Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.

Residues in proteins that are in close spatial proximity are more prone to covariate as their interactions are likely to be preserved due to structural and evolutionary constraints. If we can detect and quantify such covariation, physical contacts may then be predicted in the structure of a protein solely from the sequences that decorate it. To carry out such predictions, and following the work of others, we have implemented a multivariate Gaussian model to analyze correlation in multiple sequence alignments. We have explored and tested several numerical encodings of amino acids within this model. We have shown that 1D encodings based on amino acid biochemical and biophysical properties, as well as higher dimensional encodings computed from the principal components of experimentally derived mutation/substitution matrices, do not perform as well as a simple twenty dimensional encoding with each amino acid represented with a vector of one along its own dimension and zero elsewhere. The optimum obtained from representations based on substitution matrices is reached by using 10 to 12 principal components; the corresponding performance is less than the performance obtained with the 20-dimensional binary encoding. We highlight also the importance of the prior when constructing the multivariate Gaussian model of a multiple sequence alignment.

The non-linear problem of simulating the structural transition between two known forms of a macromolecule still remains a challenge in structural biology. The problem is usually addressed in an approximate way using 'morphing' techniques, which are linear interpolations of either the Cartesian or the internal coordinates between the initial and end states, followed by energy minimization. Here we describe a web tool that implements a new method to calculate the most probable trajectory that is exact for harmonic potentials; as an illustration of the method, the classical Calpha-based Elastic Network Model (ENM) is used both for the initial and the final states but other variants of the ENM are also possible. The Langevin equation under this potential is solved analytically using the Onsager and Machlup action minimization formalism on each side of the transition, thus replacing the original non-linear problem by a pair of linear differential equations joined by a non-linear boundary matching condition. The crossover between the two multidimensional energy curves around each state is found numerically using an iterative approach, producing the most probable trajectory and fully characterizing the transition state and its energy. Jobs calculating such trajectories can be submitted on-line at: http://lorentz.dynstr.pasteur.fr/joel/index.php.

The pentameric ligand-gated ion channel (pLGIC) from Gloeobacter violaceus (GLIC) has provided insightful structure-function views on the permeation process and the allosteric regulation of the pLGICs family. However, GLIC is activated by pH instead of a neurotransmitter and a clear picture for the gating transition driven by protons is still lacking. We used an electrostatics-based (finite difference Poisson-Boltzmann/Debye-Hückel) method to predict the acidities of all aspartic and glutamic residues in GLIC, both in its active and closed-channel states. Those residues with a predicted pK_a close to the experimental pH₅₀ were individually replaced by alanine and the resulting variant receptors were titrated by ATR/FTIR spectroscopy. E35, located in front of loop F far away from the orthosteric site, appears as the key proton sensor with a measured individual pK_a at 5.8. In the GLIC open conformation, E35 is connected through a water-mediated hydrogen-bond network first to the highly conserved electrostatic triad R192-D122-D32 and then to Y197-Y119-K248, both located at the extracellular domain-transmembrane domain interface. The second triad controls a cluster of hydrophobic side chains from the M2-M3 loop that is remodeled during the gating transition. We solved 12 crystal structures of GLIC mutants, 6 of them being trapped in an agonist-bound but nonconductive conformation. Combined with previous data, this reveals two branches of a continuous network originating from E35 that reach, independently, the middle transmembrane region of two adjacent subunits. We conclude that GLIC's gating proceeds by making use of loop F, already known as an allosteric site in other pLGICs, instead of the classic orthosteric site.

General anaesthetics have enjoyed long and widespread use but their molecular mechanism of action remains poorly understood. There is good evidence that their principal targets are pentameric ligand-gated ion channels (pLGICs) such as inhibitory GABA(A) (γ-aminobutyric acid) receptors and excitatory nicotinic acetylcholine receptors, which are respectively potentiated and inhibited by general anaesthetics. The bacterial homologue from Gloeobacter violaceus (GLIC), whose X-ray structure was recently solved, is also sensitive to clinical concentrations of general anaesthetics. Here we describe the crystal structures of the complexes propofol/GLIC and desflurane/GLIC. These reveal a common general-anaesthetic binding site, which pre-exists in the apo-structure in the upper part of the transmembrane domain of each protomer. Both molecules establish van der Waals interactions with the protein; propofol binds at the entrance of the cavity whereas the smaller, more flexible, desflurane binds deeper inside. Mutations of some amino acids lining the binding site profoundly alter the ionic response of GLIC to protons, and affect its general-anaesthetic pharmacology. Molecular dynamics simulations, performed on the wild type (WT) and two GLIC mutants, highlight differences in mobility of propofol in its binding site and help to explain these effects. These data provide a novel structural framework for the design of general anaesthetics and of allosteric modulators of brain pLGICs.