Accounting for Large Amplitude Protein Deformation during in Silico Macromolecular Docking

Rapid progress of theoretical methods and computer calculation resources has turned in silico methods into a conceivable tool to predict the 3D structure of macromolecular assemblages, starting from the structure of their separate elements. Still, some classes of complexes represent a real challenge for macromolecular docking methods. In these complexes, protein parts like loops or domains undergo large amplitude deformations upon association, thus remodeling the surface accessible to the partner protein or DNA. We discuss the problems linked with managing such rearrangements in docking methods and we review strategies that are presently being explored, as well as their limitations and success

In silico-in vitro screening of protein-protein interactions: towards the next generation of therapeutics.

International audienceProtein-protein interactions (PPIs) have a pivotal role in many biological processes suggesting that targeting macromolecular complexes will open new avenues for the design of the next generation of therapeutics. A wide range of "in silico methods" can be used to facilitate the design of protein-protein modulators. Among these methods, virtual ligand screening, protein-protein docking, structural predictions and druggable pocket predictions have become established techniques for hit discovery and optimization. In this review, we first summarize some key data about protein-protein interfaces and introduce some recently reported computer methods pertaining to the field. URLs for several recent free packages or servers are also provided. Then, we discuss four studies aiming at developing PPI modulators through the combination of in silico and in vitro screening experiments

On the Characterization and Selection of Diverse Conformational Ensembles, with Applications to Flexible Docking

To address challenging flexible docking problems, a number of docking algorithms pre-generate large collections of candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First, we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the afore-mentioned systems, we show that using the loops selected by our strategy can improve the result of the docking process

A Novel Acyl-CoA Beta-Transaminase Characterized from a Metagenome

BACKGROUND: Bacteria are key components in all ecosystems. However, our knowledge of bacterial metabolism is based solely on the study of cultivated organisms which represent just a tiny fraction of microbial diversity. To access new enzymatic reactions and new or alternative pathways, we investigated bacterial metabolism through analyses of uncultivated bacterial consortia. METHODOLOGY/PRINCIPAL FINDINGS: We applied the gene context approach to assembled sequences of the metagenome of the anaerobic digester of a municipal wastewater treatment plant, and identified a new gene which may participate in an alternative pathway of lysine fermentation. CONCLUSIONS: We characterized a novel, unique aminotransferase that acts exclusively on Coenzyme A (CoA) esters, and proposed a variant route for lysine fermentation. Results suggest that most of the lysine fermenting organisms use this new pathway in the digester. Its presence in organisms representative of two distinct bacterial divisions indicate that it may also be present in other organisms

Preexisting autoantibodies to type I IFNs underlie critical COVID-19 pneumonia in patients with APS-1

Patients with biallelic loss-of-function variants of AIRE suffer from autoimmune polyendocrine syndrome type-1 (APS-1) and produce a broad range of autoantibodies (auto-Abs), including circulating auto-Abs neutralizing most type I interferons (IFNs). These auto-Abs were recently reported to account for at least 10% of cases of life-threatening COVID-19 pneumonia in the general population. We report 22 APS-1 patients from 21 kindreds in seven countries, aged between 8 and 48 yr and infected with SARS-CoV-2 since February 2020. The 21 patients tested had auto-Abs neutralizing IFN-α subtypes and/or IFN-ω; one had anti-IFN-β and another anti-IFN-ε, but none had anti-IFN-κ. Strikingly, 19 patients (86%) were hospitalized for COVID-19 pneumonia, including 15 (68%) admitted to an intensive care unit, 11 (50%) who required mechanical ventilation, and four (18%) who died. Ambulatory disease in three patients (14%) was possibly accounted for by prior or early specific interventions. Preexisting auto-Abs neutralizing type I IFNs in APS-1 patients confer a very high risk of life-threatening COVID-19 pneumonia at any age

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk

Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism

Structural modeling and classification of active sites for guiding enzyme functional annotation

International audienceThe rate of enzyme functional characterization by experiments lags far behind the rate of gene sequence discovery, leading to an accumulation of proteins with no known function. Moreover, in public databases, function is extrapolated from a small number of proteins to all homologous members of a family resulting in 60% of superfamilies being mis-annotated [1]. Our institute has developed an integrated strategy based on in-silico prediction of enzymatic activities and in-vitro screening of enzymes for the discovery of various activities involved in microbial metabolism. As part of this strategy, we developed a structural bioinformatics method, called ASMC, for Active Sites Modeling and Clustering, which classifies proteins of a family into iso-functional sub-families and identifies functional amino acids responsible of specific enzymatic activities [2]. Experiments based on ASMC led to the unearthing of 14 potential new enzymatic activities for a family of unknown function, DUF849, and to the description of 3D-patterns for further annotation of sequences [3]. ASMC was also used to classify two phylogenetically unrelated protein families, MetX and MetA, for which we detected numerous mis-annotations in public databases. We re-examined nearly 10 000 MetA and MetX proteins using homology modeling and corrected the function for about 60% of them [4]. Our results show that the functional diversity within a protein family may be largely underestimated.References:[1] Schnoes, A. M., Brown, S. D., Dodevski, I. & Babbitt, P. C. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput Biol 5, e1000605 (2009).[2] de Melo-Minardi RC, Bastard K, Artiguenave F. Identification of subfamily-specific sites based on active sites modeling and clustering. Bioinformatics. 2010. 26(24):3075-82.[3] Bastard K, Smith AA, Vergne-Vaxelaire C, Perret A, Zaparucha A, De Melo-Minardi R, Mariage A, Boutard M, Debard A, Lechaplais C, Pelle C, Pellouin V, Perchat N, Petit JL, Kreimeyer A, Medigue C, Weissenbach J, Artiguenave F, De Berardinis V, Vallenet D, Salanoubat M. Revealing the hidden functional diversity of an enzyme family. Nat Chem Biol. 2014. 10(1):42-9.[4] Bastard K, Perret A, Mariage A, Bessonnet T, Pinet-Turpault A, Petit JL, Darii E, Bazire P, Vergne-Vaxelaire C, Brewee C, Debard A, Pellouin V, Besnard-Gonnet M, Artiguenave F, Médigue C, Vallenet D, Danchin A, Zaparucha A, Weissenbach J, Salanoubat M, de Berardinis V. Parallel evolution of non-homologous isofunctional enzymes in methionine biosynthesis. Nat Chem Biol. 2017. 13(8):858-866

Structural modeling and classification of active sites for guiding enzyme functional annotation

The rate of enzyme functional characterization by experiments lags far behind the rate of gene sequence discovery, leading to an accumulation of proteins with no known function. Moreover, in public databases, function is extrapolated from a small number of proteins to all homologous members of a family resulting in 60% of superfamilies being mis-annotated [1]. Our institute has developed an integrated strategy based on in-silico prediction of enzymatic activities and in-vitro screening of enzymes for the discovery of various activities involved in microbial metabolism. As part of this strategy, we developed a structural bioinformatics method, called ASMC, for Active Sites Modeling and Clustering, which classifies proteins of a family into iso-functional sub-families and identifies functional amino acids responsible of specific enzymatic activities [2]. Experiments based on ASMC led to the unearthing of 14 potential new enzymatic activities for a family of unknown function, DUF849, and to the description of 3D-patterns for further annotation of sequences [3]. ASMC was also used to classify two phylogenetically unrelated protein families, MetX and MetA, for which we detected numerous mis-annotations in public databases. We re-examined nearly 10 000 MetA and MetX proteins using homology modeling and corrected the function for about 60% of them [4]. Our results show that the functional diversity within a protein family may be largely underestimated.References:[1] Schnoes, A. M., Brown, S. D., Dodevski, I. & Babbitt, P. C. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput Biol 5, e1000605 (2009).[2] de Melo-Minardi RC, Bastard K, Artiguenave F. Identification of subfamily-specific sites based on active sites modeling and clustering. Bioinformatics. 2010. 26(24):3075-82.[3] Bastard K, Smith AA, Vergne-Vaxelaire C, Perret A, Zaparucha A, De Melo-Minardi R, Mariage A, Boutard M, Debard A, Lechaplais C, Pelle C, Pellouin V, Perchat N, Petit JL, Kreimeyer A, Medigue C, Weissenbach J, Artiguenave F, De Berardinis V, Vallenet D, Salanoubat M. Revealing the hidden functional diversity of an enzyme family. Nat Chem Biol. 2014. 10(1):42-9.[4] Bastard K, Perret A, Mariage A, Bessonnet T, Pinet-Turpault A, Petit JL, Darii E, Bazire P, Vergne-Vaxelaire C, Brewee C, Debard A, Pellouin V, Besnard-Gonnet M, Artiguenave F, Médigue C, Vallenet D, Danchin A, Zaparucha A, Weissenbach J, Salanoubat M, de Berardinis V. Parallel evolution of non-homologous isofunctional enzymes in methionine biosynthesis. Nat Chem Biol. 2017. 13(8):858-866