-energy relaxation paths available, closely match those of the species detected in microwave experiments. Together with their intrinsic interest, these accurate results represent ideal benchmarks for more approximate methods.

Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents.

The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper we report our latest developments with specific reference to unsupervised atomistic simulations employing non periodic boundary conditions (NPBC) followed by clustering of the trajectories employing optimized feature spaces. Next accurate variational computations are performed for a representative point of each cluster, whereas intracluster fluctuations are taken into account by a cheap yet reliable perturbative approach. A number of methodological improvements have been introduced including, e.g., more realistic reaction field effects at the outer boundary of the simulation sphere, automatic definition of the feature space by continuous perception of solute-solvent interactions, full account of polarization and charge transfer in the first solvation shell, and inclusion of vibronic contributions. After its validation, this new approach has been applied to the challenging case of solvatochromic effects on the UV-vis spectra of a prototypical nitroxide radical (TEMPO) in different solvents. The reliability, effectiveness, and robustness of the new platform is demonstrated by the remarkable agreement with experiment of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional quantum mechanics and molecular mechanics models without any accuracy reduction.

L-DOPA and Droxidopa: From Force Field Development to Molecular Docking into Human ß2-Adrenergic Receptor.

The increasing interest in the molecular mechanism of the binding of different agonists and antagonists to ß2-adrenergic receptor (ß2AR) inactive and active states has led us to investigate protein-ligand interactions using molecular docking calculations. To perform this study, the 3.2 Å X-ray crystal structure of the active conformation of human ß2AR in the complex with the endogenous agonist adrenaline has been used as a template for investigating the binding of two exogenous catecholamines to this adrenergic receptor. Here, we show the derivation of L-DOPA and Droxidopa OPLS all atom (AA) force field (FF) parameters via quantum mechanical (QM) calculations, molecular dynamics (MD) simulations in aqueous solutions of the two catecholamines and the molecular docking of both ligands into rigid and flexible ß2AR models. We observe that both ligands share with adrenaline similar experimentally observed binding anchor sites, which are constituted by Asp113/Asn312 and Ser203/Ser204/Ser207 side chains. Moreover, both L-DOPA and Droxidopa molecules exhibit binding affinities comparable to that predicted for adrenaline, which is in good agreement with previous experimental and computational results. L-DOPA and Droxidopa OPLS AA FFs have also been tested by performing MD simulations of these ligands docked into ß2AR proteins embedded in lipid membranes. Both hydrogen bonds and hydrophobic interaction networks observed over the 1 µs MD simulation are comparable with those derived from molecular docking calculations and MD simulations performed with the CHARMM FF.

Unbiased disentanglement of conformational baths with the help of microwave spectroscopy, quantum chemistry, and artificial intelligence: The puzzling case of homocysteine.

An integrated experimental-computational strategy for the accurate characterization of the conformational landscape of flexible biomolecule building blocks is proposed. This is based on the combination of rotational spectroscopy with quantum-chemical computations guided by artificial intelligence tools. The first step of the strategy is the conformer search and relative stability evaluation performed by means of an evolutionary algorithm. In this step, last generation semiempirical methods are exploited together with hybrid and double-hybrid density functionals. Next, the barriers ruling the interconversion between the low-lying conformers are evaluated in order to unravel the possible fast relaxation paths. The relative stabilities and spectroscopic parameters of the "surviving" conformers are then refined using state-of-the-art composite schemes. The reliability of the computational procedure is further improved by the inclusion of vibrational and thermal effects. The final step of the strategy is the comparison between experiment and theory without any ad hoc adjustment, which allows an unbiased assignment of the spectroscopic features in terms of different conformers and their spectroscopic parameters. The proposed approach has been tested and validated for homocysteine, a highly flexible non-proteinogenic α-amino acid. The synergism of the integrated strategy allowed for the characterization of five conformers stabilized by bifurcated N-H2⋯O=C hydrogen bonds, together with an additional conformer involving a more conventional HN⋯H-O hydrogen bond. The stability order estimated from the experimental intensities as well as the number and type of conformers observed in the gas phase are in full agreement with the theoretical predictions. Analogously, a good match has been found for the spectroscopic parameters.

Development, Validation, and Pilot Application of a Generalized Fluctuating Charge Model for Computational Spectroscopy in Solution.

A general approach enforcing nonperiodic boundary conditions for the computation of spectroscopic properties in solution has been improved including an effective description of charge-transfer contributions and coordination number adjustment for explicit solvent molecules. Both contributions are obtained from a continuous description of intermolecular hydrogen bonds, which has been employed also for an effective clustering of molecular dynamics trajectories. Fine tuning of the model has been performed for several water clusters, and then its efficiency and reliability have been demonstrated by computing the absorption spectra of different creatinine tautomers in aqueous solution.

Molecular Dynamics Simulations Enforcing Nonperiodic Boundary Conditions: New Developments and Application to the Solvent Shifts of Nitroxide Magnetic Parameters.

Multiscale methods combining quantum mechanics and molecular mechanics (QM/MM) have become the most suitable and effective strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context, we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper, we report our latest developments with specific reference to a general protocol based on atomistic simulations, carried out under nonperiodic boundary conditions (NPBC). In particular, we add to our in house MD engine a new efficient treatment of mean field electrostatic contributions to energy and forces, together with the capability of performing the simulations either in the canonical (NVT) or in the isothermal-isobaric (NPT) ensemble. Next, we provide convincing evidence that the NBPC approach enhanced by specific tweaks for rigid body propagation, allows for the simulation of solute-solvent systems with a minimum number of degrees of freedom and large integration time step. After its validation, this new approach is applied to the challenging case of solvatochromic effects on the electron paramagnetic resonance (EPR) spectrum of a prototypical nitroxide radical. To this end, we propose and validate also an automated protocol to extract and weight simulation snapshots, making use of a continuous description of the strength of solute-solvent hydrogen bridges. While further developments are being worked on, the effectiveness of our approach, even in its present form, is demonstrated by the accuracy of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional QM/MM models, without any appreciable deterioration of accuracy.

Analysis of L-DOPA and droxidopa binding to human ß2-adrenergic receptor.

Over the last two decades, an increasing number of studies has been devoted to a deeper understanding of the molecular process involved in the binding of various agonists and antagonists to active and inactive conformations of ß2-adrenergic receptor (ß2AR). The 3.2 Å x-ray crystal structure of human ß2AR active state in combination with the endogenous low affinity agonist adrenaline offers an ideal starting structure for studying the binding of various catecholamines to adrenergic receptors. We show that molecular docking of levodopa (L-DOPA) and droxidopa into rigid and flexible ß2AR models leads for both ligands to binding anchor sites comparable to those experimentally reported for adrenaline, namely D113/N312 and S203/S204/S207 side chains. Both ligands have a hydrogen bond network that is extremely similar to those of noradrenaline and dopamine. Interestingly, redocking neutral and protonated versions of adrenaline to rigid and flexible ß2AR models results in binding poses that are more energetically stable and distinct from the x-ray crystal structure. Similarly, lowest energy conformations of noradrenaline and dopamine generated by docking into flexible ß2AR models had binding free energies lower than those of best poses in rigid receptor models. Furthermore, our findings show that L-DOPA and droxidopa molecules have binding affinities comparable to those predicted for adrenaline, noradrenaline, and dopamine, which are consistent with previous experimental and computational findings and supported by the molecular dynamics simulations of ß2AR-ligand complexes performed here.

Integration of theory, simulation, artificial intelligence and virtual reality: a four-pillar approach for reconciling accuracy and interpretability in computational spectroscopy.

The established pillars of computational spectroscopy are theory and computer based simulations. Recently, artificial intelligence and virtual reality are becoming the third and fourth pillars of an integrated strategy for the investigation of complex phenomena. The main goal of the present contribution is the description of some new perspectives for computational spectroscopy, in the framework of a strategy in which computational methodologies at the state of the art, high-performance computing, artificial intelligence and virtual reality tools are integrated with the aim of improving research throughput and achieving goals otherwise not possible. Some of the key tools (e.g., continuous molecular perception model and virtual multifrequency spectrometer) and theoretical developments (e.g., non-periodic boundaries, joint variational-perturbative models) are shortly sketched and their application illustrated by means of representative case studies taken from recent work by the authors. Some of the results presented are already well beyond the state of the art in the field of computational spectroscopy, thereby also providing a proof of concept for other research fields.

4-Fluoro-Threonine: From Diastereoselective Synthesis to pH-Dependent Conformational Equilibrium in Aqueous Solution.

4-Fluoro-threonine, the only fluoro amino acid of natural origin discovered so far, is an interesting target for both synthetic and theoretical investigations. In this work, we lay the foundation for spectroscopic characterization of 4-fluoro-threonine. First, we report a diastereoselective synthetic route, which is suitable to produce synthetic material for experimental characterization. The addition of the commercially available ethyl isocyanoacetate to benzyloxyacetaldehyde led to the corresponding benzyloxy-oxazoline, which was hydrolyzed and transformed into ethyl (4S*,5S*)-5-hydroxymethyl-2-oxo-4-oxazolidinecarboxylate in a few steps. Fluorination with diethylamino sulfur trifluoride (DAST) afforded ethyl (4S*,5S*)-5-fluoromethyl-2-oxo-4-oxazolidinecarboxylate, which was deprotected to give the desired diastereomerically pure 4-fluoro-threonine, in 8-10% overall yield. With the synthetic material in our hands, acid-base titrations have been carried out to determine acid dissociation constants and the isoelectric point, which is the testing ground for the theoretical analysis. We have used machine learning coupled with quantum chemistry at the state-of-the-art to analyze the conformational space of 4-fluoro-threonine, with the aim of gaining insights from the comparison of computational and experimental results. Indeed, we have demonstrated that our approach, which couples a last-generation double-hybrid density functional including empirical dispersion contributions with a model combining explicit first-shell molecules and a polarizable continuum for describing solvent effects, provides results and trends in remarkable agreement with experiments. Finally, the conformational analysis applied to fluoro amino acids represents an interesting study for the effect of fluorine on the stability and population of conformers.

Elusive Coordination of the Ag+ Ion in Aqueous Solution: Evidence for a Linear Structure.

X-ray absorption spectroscopy (XAS) has been employed to study the coordination of the Ag+ ion in aqueous solution. The conjunction of extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES) data analysis provided results suggesting the preference for a first shell linear coordination with a mean Ag-O bond distance of 2.34(2) Å, different from the first generally accepted tetrahedral model with a longer mean Ag-O bond distance. Ab initio molecular dynamics simulations with the Car-Parrinello approach (CPMD) were also performed and were able to describe the coordination of the hydrated Ag+ ion in aqueous solution in very good agreement with the experimental data. The high sensitivity for the closest environment of the photoabsorber of the EXAFS and XANES techniques, together with the long-range information provided by CPMD and large-angle X-ray scattering (LAXS), allowed us to reconstruct the three-dimensional model of the coordination geometry around the Ag+ ion in aqueous solution. The obtained results from experiments and theoretical simulations provided a complex picture with a certain amount of water molecules with high configurational disorder at distances comprised between the first and second hydration spheres. This evidence may have caused the proliferation of the coordination numbers that have been proposed so far for Ag+ in water. Altogether these data show how the description of the hydration of the Ag+ ion in aqueous solution can be complex, differently from other metal species where hydration structures can be described by clusters with well-defined geometries. This diffuse hydration shell causes the Ag-O bond distance in the linear [Ag(H2O)2]+ ion to be ca. 0.2 Å longer than in isolated ions in solid state.

Unsupervised search of low-lying conformers with spectroscopic accuracy: A two-step algorithm rooted into the island model evolutionary algorithm.

The fruitful interplay of high-resolution spectroscopy and quantum chemistry has a long history, especially in the field of small, semi-rigid molecules. However, in recent years, the targets of spectroscopic studies are shifting toward flexible molecules, characterized by a large number of closely spaced energy minima, all contributing to the overall spectrum. Here, artificial intelligence comes into play since it is at the basis of powerful unsupervised techniques for the exploration of soft degrees of freedom. Integration of such algorithms with a two-stage QM/QM' (Quantum Mechanical) exploration/refinement strategy driven by a user-friendly graphical interface is the topic of the present paper. We will address in particular: (i) the performances of different semi-empirical methods for the exploration step and (ii) the comparison between stochastic and meta-heuristic algorithms in achieving a cheap yet complete exploration of the conformational space for medium sized chromophores. As test cases, we choose three amino acids of increasing complexity, whose full conformer enumeration has been reached only very recently. Next, we show that systems in condensed phases can be treated at the same level and with the same efficiency when employing a polarizable continuum description of the solvent. Finally, the challenging issue represented by the vibrational circular dichroism spectra of some rhodium complexes with flexible ligands has been addressed, showing that our fully unsupervised approach leads to remarkable agreement with the experiment.

Computational Spectroscopy in Solution by Integration of Variational and Perturbative Approaches on Top of Clusterized Molecular Dynamics.

Multiscale QM/MM approaches have become the most suitable and effective methods for the investigation of spectroscopic properties of medium- or large-size chromophores in condensed phases. On these grounds, we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available tools. In the present paper, we report the latest developments of such an approach with specific reference to a general workplan starting with the addition of acetonitrile to the panel of solvents already available in the General Liquid Optimized Boundary (GLOB) model enforcing nonperiodic boundary conditions (NPBC). Next, the solvatochromic shifts induced by acetonitrile on both rigid (uracil and thymine) and flexible (thyrosine) chromophores have been studied introducing in our software a number of new features ranging from rigid-geometry NPBC molecular dynamics based on the quaternion formalism to a full integration of variational (ONIOM) and perturbative (perturbed matrix method (PMM)) approaches for describing different solute-solvent topologies and local fluctuations, respectively. Finally, thymine and uracil have been studied also in methanol to point out the generality of the computational strategy. While further developments are surely needed, the strengths of our integrated approach even in its present version are demonstrated by the accuracy of the results obtained by an unsupervised approach and coupled to a computational cost strongly reduced with respect to that of conventional QM/MM models without any appreciable accuracy deterioration.

Molecular Perception for Visualization and Computation: The Proxima Library.

Proxima is a molecular perception library designed with a double purpose: to be used with immersive molecular viewers (thus providing any required feature not supported by third party libraries) and to be integrated in workflow managers thus providing the functionalities needed for the first steps of molecular modeling studies. It thus stands at the boundary between visualization and computation. The purpose of the present article is to provide a general introduction to the first release of Proxima, describe its most significant features, and highlight its performance by means of some case studies. The current version of Proxima is available for evaluation purposes at https://bitbucket.org/sns-smartlab/proxima/src/master/.

Chemical promenades: Exploring potential-energy surfaces with immersive virtual reality.

The virtual-reality framework AVATAR (Advanced Virtual Approach to Topological Analysis of Reactivity) for the immersive exploration of potential-energy landscapes is presented. AVATAR is based on modern consumer-grade virtual-reality technology and builds on two key concepts: (a) the reduction of the dimensionality of the potential-energy surface to two process-tailored, physically meaningful generalized coordinates, and (b) the analogy between the evolution of a chemical process and a pathway through valleys (potential wells) and mountain passes (saddle points) of the associated potential energy landscape. Examples including the discovery of competitive reaction paths in simple A + BC collisional systems and the interconversion between conformers in ring-puckering motions of flexible rings highlight the innovation potential that augmented and virtual reality convey for teaching, training, and supporting research in chemistry.

Deciphering Copper Coordination in the Mammalian Prion Protein Amyloidogenic Domain.

Prions are pathological isoforms of the cellular prion protein that is responsible for transmissible spongiform encephalopathies (TSE). Cellular prion protein interacts with copper, Cu(II), through octarepeat and nonoctarepeat (non-OR) binding sites. The molecular details of Cu(II) coordination within the non-OR region are not well characterized yet. By the means of small angle x-ray scattering and x-ray absorption spectroscopic methods, we have investigated the effect of Cu(II) on prion protein folding and its coordination geometries when bound to the non-OR region of recombinant prion proteins (recPrP) from mammalian species considered resistant or susceptible to TSE. As the prion resistant model, we used ovine recPrP (OvPrP) carrying the protective polymorphism at residues A136, R154, and R171, whereas as TSE-susceptible models, we employed OvPrP with V136, R154, and Q171 polymorphism and bank vole recPrP. Our analysis reveals that Cu(II) affects the structural plasticity of the non-OR region, leading to a more compacted conformation. We then identified two Cu(II) coordination geometries: in the type 1 coordination observed in OvPrP at residues A136, R154, and R171, the metal is coordinated by four residues; conversely, the type 2 coordination is present in OvPrP with V136, R154, and Q171 and bank vole recPrP, where Cu(II) is coordinated by three residues and by one water molecule, making the non-OR region more exposed to the solvent. These changes in copper coordination affect the recPrP amyloid aggregation. This study may provide new insights into the molecular mechanisms governing the resistance or susceptibility of certain species to TSE.

Two-level stochastic search of low-energy conformers for molecular spectroscopy: implementation and validation of MM and QM models.

The search for stationary points in the molecular potential energy surfaces (PES) is a problem of increasing relevance in different fields of molecular sciences especially for large, flexible systems characterized by several large-amplitude internal motions leading to shallow minima with comparable energies and separated by small barriers. After structural biology and medicinal chemistry, also high-resolution molecular spectroscopy, which is the focus of our research activity, is nowadays shifting its attention to this kind of molecular systems. In such circumstances, accurate geometrical structures and relative stabilities of all these minima are a mandatory prerequisite for the vis-à-vis comparison between computed and experimental spectra. This task raises, in turn, the problem of the best compromise between accuracy and feasibility. In our opinion, a promising route is offered by a two-level stochastic search in which a relatively inexpensive MM or QM method is used in the initial search, followed by single point energy evaluation at a higher QM level of a relatively large number of low-energy structures in order to select a final short-list of candidates, whose geometries are fully optimized at the higher QM level. Finally, the relative stabilities and properties of the final short-list of energy minima can be computed by a state-of-the-art QM approach. This strategy defines a general two-level search/three-level evaluation approach, which can be finely tuned in terms of the accuracy of the sought results. Setup of the procedure, interface with a general purpose electronic structure code and validation of the most effective low-level methods for some representative molecular systems (three already well characterized and one new) ended up with a general, robust and user-friendly tool, which can be easily used and extended also by non-specialists to aid experimental spectroscopic studies.