Redox enzymes, which catalyze reactions involving electron transfers in living organisms, are very promising components of biotechnological devices, and can be envisioned for sensing applications as well as for energy conversion. In this context, one of the most significant challenges is to achieve efficient direct electron transfer by tunneling between enzymes and conductive surfaces. Based on various examples of bioelectrochemical studies described in the recent literature, this review discusses the issue of enzyme immobilization at planar electrode interfaces. The fundamental importance of controlling enzyme orientation, how to obtain such orientation, and how it can be verified experimentally or by modeling are the three main directions explored. Since redox enzymes are sizable proteins with anisotropic properties, achieving their functional immobilization requires a specific and controlled orientation on the electrode surface. All the factors influenced by this orientation are described, ranging from electronic conductivity to efficiency of substrate supply. The specificities of the enzymatic molecule, surface properties, and dipole moment, which in turn influence the orientation, are introduced. Various ways of ensuring functional immobilization through tuning of both the enzyme and the electrode surface are then described. Finally, the review deals with analytical techniques that have enabled characterization and quantification of successful achievement of the desired orientation. The rich contributions of electrochemistry, spectroscopy (especially infrared spectroscopy), modeling, and microscopy are featured, along with their limitations.

The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown. Information obtained on the structure of macromolecular complexes is important for identifying functionally important partners but also for determining how such interactions will be perturbed by natural or engineered site mutations. Hence, to fully understand or control biological processes we need to predict in the most accurate manner protein interfaces for a protein structure, possibly without knowing its partners. Joint Evolutionary Trees (JET) is a method designed to detect very different types of interactions of a protein with another protein, ligands, DNA, and RNA. It uses a carefully designed sampling method, making sequence analysis more sensitive to the functional and structural importance of individual residues, and a clustering method parametrized on the target structure for the detection of patches on protein surfaces and their extension into predicted interaction sites. JET is a large-scale method, highly accurate and potentially applicable to search for protein partners.