alessandro.paiardini@uniroma1.it or giacomo.janson@uniroma1.it

Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle we train a generative adversarial network based on a transformer architecture with self-attention on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent coarse-grained ensembles for sequences that are not present in the training set demonstrating that transferability can be achieved beyond the limited training data. We also retrain idpGAN on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.

Summary The PyMod project is designed to act as a fully integrated interface between the popular molecular graphics viewer PyMOL, and some of the most frequently used tools for structural bioinformatics, e.g. BLAST, HMMER, Clustal, MUSCLE, PSIPRED, DOPE and MODELLER. Here we report its latest release, PyMod 3, which has been completely renewed with a graphical interface written in PyQt, to make it compatible with the most recent PyMOL versions, and has been extended with a large set of new functionalities compared to its predecessor, i.e. PyMod 2. Starting from the amino acid sequence of a target protein, users can take advantage of PyMod 3 to carry out all the steps of the homology modeling process (i.e., template searching, target-template sequence alignment, model building and quality assessment). Additionally, the integrated tools in PyMod 3 may also be used alone, in order to extend PyMOL with a wide range of capabilities. Sequence similarity searches, multiple sequence/structure alignment building, phylogenetic trees and evolutionary conservation analyses, domain parsing, single/multiple chains and loop modeling can be performed in the PyMod 3/PyMOL environment. Availability A cross-platform PyMod 3 installer package for Windows, Linux and Mac OS X, and a complete user guide with tutorials, are available at https://github.com/pymodproject/pymod. Supplementary information online-only Supplementary data available at the journal's web site.

Nutrients such as amino acids play key roles in shaping the metabolism of microorganisms in natural environments and in host-pathogen interactions. Beyond taking part to cellular metabolism and to protein synthesis, amino acids are also signaling molecules able to influence group behavior in microorganisms, such as biofilm formation. This lifestyle switch involves complex metabolic reprogramming controlled by local variation of the second messenger 3', 5'-cyclic diguanylic acid (c-di-GMP). The intracellular levels of this dinucleotide are finely tuned by the opposite activity of dedicated diguanylate cyclases (GGDEF signature) and phosphodiesterases (EAL and HD-GYP signatures), which are usually allosterically controlled by a plethora of environmental and metabolic clues. Among the genes putatively involved in controlling c-di-GMP levels in P. aeruginosa, we found that the multidomain transmembrane protein PA0575, bearing the tandem signature GGDEF-EAL, is an l-arginine sensor able to hydrolyse c-di-GMP. Here, we investigate the basis of arginine recognition by integrating bioinformatics, molecular biophysics and microbiology. Although the role of nutrients such as l-arginine in controlling the cellular fate in P. aeruginosa (including biofilm, pathogenicity and virulence) is already well established, we identified the first l-arginine sensor able to link environment sensing, c-di-GMP signaling and biofilm formation in this bacterium.

Modulation of the interaction of regulatory 14-3-3 proteins to their physiological partners through small cell-permeant molecules is a promising strategy to control cellular processes where 14-3-3s are engaged. Here, we show that the fungal phytotoxin fusicoccin (FC), known to stabilize 14-3-3 association to the plant plasma membrane H 1 -ATPase, is able to stabilize 14-3-3 interaction to several client proteins with a mode III binding motif. Isothermal titration calorimetry analysis of the interaction between 14-3-3s and different peptides reproducing a mode III binding site demonstrated the FC ability to stimulate 14-3-3 the association. Moreover, molecular docking studies provided the structural rationale for the differential FC effect, which exclusively depends on the biochemical properties of the residue in peptide C-terminal position. Our study proposes FC as a promising tool to control cellular processes regulated by 14-3-3 proteins, opening new perspectives on its potential pharmacological applications. V C 2014 IUBMB Life, 66(1): [52][53][54][55][56][57][58][59][60][61][62] 2014