Machine learning based prediction of esterases' promiscuity

Els enzims són de gran interès per a la majoria de les indústries, no obstant la seva caracterització en el laboratori és costosa i molt laboriosa, fet que ha impulsat el desenvolupament de tecnologies de predicció de les activitats dels enzims. Malgrat això, els enzims industrials han de tenir unes propietats molt específiques com per exemple alta especificitat, alta activitat en condicions no biològiques i alta promiscuitat, característiques que no estan ben cobertes per les eines de predicció actuals. Per aquest motiu, amb aquest projecte, s'intenta mitigar el problema creant classificadors binaris que poden predir la promiscuitat de les esterases.Enzymes are of great interest for a vast variety of industries; however, the experimental characterization is very time consuming and expensive. Moreover, industrial enzymes need to adapt to nonbiological conditions while maintaining high activity, promiscuity and stereo-selectivity, properties that are not well covered, currently, by prediction technologies which means that their characterization still relies solely on experimentation. This project has the intention of mitigating the problem by developing binary classifiers and multi-classifiers that can predict the promiscuity of esterases, one of the many industrially relevant enzymes

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.This work has also been supported by predoctoral fellowships FPU19/00608 and PRE2020-091825, and the PID2019-106370RBI00/AEI/10.13039/501100011033 grant from the Spanish Ministry of Science and Innovation.Peer ReviewedPostprint (author's final draft

EP-Pred: A machine learning tool for bioprospecting promiscuous ester hydrolases

When bioprospecting for novel industrial enzymes, substrate promiscuity is a desirable property that increases the reusability of the enzyme. Among industrial enzymes, ester hydrolases have great relevance for which the demand has not ceased to increase. However, the search for new substrate promiscuous ester hydrolases is not trivial since the mechanism behind this property is greatly influenced by the active site’s structural and physicochemical characteristics. These characteristics must be computed from the 3D structure, which is rarely available and expensive to measure, hence the need for a method that can predict promiscuity from sequence alone. Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases. Experimental results confirmed the validity of our method since all ten proteins were found to exhibit a broad substrate ambiguity.This study was conducted under the auspices of the FuturEnzyme and Oxipro Projects funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 101000327 and 101000607. We also acknowledge financial support under Grants PID2020-112758RB-I00 (M.F.), PID2019-106370RB-I00 (V.G.) and PDC2021-121534-I00 (M.F.) and PID2019-106370RB-I00/AEI/10.13039/501100011033 (A.R.-M.), from the Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (AEI) (Digital Object Identifier 10.13039/501100011033), Fondo Europeo de Desarrollo Regional (FEDER) and the European Union (“NextGen-erationEU/PRTR”), and Grant 2020AEP061 (M.F.) from the Agencia Estatal CSIC.Peer ReviewedPostprint (published version

Structural Elucidation and Engineering of a Bacterial Carbohydrate Oxidase

Flavin-dependent carbohydrate oxidases are valuable tools in biotechnological applications due to their high selectivity in the oxidation of carbohydrates. In this study, we report the biochemical and structural characterization of a recently discovered carbohydrate oxidase from the bacterium Ralstonia solanacearum, which is a member of the vanillyl alcohol oxidase flavoprotein family. Due to its exceptionally high activity toward N-acetyl-d-galactosamine and N-acetyl-d-glucosamine, the enzyme was named N-acetyl-glucosamine oxidase (NagOx). In contrast to most known (fungal) carbohydrate oxidases, NagOx could be overexpressed in a bacterial host, which facilitated detailed biochemical and enzyme engineering studies. Steady state kinetic analyses revealed that non-acetylated hexoses were also accepted as substrates albeit with lower efficiency. Upon determination of the crystal structure, structural insights into NagOx were obtained. A large cavity containing a bicovalently bound FAD, tethered via histidyl and cysteinyl linkages, was observed. Substrate docking highlighted how a single residue (Leu251) plays a key role in the accommodation of N-acetylated sugars in the active site. Upon replacement of Leu251 (L251R mutant), an enzyme variant was generated with a drastically modified substrate acceptance profile, tuned toward non-N-acetylated monosaccharides and disaccharides. Furthermore, the activity toward bulkier substrates such as the trisaccharide maltotriose was introduced by this mutation. Due to its advantage of being overexpressed in a bacterial host, NagOx can be considered a promising alternative engineerable biocatalyst for selective oxidation of monosaccharides and oligosaccharides.</p

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.This work has also been supported by predoctoral fellowships FPU19/00608 and PRE2020-091825, and the PID2019-106370RBI00/AEI/10.13039/501100011033 grant from the Spanish Ministry of Science and Innovation.Peer ReviewedPostprint (author's final draft

Machine learning based prediction of esterases' promiscuity

Els enzims són de gran interès per a la majoria de les indústries, no obstant la seva caracterització en el laboratori és costosa i molt laboriosa, fet que ha impulsat el desenvolupament de tecnologies de predicció de les activitats dels enzims. Malgrat això, els enzims industrials han de tenir unes propietats molt específiques com per exemple alta especificitat, alta activitat en condicions no biològiques i alta promiscuitat, característiques que no estan ben cobertes per les eines de predicció actuals. Per aquest motiu, amb aquest projecte, s'intenta mitigar el problema creant classificadors binaris que poden predir la promiscuitat de les esterases.Enzymes are of great interest for a vast variety of industries; however, the experimental characterization is very time consuming and expensive. Moreover, industrial enzymes need to adapt to nonbiological conditions while maintaining high activity, promiscuity and stereo-selectivity, properties that are not well covered, currently, by prediction technologies which means that their characterization still relies solely on experimentation. This project has the intention of mitigating the problem by developing binary classifiers and multi-classifiers that can predict the promiscuity of esterases, one of the many industrially relevant enzymes

Insights into peculiar fungal LPMO family members holding a short C-terminal sequence reminiscent of phosphate binding motifs

International audienceLytic polysaccharide monooxygenases (LPMOs) are taxonomically widespread copper-enzymes boosting biopolymers conversion (e.g. cellulose, chitin) in Nature. White-rot Polyporales, which are major fungal wood decayers, may possess up to 60 LPMO-encoding genes belonging to the auxiliary activities family 9 (AA9). Yet, the functional relevance of such multiplicity remains to be uncovered. Previous comparative transcriptomic studies of six Polyporales fungi grown on cellulosic substrates had shown the overexpression of numerous AA9-encoding genes, including some holding a C-terminal domain of unknown function (“X282”). Here, after carrying out structural predictions and phylogenetic analyses, we selected and characterized six AA9-X282s with different C-term modularities and atypical features hitherto unreported. Unexpectedly, after screening a large array of conditions, these AA9-X282s showed only weak binding properties to cellulose, and low to no cellulolytic oxidative activity. Strikingly, proteomic analysis revealed the presence of multiple phosphorylated residues at the surface of these AA9-X282s, including a conserved residue next to the copper site. Further analyses focusing on a 9 residues glycine-rich C-term extension suggested that it could hold phosphate-binding properties. Our results question the involvement of these AA9 proteins in the degradation of plant cell wall and open new avenues as to the divergence of function of some AA9 members