Search

The CAPRI and CASP prediction experiments have demonstrated the power of community wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting there may be important physical chemistry missing in the energy calculations. 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the non-polar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were on average structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a non-binder.

International audience; The resistance of microbes to commonly used antibiotics has become a worldwide health problem. A major underlying mechanism of microbial antibiotic resistance is the export of drugs from bacterial cells. Drug efflux is mediated through the action of multidrug resistance efflux pumps located in the bacterial cell membranes. The critical role of bacterial efflux pumps in antibiotic resistance has directed research efforts to the identification of novel efflux pump inhibitors that can be used alongside antibiotics in clinical settings. Here, we aimed to find potential inhibitors of the archetypical ATP-binding cassette (ABC) efflux pump BmrA of Bacillus subtilis via virtual screening of the Mu.Ta.Lig. Chemotheca small molecule library. Molecular docking calculations targeting the nucleotide-binding domain of BmrA were performed using AutoDock Vina. Following a further drug-likeness filtering step based on Lipinski’s Rule of Five, top 25 scorers were identified. These ligands were then clustered into separate groups based on their contact patterns with the BmrA nucleotide-binding domain. Six ligands with distinct contact patterns were used for further in vitro inhibition assays based on intracellular ethidium bromide accumulation. Using this methodology, we identified two novel inhibitors of BmrA from the Chemotheca small molecule library.

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, the Human Leukocyte Antigen (HLA) chain of MHC is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with pMHC in a common diagonal binding mode, and KIR-pMHC interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or Tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.Author SummaryA protein complex called the Major Histocompatibility Complex (MHC) plays a critical role in our fight against pathogens via presentation of antigenic peptides to receptor molecules of our immune system cells. Our knowledge on genetics, structure and protein interactions of MHC revealed that the peptide-binding groove of Human Leukocyte Chain (HLA I) of this complex is highly polymorphic and interacts with different proteins for peptide-binding and presentation over the course of its lifetime. Although the relationship between polymorphism and peptide-binding is well-known, we still lack a proper framework to understand how this polymorphism affects the overall MHC structure and protein interactions. Here, we used computational biophysics methods to generate structural models of 1436 HLA I alleles, and quantified local frustration within the HLA I, which indicates energetic optimization levels of contacts between amino acids. We identified a group of minimally frustrated and conserved positions which may be responsible for the conserved MHC structure, and detected high frustration patches on HLA surface positions taking part in interactions with other immune system proteins. Our results provide a biophysical basis for relationships between sequence, structure, and function of MHC I.

Sercinoglu, Onur/0000-0003-1361-8160; Ozbek, Pemra/0000-0002-3043-0015 WOS: 000475901600068 PubMed: 31114881 ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.

DNABINDPROT is designed to predict DNA-binding residues, based on the fluctuations of residues in high-frequency modes by the Gaussian network model. The residue pairs that display high mean-square distance fluctuations are analyzed with respect to DNA binding, which are then filtered with their evolutionary conservation profiles and ranked according to their DNA-binding propensities. If the analyses are based on the exact outcome of fluctuations in the highest mode, using a conservation threshold of 5, the results have a sensitivity, specificity, precision and accuracy of 9.3%, 90.5%, 18.1% and 78.6%, respectively, on a dataset of 36 unbound-bound protein structure pairs. These values increase up to 24.3%, 93.4%, 45.3% and 83.3% for the respective cases, when the neighboring two residues are considered. The relatively low sensitivity appears with the identified residues being selective and susceptible more for the binding core residues rather than all DNA-binding residues. The predicted residues that are not tagged as DNA-binding residues are those whose fluctuations are coupled with DNA-binding sites. They are in close proximity as well as plausible for other functional residues, such as ligand and protein-protein interaction sites., Scientific and Technological Research Council of Turkey (TÜBİTAK)-BIDEB; Betil Fund; Turkish Academy of Sciences (TÜBA); State Planning Organization of the Turkish Republic