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
Highlights d pCharme is the chromatin-retained isoform of the musclespecific Charme lncRNA d Intronic signals coordinate the association of pCharme with MATR3 and PTBP1 d The particle assembly prompts pCharme intron-1 chromatin retention d Deletion of the intron-1 by CRISPR-Cas9 leads to heart defects in mouse
Sulfur-containing amino acids play essential roles in many organisms. The protozoan parasite Toxoplasma gondii includes the genes for cystathionine β-synthase and cystathionine γ-lyase (TgCGL), as well as for cysteine synthase, which are crucial enzymes of the transsulfuration and de novo pathways for cysteine biosynthesis, respectively. These enzymes are specifically expressed in the oocyst stage of T. gondii. However, their functionality has not been investigated. Herein, we expressed and characterized the putative CGL from T. gondii. Recombinant TgCGL almost exclusively catalyses the α,γ-hydrolysis of l-cystathionine to form l-cysteine and displays marginal reactivity toward l-cysteine. Structure-guided homology modelling revealed two striking amino acid differences between the human and parasite CGL active-sites (Glu59 and Ser340 in human to Ser77 and Asn360 in toxoplasma). Mutation of Asn360 to Ser demonstrated the importance of this residue in modulating the specificity for the catalysis of α,β- versus α,γ-elimination of l-cystathionine. Replacement of Ser77 by Glu completely abolished activity towards l-cystathionine. Our results suggest that CGL is an important functional enzyme in T. gondii, likely implying that the reverse transsulfuration pathway is operative in the parasite; we also probed the roles of active-site architecture and substrate binding conformations as determinants of reaction specificity in transsulfuration enzymes.
Protein structure refinement is the last step in protein structure prediction pipelines.Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here.We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.