GraSp-PSN: A web server for graph spectra based analysis of protein structure networks

The function of a protein is most of the time achieved due to minute conformational changes in its structure due to ligand binding or environmental changes or other interactions. Hence the analysis of structure of proteins should go beyond the analysis of mere atom contacts and should include the emergent global structure as a whole. This can be achieved by graph spectra based analysis of protein structure networks. GraSp-PSN is a web server that can assist in (1) acquiring weighted protein structure network (PSN) and network parameters ranging from atomic level to global connectivity from the three dimensional coordinates of a protein, (2) generating scores for comparison of a pair of protein structures with detailed information of local to global connectivity, and (3) assigning perturbation scores to the residues and their interactions, that can prioritise them in terms of residue clusters. The methods implemented in the server are generic in nature and can be used for comparing networks in any discipline by uploading adjacency matrices in the server. The webserver can be accessed using the following link: https://pople.mbu.iisc.ac.in/.

Comparison of structural networks across homologous proteins

Protein sequence determines its structure and function. The indirect relationship between protein function and structure lies deep-rooted in the structural topology that has evolved into performing optimal function. The evolution of structure and its interconnectivity has been conventionally studied by comparing the root means square deviation between protein structures at the backbone level. Two factors that are necessary for the quantitative comparison of non-covalent interactions are (a) explicit inclusion of the coordinates of side-chain atoms and (b) consideration of multiple structures from the conformational landscape to account for structural variability. We have recently addressed these fundamental issues by investigating the alteration of inter-residue interactions across an ensemble of protein structure networks through a graph spectral approach. In this study, we have developed a rigorous method to compare the structure networks of homologous proteins, with a wide range of sequence identity percentages. A range of dissimilarity measures that show the extent of change in the network across homologous structures are generated, which also includes the comparison of the protein structure variability. We discuss in detail, scenarios where the variation of structure is not accompanied by loss or gain of the overall network and its vice versa. The sequence-based phylogeny among the homologs is also compared with the lineage obtained from information from such a robust structure comparison. In summary, we can obtain a quantitative comparison score for the structure networks of homologous proteins, which also enables us to study the evolution of protein function based on the variation of their topologies.

Gefitinib Inhibits Rifampicin-Induced CYP3A4 Gene Expression in Human Hepatocytes

During multidrug combination chemotherapy, activation of the nuclear receptor and the transcription factor human pregnane xenobiotic receptor (hPXR) has been shown to play a role in the development of chemoresistance. Mechanistically, this could occur due to the cancer drug activation of hPXR and the subsequent upregulation of hPXR target genes such as the drug metabolism enzyme, cytochrome P450 3A4 (CYP3A4). In the context of hPXR-mediated drug resistance, hPXR antagonists would be useful adjuncts to PXR-activating chemotherapy. However, there are currently no clinically approved hPXR antagonists in the market. Gefitinib (GEF), a tyrosine kinase inhibitor used for the treatment of advanced non-small-cell lung cancer and effectively used in combinational chemotherapy treatments, is a promising candidate owing to its hPXR ligand-like features. We, therefore, investigated whether GEF would act as an hPXR antagonist when combined with a known hPXR agonist, rifampicin (RIF). At therapeutically relevant concentrations, GEF successfully inhibited the RIF-induced upregulation of endogenous CYP3A4 gene expression in human primary hepatocytes and human hepatocells. Additionally, GEF inhibited the RIF induction of hPXR-mediated CYP3A4 promoter activity in HepG2 human liver carcinoma cells. The computational modeling of molecular docking predicted that GEF could bind to multiple sites on hPXR including the ligand-binding pocket, allowing for potential as a direct antagonist as well as an allosteric inhibitor. Indeed, GEF bound to the ligand-binding domain of the hPXR in cell-free assays, suggesting that GEF directly interacts with the hPXR. Taken together, our results suggest that GEF, at its clinically relevant therapeutic concentration, can antagonize the hPXR agonist-induced CYP3A4 gene expression in human hepatocytes. Thus, GEF could be a potential candidate for use in combinational chemotherapies to combat hPXR agonist-induced chemoresistance. Further studies are warranted to determine whether GEF has sufficient hPXR inhibitor abilities to overcome the hPXR agonist-induced chemoresistance.

Observation of ultracold atomic bubbles in orbital microgravity

Substantial leaps in the understanding of quantum systems have been driven by exploring geometry, topology, dimensionality and interactions in ultracold atomic ensembles^1-6. A system where atoms evolve while confined on an ellipsoidal surface represents a heretofore unexplored geometry and topology. Realizing an ultracold bubble-potentially Bose-Einstein condensed-relates to areas of interest including quantized-vortex flow constrained to a closed surface topology, collective modes and self-interference via bubble expansion^7-17. Large ultracold bubbles, created by inflating smaller condensates, directly tie into Hubble-analogue expansion physics^18-20. Here we report observations from the NASA Cold Atom Lab²¹ facility onboard the International Space Station of bubbles of ultracold atoms created using a radiofrequency-dressing protocol. We observe bubble configurations of varying size and initial temperature, and explore bubble thermodynamics, demonstrating substantial cooling associated with inflation. We achieve partial coverings of bubble traps greater than one millimetre in size with ultracold films of inferred few-micrometre thickness, and we observe the dynamics of shell structures projected into free-evolving harmonic confinement. The observations are among the first measurements made with ultracold atoms in space, using perpetual freefall to explore quantum systems that are prohibitively difficult to create on Earth. This work heralds future studies (in orbital microgravity) of the Bose-Einstein condensed bubble, the character of its excitations and the role of topology in its evolution.

Understanding structural variability in proteins using protein structural networks

Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

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Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.

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The comparison of protein structural networks can better discriminate conformations with similar backbones.

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Specific interactions between solvent accessible and inaccessible residues are poorly preserved.

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Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.

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These side-chain network-based studies provide a framework to correlate protein structure-function relationships.

Network Re-Wiring During Allostery and Protein-Protein Interactions: A Graph Spectral Approach

The process of allostery is often guided by subtle changes in the non-covalent interactions between residues of a protein. These changes may be brought about by minor perturbations by natural processes like binding of a ligand or protein-protein interaction. The challenge lies in capturing minute changes at the residue interaction level and following their propagation at local as well as global distances. While macromolecular effects of the phenomenon of allostery are inferred from experiments, a computational microscope can elucidate atomistic-level details leading to such macromolecular effects. Network formalism has served as an attractive means to follow this path and has been pursued further for the past couple of decades. In this chapter some concepts and methods are summarized, and recent advances are discussed. Specifically, the changes in strength of interactions (edge weight) and their repercussion on the overall protein organization (residue clustering) are highlighted. In this review, we adopt a graph spectral method to probe these subtle changes in a quantitative manner. Further, the power of this method is demonstrated for capturing re-ordering of side-chain interactions in response to ligand binding, which culminates into formation of a protein-protein complex in β₂-adrenergic receptors.

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.

Targeting the pregnane X receptor using microbial metabolite mimicry

The human PXR (pregnane X receptor), a master regulator of drug metabolism, has essential roles in intestinal homeostasis and abrogating inflammation. Existing PXR ligands have substantial off-target toxicity. Based on prior work that established microbial (indole) metabolites as PXR ligands, we proposed microbial metabolite mimicry as a novel strategy for drug discovery that allows exploiting previously unexplored parts of chemical space. Here, we report functionalized indole derivatives as first-in-class non-cytotoxic PXR agonists as a proof of concept for microbial metabolite mimicry. The lead compound, FKK6 (Felix Kopp Kortagere 6), binds directly to PXR protein in solution, induces PXR-specific target gene expression in cells, human organoids, and mice. FKK6 significantly represses pro-inflammatory cytokine production cells and abrogates inflammation in mice expressing the human PXR gene. The development of FKK6 demonstrates for the first time that microbial metabolite mimicry is a viable strategy for drug discovery and opens the door to underexploited regions of chemical space.

Belinostat, at Its Clinically Relevant Concentrations, Inhibits Rifampicin-Induced CYP3A4 and MDR1 Gene Expression

Activation of human pregnane X receptor (hPXR) has been associated with induction of chemoresistance. It has been proposed that such chemoresistance via cytochrome P450/drug transporters can be reversed with the use of antagonists that specifically abrogate agonist-mediated hPXR activation. Unfortunately, proposed antagonists lack the specificity and appropriate pharmacological characteristics that allow these features to be active in the clinic. We propose that, ideally, an hPXR antagonist would be a cancer drug itself that is part of a "cancer drug cocktail" and effective as an hPXR antagonist at therapeutic concentrations. Belinostat (BEL), a histone deacetylase inhibitor approved for the treatment of relapsed/refractory peripheral T-cell lymphoma, and often used in combination with chemotherapy, is an attractive candidate based on its hPXR ligand-like features. We sought to determine whether these features of BEL might allow it to behave as an antagonist in combination chemotherapy regimens that include hPXR activators. BEL represses agonist-activated hPXR target gene expression at its therapeutic concentrations in human primary hepatocytes and LS174T human colon cancer cells. BEL repressed rifampicin-induced gene expression of CYP3A4 and multidrug resistance protein 1, as well as their respective protein activities. BEL decreased rifampicin-induced resistance to SN-38, the active metabolite of irinotecan, in LS174T cells. This finding indicates that BEL could suppress hPXR agonist-induced chemoresistance. BEL attenuated the agonist-induced steroid receptor coactivator-1 interaction with hPXR, and, together with molecular docking studies, the study suggests that BEL directly interacts with multiple sites on hPXR. Taken together, our results suggest that BEL, at its clinically relevant therapeutic concentration, can antagonize hPXR agonist-induced gene expression and chemoresistance.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Validation of protein structure models using network similarity score

Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories.

Exploration of the conformational landscape in pregnane X receptor reveals a new binding pocket

Ligand-regulated pregnane X receptor (PXR), a member of the nuclear receptor superfamily, plays a central role in xenobiotic metabolism. Despite its critical role in drug metabolism, PXR activation can lead to adverse drug-drug interactions and early stage metabolism of drugs. Activated PXR can induce cancer drug resistance and enhance the onset of malignancy. Since promiscuity in ligand binding makes it difficult to develop competitive inhibitors targeting PXR ligand binding pocket (LBP), it is essential to identify allosteric sites for effective PXR antagonism. Here, molecular dynamics (MD) simulation studies unravelled the existence of two different conformational states, namely "expanded" and "contracted", in apo PXR ligand binding domain (LBD). Ligand binding events shifted this conformational equilibrium and locked the LBD in a single "ligand-adaptable" conformational state. Ensemble-based computational solvent mapping identified a transiently open potential small molecule binding pocket between α5 and α8 helices, named "α8 pocket", whose opening-closing mechanism directly correlated with the conformational shift in LBD. A virtual hit identified through structure-based virtual screening against α8 pocket locks the pocket in its open conformation. MD simulations further revealed that the presence of small molecule at allosteric site disrupts the LBD dynamics and locks the LBD in a "tightly-contracted" conformation. The molecular details provided here could guide new structural studies to understand PXR activation and antagonism.

A Deterministic Optimization Approach to Protein Sequence Design Using Continuous Models

Determining the sequence of amino acid residues in a heteropolymer chain of a protein with a given conformation is a discrete combinatorial problem that is not generally amenable for gradient-based continuous optimization algorithms. In this paper we present a new approach to this problem using continuous models. In this modeling, continuous “state functions” are proposed to designate the type of each residue in the chain. Such a continuous model helps define a continuous sequence space in which a chosen criterion is optimized to find the most appropriate sequence. Searching a continuous sequence space using a deterministic optimization algorithm makes it possible to find the optimal sequences with much less computation than many other approaches. The computational efficiency of this method is further improved by combining it with a graph spectral method, which explicitly takes into account the topology of the desired conformation and also helps make the combined method more robust. The continuous modeling used here appears to have additional advantages in mimicking the folding pathways and in creating the energy landscapes that help find sequences with high stability and kinetic accessibility. To illustrate the new approach, a widely used simplifying assumption is made by considering only two types of residues: hydrophobic (H) and polar (P). Self-avoiding compact lattice models are used to validate the method with known results in the literature and data that can be practically obtained by exhaustive enumeration on a desktop computer. We also present examples of sequence design for the HP models of some real proteins, which are solved in less than five minutes on a single-processor desktop computer. Some open issues and future extensions are noted.

Mechanism of Iron-Dependent Repressor (IdeR) Activation and DNA Binding: A Molecular Dynamics and Protein Structure Network Study

Metalloproteins form a major class of enzymes in the living system that are involved in crucial biological functions such as catalysis, redox reactions and as 'switches' in signal transductions. Iron dependent repressor (IdeR) is a metal-sensing transcription factor that regulates free iron concentration in Mycobacterium tuberculosis. IdeR is also known to promote bacterial virulence, making it an important target in the field of therapeutics. Mechanistic details of how iron ions modulate IdeR such that it dimerizes and binds to DNA is not understood clearly. In this study, we have performed molecular dynamic simulations and integrated it with protein structure networks to study the influence of iron on IdeR structure and function. A significant structural variation between the metallated and the non-metallated system is observed. Our simulations clearly indicate the importance of iron in stabilizing its monomeric subunit, which in turn promotes dimerization. However, the most striking results are obtained from the simulations of IdeR-DNA complex in the absence of metals, where at the end of 100ns simulations, the protein subunits are seen to rapidly dissociate away from the DNA, thereby forming an excellent resource to investigate the mechanism of DNA binding. We have also investigated the role of iron as an allosteric regulator of IdeR that positively induces IdeR-DNA complex formation. Based on this study, a mechanistic model of IdeR activation and DNA binding has been proposed.

An automated approach to network features of protein structure ensembles

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

A histidine aspartate ionic lock gates the iron passage in miniferritins from Mycobacterium smegmatis

Dps (DNA-binding protein from starved cells) are dodecameric assemblies belonging to the ferritin family that can bind DNA, carry out ferroxidation, and store iron in their shells. The ferritin-like trimeric pore harbors the channel for the entry and exit of iron. By representing the structure of Dps as a network we have identified a charge-driven interface formed by a histidine aspartate cluster at the pore interface unique to Mycobacterium smegmatis Dps protein, MsDps2. Site-directed mutagenesis was employed to generate mutants to disrupt the charged interactions. Kinetics of iron uptake/release of the wild type and mutants were compared. Crystal structures were solved at a resolution of 1.8-2.2 Å for the various mutants to compare structural alterations vis à vis the wild type protein. The substitutions at the pore interface resulted in alterations in the side chain conformations leading to an overall weakening of the interface network, especially in cases of substitutions that alter the charge at the pore interface. Contrary to earlier findings where conserved aspartate residues were found crucial for iron release, we propose here that in the case of MsDps2, it is the interplay of negative-positive potentials at the pore that enables proper functioning of the protein. In similar studies in ferritins, negative and positive patches near the iron exit pore were found to be important in iron uptake/release kinetics. The unique ionic cluster in MsDps2 makes it a suitable candidate to act as nano-delivery vehicle, as these gated pores can be manipulated to exhibit conformations allowing for slow or fast rates of iron release.

Weighting schemes in metabolic graphs for identifying biochemical routes

Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes [Formula: see text] fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks.

Ranking the quality of protein structure models using sidechain based network properties

Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server ( http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.