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      Breaking the DFT Energy Bias Caused by Intramolecular Hydrogen‐Bonding Interactions with MESSI, A Structural Elucidation Method Inspired by Wisdom of Crowd Theory**

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          Abstract

          The use of quantum‐based NMR methods to complement and guide the connectivity and stereochemical assignment of natural and unnatural products has grown enormously. One of the unsolved problems is related to the improper calculation of the conformational landscape of flexible molecules that have functional groups capable of generating a complex network of intramolecular H‐bonding (IHB) interactions. Here the authors present MESSI (Multi‐Ensemble Strategy for Structural Identification), a method inspired by the wisdom of the crowd theory that breaks with the traditional mono‐ensemble approach. By including independent mappings of selected artificially manipulated ensembles, MESSI greatly improves the sense of the assignment by neutralizing potential energy biases.

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          Most cited references36

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          Wisdom of crowds for robust gene network inference

          Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
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            Computational prediction of 1H and 13C chemical shifts: a useful tool for natural product, mechanistic, and synthetic organic chemistry.

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              Beyond DP4: an Improved Probability for the Stereochemical Assignment of Isomeric Compounds using Quantum Chemical Calculations of NMR Shifts.

              The DP4 probability is one of the most sophisticated and popular approaches for the stereochemical assignment of organic molecules using GIAO NMR chemical shift calculations when only one set of experimental data is available. In order to improve the performance of the method, we have developed a modified probability (DP4+), whose main differences from the original DP4 are the inclusion of unscaled data and the use of higher levels of theory for the NMR calculation procedure. With these modifications, a significant improvement in the overall performance was achieved, providing accurate and confident results in establishing the stereochemistry of 48 challenging isomeric compounds.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Chemistry – A European Journal
                Chemistry A European J
                Wiley
                0947-6539
                1521-3765
                June 22 2023
                May 03 2023
                June 22 2023
                : 29
                : 35
                Affiliations
                [1 ] Instituto de Química Rosario (IQUIR CONICET-UNR) and Facultad de Ciencias Bioquímicas y Farmacéuticas Universidad Nacional de Rosario Suipacha 531 S2002LRK) Rosario (República Argentina
                [2 ] Instituto de Investigaciones en Ingeniería Ambiental Química y Biotecnología Aplicada (INGEBIO) Facultad de Química e Ingeniería del Rosario Pontificia Universidad Católica Argentina S2002QEO Rosario Argentina
                Article
                10.1002/chem.202300420
                36973182
                e15818f6-18d8-40e9-990f-30d0885ee4dc
                © 2023

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