Combining aldolases and transaminases for the synthesis of 2‑amino-4-hydroxybutanoic acid

Amino acids are of paramount importance as chiral building blocks of life, for drug development in modern medicinal chemistry, and for the manufacture of industrial products. In this work, the stereoselective synthesis of (S)- and (R)-2-amino-4-hydroxybutanoic acid was accomplished using a systems biocatalysis approach comprising a biocatalytic one-pot cyclic cascade by coupling of an aldol reaction with an ensuing stereoselective transamination. A class II pyruvate aldolase from E. coli, expressed as a soluble fusion protein, in tandem with either an S- or R-selective, pyridoxal phosphate dependent transaminase was used as a catalyst to realize the conversion, with formaldehyde and alanine being the sole starting materials. Interestingly, the class II pyruvate aldolase was found to tolerate formaldehyde concentrations of up to 1.4 M. The cascade system was found to reach product concentrations for (S)- or (R)-2-amino-4-hydroxybutanoic acid of at least 0.4 M, rendering yields between 86% and >95%, respectively, productivities of >80 g L–1 d–1, and ee values of >99%.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 635595 (CarbaZymes), the Ministerio de Economía y Competitividad (MINECO), the Fondo Europeo de Desarrollo Regional (FEDER) (grant no. CTQ2015-63563-R to P.C.), and COST action CM1303 Systems Biocatalysis.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer reviewe

Biocatalytic Aldol Addition of Simple Aliphatic Nucleophiles to Hydroxyaldehydes

This ACS article is provided to You under the terms of this Standard ACS AuthorChoice/Editors' Choice usage agreement between You and the American Chemical Society ("ACS")(https://pubs.acs.org/page/policy/authorchoice_termsofuse.html)Asymmetric aldol addition of simple aldehydes and ketones to electrophiles is a cornerstone reaction for the synthesis of unusual sugars and chiral building blocks. We investigated -fructose-6-phosphate aldolase from E. coli (FSA) D6X variants as catalysts for the aldol additions of ethanal and nonfunctionalized linear and cyclic aliphatic ketones as nucleophiles to nonphosphorylated hydroxyaldehydes. Thus, addition of propanone, cyclobutanone, cyclopentanone, or ethanal to 3-hydroxypropanal or (S)- or (R)-3-hydroxybutanal catalyzed by FSA D6H and D6Q variants furnished rare deoxysugars in 8-77% isolated yields with high stereoselectivity (97:3 dr and >95% ee)

Inhibition of Sema-3A Promotes Cell Migration, Axonal Growth, and Retinal Ganglion Cell Survival

Semaphorin 3A (Sema-3A) is a secreted protein that deflects axons from inappropriate regions and induces neuronal cell death. Intravitreal application of polyclonal antibodies against Sema-3A prevents loss of retinal ganglion cells ensuing from axotomy of optic nerves. This suggested a therapeutic approach for neuroprotection via inhibition of the Sema-3A pathway.Funded by the EU seventh framework program, Grant Agreement #604884.Peer reviewe

Using MobilitApp to analyse multimodal trips of citizens in Barcelona

This project is based on the idea of further improving the implementation of MobilitApp. It is an Android application designed and developed by students of the ETSETB with the collaboration of the ATM of Barcelona. The project started in 2014 with one main objective: to optimise urban mobility. To do so, it uses a Machine Learning model based on collecting data from an accelerometer, a magnetometer and a gyroscope, to train it and be able to detect the transport used by its users. More specifically, it uses a Deep Learning model designed by members of the SISCOM research group of the UPC. The thesis starts at a point where MobilitApp is already able to generate User Info. These are the lines of data with information on how, when and where its users move. All this data is collected in files as a database and automatically the need arises to be able to process all this data in order to reach conclusions at project level. Basically, the aim of this thesis is to create an application capable of reading a database, selecting filters of gender, age, route start dates, route end dates, origin zones, destination zones and transport methods used, and with them create graphs representing all the information. With this, any member involved in the project can draw conclusions from the User Info obtained in a much more agile and user-friendly way.Este proyecto se basa en la idea de seguir mejorando la implementación de MobilitApp. Es una aplicación Android diseñada y desarrollada por estudiantes de la ETSETB junto con la colaboración de la ATM de Barcelona. El proyecto se inició en 2014 con un objetivo principal: optimizar la movilidad urbana. Para ello, se utiliza un modelo de Machine Learning basado en recoger datos de un acelerómetro, un magnetómetro y un giroscopio, para entrenarlo y ser capaz de detectar el transporte utilizado por sus usuarios. Más concretamente, se utiliza un modelo de Deep Learning diseñado por miembros del grupo de investigación SISCOM de la misma UPC. La tesis se inicia en un punto en el que MobilitApp ya es capaz de generar User Info. Estos son las líneas de datos con la información de como, cuando y donde se mueven sus usuarios. Todos estos datos se recogen en archivos a modo de base de datos y automáticamente surge la necesidad de poder procesar todos estos para poder llagar a conclusiones a nivel de proyecto. Básicamente, el objetivo de este trabajo es crear una aplicación capaz de leer una base de datos, seleccionar filtros de género, edad, fechas de inicio de ruta, fechas de final de ruta, zonas de origen, zonas de destino y métodos de transporte utilizados, y con ellos crear gráficos representando toda la información. Con esto, se consigue que cualquier miembro involucrado en el proyecto pueda interpretar grupos de User Info almacenados de una manera mucho más ágil y user-friendly.Aquest projecte es basa en la idea de seguir millorant la implementació de MobilitApp. És una aplicació Android dissenyada y desenvolupada per estudiants de la ETSETB junt amb la col·laboració de la ATM de Barcelona. El projecte es va iniciar al 2014 amb un objectiu principal: optimitzar la mobilitat urbana. Per fer-ho, s?utilitza un model de Machine Learning basat en recollir dades d?un acceleròmetre, un magnetòmetre i un giroscopi, per entrenar- lo i ser capaç de detectar el transport utilitzat pels seus usuaris. Més concretament, s?utilitza un model de Deep Learning dissenyat per els membres del grup d?investigació SISCOM de la mateixa UPC. La tesis s?inicia en un punt en el que MobilitApp ja és capaç de generar User Info. Són les línies de dades amb la informació de com, quan i on es mouen els seus usuaris. Totes aquestes dades es recullen en arxius a mode de base de dades i automàticament sorgeix la necessitat de poder processar totes aquestes per poder arribar a conclusions a nivell de projecte. Bàsicament, l?objectiu d?aquest treball és crear una aplicació capaç de llegir una base de dades i seleccionar filtres de gènere, edat, dates d?inici de ruta, dates de final de ruta, zones origen, zones destí i mètodes de transport utilitzats, i amb ells crear gràfics representant tota la informació recaptada. Amb això, s?aconsegueix que qualsevol membre involucrat en el projecte pugui interpretar els grups de User Info emmagatzemats d?una manera molt més àgil i user-friendly

Live‐Cell‐Templated Dynamic Combinatorial Chemistry

Dynamic covalent chemistry combines in a single step the screening and synthesis of ligands for biomolecular recognition. In order to do that, a chemical entity is used as template within a dynamic combinatorial library of interconverting species, so that the stronger binders are amplified due to the efficient interaction with the target. Here we employed whole A549 living cells as template in a dynamic mixture of imines, for which amplification reflects the efficient and selective interaction with the corresponding extracellular matrix. The amplified polyamine showed strong interaction with the A549 extracellular matrix in on‐cell NMR experiments, while combination of NMR, SPR, and molecular dynamics simulations in model systems provided insights on the molecular recognition event. Notably, our work pioneers the use of whole living cells in dynamic combinatorial chemistry, which paves the way towards the discovery of new bioactive molecules in a more biorelevant environment. Dynamic combinatorial chemistry (DCC) proposes the use of dynamic combinatorial libraries (DCL) for the generation of species able to exchange through reversible covalent bonds.1 These molecular systems are responsive to external stimuli by modification of the DCL composition,2 with the stabilized members being amplified at the expense of the other components in the mixture.3 Within the chemical biology field, the DCC approach has led to the discovery of new protein ligands,4 nucleic acids binders,5 and even replicators.6 For biological applications, it should be desirable that the conditions used for the DCC screening resemble those in the place of action.7 Inspired by Sander's comparison of DCC with the immune system,8 we envisioned targeting the extracellular matrix (ECM).9 The external surface of the cells is formed by a complex network of glycoproteins and anionic polysaccharides that is fundamental for processes such as cell communication,10 regeneration,11 metastasis,12 and host‐pathogen infection.13 The ECM is the first barrier for a molecule (i.e. a drug) to enter inside the cell; thus, navigating the ECM is fundamental in biomedicine and in chemical biology.14 However, the chemical and structural complexity of the ECM have hindered its detailed molecular characterization and frustrated the rational design of synthetic ligands.15 Paradoxically, the intrinsic complexity of the ECM offers an ideal playground for the realization of the self‐organizing features of DCC (Figure 1 A), which has demonstrated its power for the discovery of strong binders to challenging biomolecules.1, 2-4, 5-8 Considering our recent results in the identification of a strong heparin binder16 and the chemical similarity between heparin and the glycosaminoglycans (GAGs) of the ECM,14 we designed a library (Figure 1 B) combining spermine as a cationic polyamine scaffold17 with a set of aromatic aldehydes, which would mediate binding through CH–aryl interactions with the saccharide units.18 Thus, the dynamic mixture of imines (2XY ) obtained by the reaction between spermine (1 ) and an equimolecular mixture of four aromatic aldehydes (A , B , K , L ) was incubated with living cells and reduced in situ with NaBH3CN to the corresponding polyamines (3XY ). As an initial model, we used the A549 human lung adenocarcinoma cell line since the ECM of these cells is rich in anionic GAGs.19 The supernatant was analyzed by UPLC‐MS allowing the identification and quantification of each member of the library (Figure 1 C). The normalized area of the UPLC‐MS peaks for the reactions performed in the presence (A T) and in the absence (A 0) of cells was compared by the calculation of the corresponding amplification factors (AF=A T/A 0).We thank Prof. A. Messeguer for helpful discussion. The ICTS “NANOBIOSIS” (CAbS, IQAC‐CSIC, CIBER‐BBN) is acknowledged for the assistance and support with the use of OpenSPR. This work was supported by the Spanish Ministry of Science and Innovation/ Spanish Research Agency (MCI/AEI/FEDER, RTI2018‐096182‐B‐I00, CSIC13‐4E‐2076) and AGAUR (2017 SGR 208).Peer reviewe

Entropy-driven homochiral self-sorting of a dynamic library

A dynamic mixture of stereoisomeric macrocycles derived from glutamic acid displayed a homochiral self-selection when increasing the acetonitrile content of the aqueous mixed medium. The homochiral self-sorting required the anionic form of the side chains and increased at higher temperature, implying an entropic origin. Conformational analysis (NMR and MD simulations) allowed us to explain the observed behaviour. The results show that entropy can play a role in the homochiral self-sorting in adaptive bio-inspired chemical systems.The financial support from MINECO/FEDER (CTQ2012-38543-C03-03 and CTQ2015-70117-R projects) and AGAUR (2014 SGR 231) is gratefully acknowledged.Peer reviewe

Structure-guided redesign of D-fructose-6-phosphate aldolase from E. coli: remarkable activity and selectivity towards acceptor substrates by two-point mutation

Structure-guided re-design of the acceptor binding site of D-fructose-6-phosphate aldolase from E. coli leads to the construction of FSA A129S/A165G double mutant with an activity between 5- to >900-fold higher than that of wild-type towards N-Cbz-aminoaldehyde derivatives.Peer reviewe