FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at http://foldx.embl.de/. The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.

The FoldX web server: an online force field

'Reactive oxygen species' (ROS) is an umbrella term for an array of derivatives of molecular oxygen that occur as a normal attribute of aerobic life. Elevated formation of the different ROS leads to molecular damage, denoted as 'oxidative distress'. Here we focus on ROS at physiological levels and their central role in redox signalling via different post-translational modifications, denoted as 'oxidative eustress'. Two species, hydrogen peroxide (H2O2) and the superoxide anion radical (O2·-), are key redox signalling agents generated under the control of growth factors and cytokines by more than 40 enzymes, prominently including NADPH oxidases and the mitochondrial electron transport chain. At the low physiological levels in the nanomolar range, H2O2 is the major agent signalling through specific protein targets, which engage in metabolic regulation and stress responses to support cellular adaptation to a changing environment and stress. In addition, several other reactive species are involved in redox signalling, for instance nitric oxide, hydrogen sulfide and oxidized lipids. Recent methodological advances permit the assessment of molecular interactions of specific ROS molecules with specific targets in redox signalling pathways. Accordingly, major advances have occurred in understanding the role of these oxidants in physiology and disease, including the nervous, cardiovascular and immune systems, skeletal muscle and metabolic regulation as well as ageing and cancer. In the past, unspecific elimination of ROS by use of low molecular mass antioxidant compounds was not successful in counteracting disease initiation and progression in clinical trials. However, controlling specific ROS-mediated signalling pathways by selective targeting offers a perspective for a future of more refined redox medicine. This includes enzymatic defence systems such as those controlled by the stress-response transcription factors NRF2 and nuclear factor-κB, the role of trace elements such as selenium, the use of redox drugs and the modulation of environmental factors collectively known as the exposome (for example, nutrition, lifestyle and irradiation).

Reactive oxygen species (ROS) as pleiotropic physiological signalling agents.

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

/pdf/the-cluspro-web-server-for-protein-protein-docking-4ikcg72le9.pdf

The ClusPro web server for protein-protein docking.

Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

The Nrf2 regulatory network provides an interface between redox and intermediary metabolism

The present invention relates to a compound having affinity for a negatively charged phospholipid as well as to a detection molecule, to a conjugate and to a pharmaceutical composition comprising said compound. Generally speaking, the compound of the present invention is useful for specific recognition of lipid vectors. It may be used in engineering and in the generation of compound for recognizing and sequestrating of negatively charged lipids, such as phosphatidyl-serine and phosphatidic acid. The chemical structure of the present invention may have the construction (I).

Chemical structure having an affinity for a phospholipid and labeling compound diagnose kit and drug comprising this structure

Histone H3K4 methylation is a feature of meiotic recombination hotspots shared by many organisms including plants and mammals. Meiotic recombination is initiated by programmed double-strand break (DSB) formation that in budding yeast takes place in gene promoters and is promoted by histone H3K4 di/trimethylation. This histone modification is recognized by Spp1, a PHD-finger containing protein that belongs to the conserved histone H3K4 methyltransferase Set1 complex. During meiosis, Spp1 binds H3K4me3 and interacts with a DSB protein, Mer2, to promote DSB formation close to gene promoters. How Set1 complex- and Mer2- related functions of Spp1 are connected is not clear. Here, combining genome-wide localization analyses, biochemical approaches and the use of separation of function mutants, we show that Spp1 is present within two distinct complexes in meiotic cells, the Set1 and the Mer2 complexes. Disrupting the Spp1-Set1 interaction mildly decreases H3K4me3 levels and does not affect meiotic recombination initiation. Conversely, the Spp1-Mer2 interaction is required for normal meiotic recombination initiation, but dispensable for Set1 complex-mediated histone H3K4 methylation. Finally, we evidence that Spp1 preserves normal H3K4me3 levels independently of the Set1 complex. We propose a model where the three populations of Spp1 work sequentially to promote recombination initiation: first by depositing histone H3K4 methylation (Set1 complex), next by reading and protecting histone H3K4 methylation, and finally by making the link with the chromosome axis (Mer2-Spp1 complex). This work deciphers the precise roles of Spp1 in meiotic recombination and opens perspectives to study its functions in other organisms where H3K4me3 is also present at recombination hotspots.

/pdf/separable-functions-of-the-phd-finger-protein-spp1-in-the-zd4jx6ui39.pdf

Separable functions of the PHD finger protein Spp1 in the Set1 and the meiotic DSB forming complexes cooperate for meiotic DSB formation.

The CTD of HpDprA, a DNA binding Winged Helix domain which do not bind dsDNA

Originally published in: Protein Folding Handbook. Part I. Edited by Johannes Buchner and Thomas Kiefhaber. Copyright © 2005 Wiley-VCH Verlag GmbH & Co. KGaA Weinheim. Print ISBN: 3-527-30784-2



The ability to predict with good accuracy the effect of mutations on protein stability or complex formation is needed in order to design new proteins, as well as to understand the effect of single nucleotide polymorphisms on human health. In the following sections we will summarize what is known and can be used for this purpose. We will start by describing the forces that act on proteins, an understanding of which is a prerequisite to understanding the following section dealing with prediction methods. Finally we will briefly summarize other possible unexpected results of making mutants, such as changes in “in vivo” stability or inducing protein aggregation.



The sections in this article are


Physical Forces that Determine Protein Conformational Stability
Protein Conformational Stability
Structures of the N and D States [2-6]
Studies Aimed at Understanding the Physical Forces that Determine Protein Conformational Stability [1, 2, 8, 19-26]
Forces Determining Conformational Stability [1, 2, 8, 19-27]
Intramolecular Interactions
van der Waals Interactions
Electrostatic Interactions
Conformational Strain


Solvation
Intramolecular Interactions and Solvation Taken Together
Entropy
Cavity Formation
Summary


Effect of Point Mutations on in vitro Protein Stability
General Considerations on Protein Plasticity upon Mutation
Predictive Strategies
Methods
From Sequence and Multiple Sequence Alignment Analysis
Statistical Analysis of the Structure Databases
Helix/Coil Transition Model
Physicochemical Method Based on Protein Engineering Experiments
Methods Based only on the Basic Principles of Physics and Thermodynamics




Mutation Effects on in vivo Stability
The N-terminal Rule
The C-terminal Rule
PEST Signals


Mutation Effects on Aggregation


Keywords:

protein stability;
point mutation;
conformational strain;
structure prediction

Predicting Free Energy Changes of Mutations in Proteins

The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination. We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of these individual scores by respectively 6.5, 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%. All data used for benchmarking and scoring results, as well as pipelining scripts, are available at http://biodev.cea.fr/interevol/interevdata/

/pdf/atomic-level-evolutionary-information-improves-protein-3p1kd6922b.pdf

Raphael Guerois

Papers

Chemical structure having an affinity for a phospholipid and labeling compound diagnose kit and drug comprising this structure

Separable functions of the PHD finger protein Spp1 in the Set1 and the meiotic DSB forming complexes cooperate for meiotic DSB formation.

The CTD of HpDprA, a DNA binding Winged Helix domain which do not bind dsDNA

Predicting Free Energy Changes of Mutations in Proteins

Atomic-level evolutionary information improves protein-protein interface scoring