Identification of hot-spot residues in protein-protein interactions by computational docking

<p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p

Identifying the unmet information and support needs of women with autoimmune rheumatic diseases during pregnancy planning, pregnancy and early parenting: mixed-methods study

Background Autoimmune rheumatic diseases (ARDs) such as inflammatory arthritis and Lupus, and many of the treatments for these diseases, can have a detrimental impact on fertility and pregnancy outcomes. Disease activity and organ damage as a result of ARDs can affect maternal and foetal outcomes. The safety and acceptability of hormonal contraceptives can also be affected. The objective of this study was to identify the information and support needs of women with ARDs during pregnancy planning, pregnancy and early parenting. Methods This mixed methods study included a cross-sectional online survey and qualitative narrative interviews. The survey was completed by 128 women, aged 18–49 in the United Kingdom with an ARD who were thinking of getting pregnant in the next five years, who were pregnant, or had young children (< 5 years old). The survey assessed quality-of-life and information needs (Arthritis Impact Measurement Scale Short Form and Educational Needs Assessment Tool), support received, what women found challenging, what was helpful, and support women would have liked. From the survey participants, a maximum variation sample of 22 women were purposively recruited for qualitative interviews. Interviews used a person-centered participatory approach facilitated by visual methods, which enabled participants to reflect on their experiences. Interviews were also carried out with seven health professionals purposively sampled from primary care, secondary care, maternity, and health visiting services. Results Survey findings indicated an unmet need for information in this population (ENAT total mean 104.85, SD 30.18). Women at the pre-conception stage reported higher needs for information on pregnancy planning, fertility, giving birth, and breastfeeding, whereas those who had children already expressed a higher need for information on pain and mobility. The need for high quality information, and more holistic, multi-disciplinary, collaborative, and integrated care consistently emerged as themes in the survey open text responses and interviews with women and health professionals. Conclusions There is an urgent need to develop and evaluate interventions to better inform, support and empower women of reproductive age who have ARDs as they navigate the complex challenges that they face during pregnancy planning, pregnancy and early parenting

Crystal structures of the CusA efflux pump suggest methionine-mediated metal transport

Gram-negative bacteria, such as Escherichia coli, frequently utilize tripartite efflux complexes in the resistance-nodulation-cell division (RND) family to expel diverse toxic compounds from the cell.1,2 The efflux system CusCBA is responsible for extruding biocidal Cu(I) and Ag(I) ions.3,4 No prior structural information was available for the heavy-metal efflux (HME) subfamily of the RND efflux pumps. Here we describe the crystal structures of the inner membrane transporter CusA in the absence and presence of bound Cu(I) or Ag(I). These CusA structures provide important new structural information about the HME sub-family of RND efflux pumps. The structures suggest that the metal binding sites, formed by a three-methionine cluster, are located within the cleft region of the periplasmic domain. Intriguingly, this cleft is closed in the apo-CusA form but open in the CusA-Cu(I) and CusA-Ag(I) structures, which directly suggests a plausible pathway for ion export. Binding of Cu(I) and Ag(I) triggers significant conformational changes in both the periplasmic and transmembrane domains. The crystal structure indicates that CusA has, in addition to the three-methionine metal binding site, four methionine pairs - three located in the transmembrane region and one in the periplasmic domain. Genetic analysis and transport assays suggest that CusA is capable of actively picking up metal ions from the cytosol, utilizing these methionine pairs/clusters to bind and export metal ions. These structures suggest a stepwise shuttle mechanism for transport between these sites

Volume-based solvation models out-perform area-based models in combined studies of wild-type and mutated protein-protein interfaces

<p>Abstract</p> <p>Background</p> <p>Empirical binding models have previously been investigated for the energetics of protein complexation (ΔG models) and for the influence of mutations on complexation (i.e. differences between wild-type and mutant complexes, ΔΔG models). We construct binding models to directly compare these processes, which have generally been studied separately.</p> <p>Results</p> <p>Although reasonable fit models were found for both ΔG and ΔΔG cases, they differ substantially. In a dataset curated for the absence of mainchain rearrangement upon binding, non-polar area burial is a major determinant of ΔG models. However this ΔG model does not fit well to the data for binding differences upon mutation. Burial of non-polar area is weighted down in fitting of ΔΔG models. These calculations were made with no repacking of sidechains upon complexation, and only minimal packing upon mutation. We investigated the consequences of more extensive packing changes with a modified mean-field packing scheme. Rather than emphasising solvent exposure with relatively extended sidechains, rotamers are selected that exhibit maximal packing with protein. This provides solvent accessible areas for proteins that are much closer to those of experimental structures than the more extended sidechain regime. The new packing scheme increases changes in non-polar burial for mutants compared to wild-type proteins, but does not substantially improve agreement between ΔG and ΔΔG binding models.</p> <p>Conclusion</p> <p>We conclude that solvent accessible area, based on modelled mutant structures, is a poor correlate for ΔΔG upon mutation. A simple volume-based, rather than solvent accessibility-based, model is constructed for ΔG and ΔΔG systems. This shows a more consistent behaviour. We discuss the efficacy of volume, as opposed to area, approaches to describe the energetic consequences of mutations at interfaces. This knowledge can be used to develop simple computational screens for binding in comparative modelled interfaces.</p

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

LGBTQ parenting post heterosexual relationship dissolution

The chapter examines parenting among sexual and gender minorities post heterosexual relationship dissolution (PHRD). Reviewing the literature around intersecting identities of LGBTQ parents, we consider how religion, race, and socioeconomic status are associated with routes into and out of heterosexual relationships and variation in the lived experience of sexual and gender identity minorities, in particular how LGBTQ parents PHRD feel about being out. Further consideration is given to examining how family relationships change and develop as parental sexual and/or gender identity changes. We also explore the impact of PHRD identity and parenthood on new partnerships and stepfamily experiences. The chapter addresses the reciprocal relationship between research on LGBTQ parenting and policy and legal influences that impact upon the experience of LGBTQ parenting PHRD when custody and access are disputed. Finally, the chapter includes future research directions and implications for practice in an area that has been revitalized in recent years

Structural Characterization of Protein–Protein Interactions with pyDockSAXS

Structural characterization of protein–protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein–protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein–protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server (https://life.bsc.es/pid/pydocksaxs), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein–protein complex from SAXS data.This work was supported by the Spanish Ministry of Science (grant BIO2016-79930-R), the European Union H2020 programme (grant MuG 676566), and the Labex EpiGenMed, an “Investissements d’avenir” program (ANR-10-LABX-12-01). The CBS is a member of France-BioImaging (FBI) and the French Infrastructure for Integrated Structural Biology (FRISBI), two national infrastructures supported by the French National Research Agency (ANR-10-INSB-04-01 and ANR-10-INSB-05, respectively).Peer reviewe