Bioprospecting reveals class III ω-transaminases converting bulky ketones and environmentally relevant polyamines

Amination of bulky ketones, particularly in (R) configuration, is an attractive chemical conversion; however, known ω-transaminases (ω-TAs) show insufficient levels of performance. By applying two screening methods, we discovered 10 amine transaminases from the class III ω-TA family that were 38% to 76% identical to homologues. We present examples of such enzymes preferring bulky ketones over keto acids and aldehydes with stringent (S) selectivity. We also report representatives from the class III ω-TAs capable of converting (R) and (S) amines and bulky ketones and one that can convert amines with longer alkyl substituents. The preference for bulky ketones was associated with the presence of a hairpin region proximal to the conserved Arg414 and residues conforming and close to it. The outward orientation of Arg414 additionally favored the conversion of (R) amines. This configuration was also found to favor the utilization of putrescine as an amine donor, so that class III ω-TAs with Arg414 in outward orientation may participate in vivo in the catabolism of putrescine. The positioning of the conserved Ser231 also contributes to the preference for amines with longer alkyl substituents. Optimal temperatures for activity ranged from 45 to 65°C, and a few enzymes retained ≥50% of their activity in water-soluble solvents (up to 50% [vol/vol]). Hence, our results will pave the way to design, in the future, new class III ω-TAs converting bulky ketones and (R) amines for the production of high-value products and to screen for those converting putrescine

Decoding the ocean's microbiological secrets for marine enzyme biodiscovery

A global census of marine microbial life has been underway over the past several decades. During this period, there have been scientific breakthroughs in estimating microbial diversity and understanding microbial functioning and ecology. It is estimated that the ocean, covering 71% of the earth's surface with its estimated volume of about 2 x 10(18) m(3) and an average depth of 3800 m, hosts the largest population of microbes on Earth. More than 2 million eukaryotic and prokaryotic species are thought to thrive both in the ocean and on its surface. Prokaryotic cell abundances can reach densities of up to 10(12) cells per millilitre, exceeding eukaryotic densities of around 10(6) cells per millilitre of seawater. Besides their large numbers and abundance, marine microbial assemblages and their organic catalysts (enzymes) have a largely underestimated value for their use in the development of industrial products and processes. In this perspective article, we identified critical gaps in knowledge and technology to fast-track this development. We provided a general overview of the presumptive microbial assemblages in oceans, and an estimation of what is known and the enzymes that have been currently retrieved. We also discussed recent advances made in this area by the collaborative European Horizon 2020 project 'INMARE'

Gastrointestinal eosinophils in health, disease and functional disorders

Esterases receive special attention because of their wide distribution in biological systems and environments and their importance for physiology and chemical synthesis. The prediction of esterases’ substrate promiscuity level from sequence data and the molecular reasons why certain such enzymes are more promiscuous than others remain to be elucidated. This limits the surveillance of the sequence space for esterases potentially leading to new versatile biocatalysts and new insights into their role in cellular function. Here, we performed an extensive analysis of the substrate spectra of 145 phylogenetically and environmentally diverse microbial esterases, when tested with 96 diverse esters. We determined the primary factors shaping their substrate range by analyzing substrate range patterns in combination with structural analysis and protein–ligand simulations. We found a structural parameter that helps rank (classify) the promiscuity level of esterases from sequence data at 94% accuracy. This parameter, the active site effective volume, exemplifies the topology of the catalytic environment by measuring the active site cavity volume corrected by the relative solvent accessible surface area (SASA) of the catalytic triad. Sequences encoding esterases with active site effective volumes (cavity volume/SASA) above a threshold show greater substrate spectra, which can be further extended in combination with phylogenetic data. This measure provides also a valuable tool for interrogating substrates capable of being converted. This measure, found to be transferred to phosphatases of the haloalkanoic acid dehalogenase superfamily and possibly other enzymatic systems, represents a powerful tool for low-cost bioprospecting for esterases with broad substrate ranges, in large scale sequence data sets.C.C. thanks the Spanish Ministry of Economy, Industry and Competitiveness for a Ph.D. fellowship (Grant BES-2015-073829). This project received funding from the European Union’s Horizon 2020 research and innovation program [Blue Growth: Unlocking the potential of Seas and Oceans] under grant agreement no. 634486 (project acronym INMARE). This research was also supported by the European Community Projects MAGICPAH (FP7-KBBE-2009-245226), ULIXES (FP7-KBBE-2010-266473), and KILLSPILL (FP7-KBBE2012-312139) and grants BIO2011-25012, PCIN-2014-107, BIO2014-54494-R, and CTQ2016-79138-R from the Spanish Ministry of Economy, Industry and Competitiveness. The present investigation was also funded by the Spanish Ministry of Economy, Industry and Competitiveness within the ERA NET IB2, grant no. ERA-IB-14-030 (MetaCat), the UK Biotechnology and Biological Sciences Research Council (BBSRC), grant no. BB/M029085/1, and the German Research Foundation (FOR1296). R.B. and P.N.G. acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via the Welsh Government. O.V.G. and P.N.G. acknowledge the support of the Centre of Environmental Biotechnology Project funded by the European Regional Development Fund (ERDF) through the Welsh Government. A.Y. and A.S. gratefully acknowledge funding from Genome Canada (2009-OGI-ABC-1405) and the NSERC Strategic Network grant IBN. A.I.P. was supported by the Counseling of Economy and Employment of the Principality of Asturias, Spain (Grant FC-15-GRUPIN14-107). V.G. acknowledges the joint BSC-CRG-IRB Research Program in Computational Biology. The authors gratefully acknowledge financial support provided by the European Regional Development Fund (ERDF).We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer reviewe