Allosteric transcription factor (aTF) based biosensors can be used to engineer genetic circuits for a wide range of applications. The literature and online databases contain hundreds of experimentally validated molecule-TF pairs; however, the knowledge is scattered and often incomplete. Additionally, compared to the number of compounds that can be produced in living systems, those with known associated TF-compound interactions are low. For these reasons, new tools that help researchers find new possible TF-ligand pairs are called for. In this work, we present Sensbio, a computational tool that through similarity comparison against a TF-ligand reference database, is able to identify putative transcription factors that can be activated by a given input molecule. In addition to the collection of algorithms, an online application has also been developed, together with a predictive model created to find new possible matches based on machine learning.

Summary: Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries. Availability and implementation: The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. PartsGenie is introduced to facilitate the computational design of such synthetic biology parts, bridging the gap between optimization tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language, and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a number of DNA optimization algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimization and dissemination of reusable synthetic biology parts through an integrated application. Availability and implementation: PartsGenie is freely available at https://parts.synbiochem.co.uk.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

MOTIVATION: Enzyme promiscuity, a property with practical applications in biotechnology and synthetic biology, has been related to the evolvability of enzymes. At the molecular level, several structural mechanisms have been linked to enzyme promiscuity in enzyme families. However, it is at present unclear to what extent these observations can be generalized. Here, we introduce for the first time a method for predicting catalytic and substrate promiscuity using a graph-based representation known as molecular signature. RESULTS: Our method, which has an accuracy of 85% for the non-redundant KEGG database, is also a powerful analytical tool for characterizing structural determinants of protein promiscuity. Namely, we found that signatures with higher contribution to the prediction of promiscuity are uniformly distributed in the protein structure of promiscuous enzymes. In contrast, those signatures that act as promiscuity determinants are significantly depleted around non-promiscuous catalytic sites. In addition, we present the study of the enolase and aminotransferase superfamilies as illustrative examples of characterization of promiscuous enzymes within a superfamily and achievement of enzyme promiscuity by protein reverse engineering. Recognizing the role of enzyme promiscuity in the process of natural evolution of enzymatic function can provide useful hints in the design of directed evolution experiments. We have developed a method with potential applications in the guided discovery and enhancement of latent catalytic capabilities surviving in modern enzymes. AVAILABILITY: http://www.issb.genopole.fr~faulon.

MOTIVATION: Protein dynamics is believed to influence protein function through a variety of mechanisms, some of which are not fully understood. Thus, prediction of protein flexibility from sequence or structural characteristics would assist in comprehension of the ways dynamics is linked to function, and would be important in protein modeling and design. In particular, quantitative description of side-chain dynamics would allow us to understand the role of side-chain flexibility in different functional processes, such as protein-ligand and protein-protein interactions. RESULTS: Using a dataset of 18 proteins, we trained a neural network for the prediction of methyl-bearing side-chain dynamics as described by the methyl side-chain generalized order parameters (S(2)) inferred from NMR data. The network uses 10 input parameters extracted from 3D structures. The average correlation coefficient between the experimental and predicted generalized order parameters is r = 0.71 +/- 0.029. Further analysis revealed that the order parameter depends more strongly on the methyl carbon packing density, the methyl carbon distance to the C(alpha) atom, and the knowledge-based pair-wise contact potential between the methyl carbon and neighboring amino acids. In general, we observed an improvement in the prediction of methyl order parameters by our network in comparison with molecular dynamics simulations. The sensitivity of the predictions to minor structural changes was illustrated in two examples (calmodulin and barnase) by comparing the S(2) predictions for the unbound and ligand-bound structures. The method was able to correctly predict most of the significant changes in side-chain dynamics upon ligand binding, and identified some residues involved in long-range communications or protein-ligand binding. AVAILABILITY: http://epigenomique.genopole.fr/ approximately carbonell

Domains are the building blocks of proteins and play a crucial role in protein-protein interactions. Here, we propose a new approach for the analysis and prediction of domain-domain interfaces. Our method, which relies on the representation of domains as residue-interacting networks, finds an optimal decomposition of domain structures into modules. The resulting modules comprise highly cooperative residues, which exhibit few connections with other modules. We found that non-overlapping binding sites in a domain, involved in different domain-domain interactions, are generally contained in different modules. This observation indicates that our modular decomposition is able to separate protein domains into regions with specialized functions. Our results show that modules with high modularity values identify binding site regions, demonstrating the predictive character of modularity. Furthermore, the combination of modularity with other characteristics, such as sequence conservation or surface patches, was found to improve our predictions. In an attempt to give a physical interpretation to the modular architecture of domains, we analyzed in detail six examples of protein domains with available experimental binding data. The modular configuration of the TEM1-beta-lactamase binding site illustrates the energetic independence of hotspots located in different modules and the cooperativity of those sited within the same modules. The energetic and structural cooperativity between intramodular residues is also clearly shown in the example of the chymotrypsin inhibitor, where non-binding site residues have a synergistic effect on binding. Interestingly, the binding site of the T cell receptor beta chain variable domain 2.1 is contained in one module, which includes structurally distant hot regions displaying positive cooperativity. These findings support the idea that modules possess certain functional and energetic independence. A modular organization of binding sites confers robustness and flexibility to the performance of the functional activity, and facilitates the evolution of protein interactions.