A computational methodology to diagnose sequence-variant dynamic perturbations by comparing atomic protein structures.

MOTIVATION: The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures. RESULTS: We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity. AVAILABILITY AND IMPLEMENTATION: The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few.

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development. Thus, to set up customized therapies, the structural influences of amino acids on one another need to be tracked down. Using network-based models and classical analysis of amino acid and atomic packing in protein structures, the influence of first shell neighbors on the structural fate of a position upon mutation, is revisited. Regardless of the type and position in a structure, amino acids satisfy on average over their neighbors, a low and similar number of atomic interactions, the average called the neighborhood watch (Nw). The structural tolerance of a position to mutation depends on the modulation of the composition and/or proximity of neighbors to maintain the same Nw, before and after mutation, at every position. Changes, upon mutation of the number of atomic interactions at the level of individual pairs (wij) are structurally tolerated but influence structural dynamics. Robust, fragile and rescue interactions can be identified with Nw and wij, offering a framework to classify sequence variants according to position-dependent structural changes.

Protein structural robustness to mutations: an in silico investigation.

Proteins possess qualities of robustness and adaptability to perturbations such as mutations, but occasionally fail to withstand them, resulting in loss of function. Herein, the structural impact of mutations is investigated independently of the functional impact. Primarily, we aim at understanding the mechanisms of structural robustness pre-requisite for functional integrity. The structural changes due to mutations propagate from the site of mutation to residues much more distant than typical scales of chemical interactions, following a cascade mechanism. This can trigger dramatic changes or subtle ones, consistent with a loss of function and disease or the emergence of new functions. Robustness is enhanced by changes producing alternative structures, in good agreement with the view that proteins are dynamic objects fulfilling their functions from a set of conformations. This result, robust alternative structures, is also coherent with epistasis or rescue mutations, or more generally, with non-additive mutational effects and compensatory mutations. To achieve this study, we developed the first algorithm, referred to as Amino Acid Rank (AAR), which follows the structural changes associated with mutations from the site of the mutation to the entire protein structure and quantifies the changes so that mutations can be ranked accordingly. Assessing the paths of changes opens the possibility of assuming secondary mutations for compensatory mechanisms.

Connected Component Analysis of Dynamical Perturbation Contact Networks.

Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.

GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3.

Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.

A protocol to measure slow protein dynamics of the cholera toxin B pentamers using broadband dielectric spectroscopy.

The present protocol describes how to measure experimentally the slow protein dynamics that take place upon the thermal unfolding of the B subunit cholera toxin pentamers using broadband dielectric spectroscopy (BDS) in weakly hydrated and nanoconfined conditions. Transient unfolding intermediates, rarely identified otherwise, are revealed thanks to the B subunit's remarkable heat resistance up to 180°C and distinct molecular dynamics. The frequencies detected experimentally are consistent with the spatiotemporal scales of motions of molecular dynamics simulation. For complete details on the use and execution of this protocol, please refer to Bourgeat et al. (2021, 2019).

Topology Results on Adjacent Amino Acid Networks of Oligomeric Proteins.

In this chapter, we focus on topology measurements of the adjacent amino acid networks for a data set of oligomeric proteins and some of its subnetworks. The aim is to present many mathematical tools in order to understand the structures of proteins implicitly coded in such networks and subnetworks. We mainly investigate four important networks by computing the number of connected components, the degree distribution, and assortativity measures. We compare each result in order to prove that the four networks have quite independent topologies.

Experimental diagnostic of sequence-variant dynamic perturbations revealed by broadband dielectric spectroscopy.

Genetic diversity leads to protein robustness, adaptability, and failure. Some sequence variants are structurally robust but functionally disturbed because mutations bring the protein onto unfolding/refolding routes resulting in misfolding diseases (e.g., Parkinson). We assume dynamic perturbations introduced by mutations foster the alternative unfolding routes and test this possibility by comparing the unfolding dynamics of the heat-labile enterotoxin B pentamers and the cholera toxin B pentamers, two pentamers structurally and functionally related and robust to 17 sequence variations. The B-subunit thermal unfolding dynamics are monitored by broadband dielectric spectroscopy in nanoconfined and weakly hydrated conditions. Distinct dielectric signals reveal the different B-subunits unfolding dynamics. Combined with network analyses, the experiments pinpoint the role of three mutations A1T, E7D, and E102A, in diverting LTB5 to alternative unfolding routes that protect LTB5 from dissociation. Altogether, the methodology diagnoses dynamics faults that may underlie functional disorder, drug resistance, or higher virulence of sequence variants.

Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures.

Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.

Trimeric reassembly of the globular domain of human C1q.

C1q is a versatile recognition protein which binds to a variety of targets and consequently triggers the classical pathway of complement. C1q is a hetero-trimer composed of three chains (A, B and C) arranged in three domains, a short N-terminal region, followed by a collagenous repeat domain that gives rise to the formation of (ABC) triple helices, each ending in a C-terminal hetero-trimeric globular domain, called gC1q, which is responsible for the recognition properties of C1q. The mechanism of the trimeric assembly of C1q and in particular the role of each domain in the process is unknown. Here, we have investigated if the gC1q domain was able to assemble into functional trimers, in vitro, in the absence of the collagenous domain, a motif known to promote obligatory trimers in other proteins. Acid-mediated gC1q protomers reassembled into functional trimers, once neutralized, indicating that it is the gC1q domain which possesses the information for trimerization. However, reassembly occurred after neutralization, only if the gC1q protomers had preserved a residual tertiary structure at the end of the acidic treatment. Thus, the collagenous domain of C1q might initialize the folding of the gC1q domain so that subsequent assembly of the entire molecule can occur.

Experimental Protein Molecular Dynamics: Broadband Dielectric Spectroscopy coupled with nanoconfinement.

Protein dynamics covers multiple spatiotemporal scale processes, among which slow motions, not much understood even though they are underlying protein folding and protein functions. Protein slow motions are associated with structural heterogeneity, short-lived and poorly populated conformations, hard to detect individually. In addition, they involve collective motions of many atoms, not easily tracked by simulation and experimental devices. Here we propose a biophysical approach, coupling geometrical nanoconfinement and broadband dielectric spectroscopy (BDS), which distinguishes protein conformations by their respective molecular dynamics. In particular, protein-unfolding intermediates, usually poorly populated in macroscopic solutions are detected. The protein dynamics is observed under unusual conditions (sample nanoconfinement and dehydration) highlighting the robustness of protein structure and protein dynamics to a variety of conditions consistent with protein sustainability. The protein dielectric signals evolve with the temperature of thermal treatments indicating sensitivity to atomic and molecular interaction changes triggered by the protein thermal unfolding. As dipole fluctuations depend on both collective large-scale motions and local motions, the approach offers a prospect to track in-depth unfolding events.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks.

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

From local to global changes in proteins: a network view.

To fulfill the biological activities in living organisms, proteins are endowed with dynamics, robustness and adaptability. The three properties co-exist because they allow global changes in structure to arise from local perturbations (dynamics). Robustness refers to the ability of the protein to incur such changes without suffering loss of function; adaptability is the emergence of a new biological activity. Since loss of function may jeopardize the survival of the organism and lead to disease, adaptability may occur through the combination of two local perturbations that together rescue the initial function. The review highlights the relevancy of computational network analysis to understand how a local change produces global changes.

Intermolecular ß-strand networks avoid hub residues and favor low interconnectedness: a potential protection mechanism against chain dissociation upon mutation.

Altogether few protein oligomers undergo a conformational transition to a state that impairs their function and leads to diseases. But when it happens, the consequences are not harmless and the so-called conformational diseases pose serious public health problems. Notorious examples are the Alzheimer's disease and some cancers associated with a conformational change of the amyloid precursor protein (APP) and of the p53 tumor suppressor, respectively. The transition is linked with the propensity of ß-strands to aggregate into amyloid fibers. Nevertheless, a huge number of protein oligomers associate chains via ß-strand interactions (intermolecular ß-strand interface) without ever evolving into fibers. We analyzed the layout of 1048 intermolecular ß-strand interfaces looking for features that could provide the ß-strands resistance to conformational transitions. The interfaces were reconstructed as networks with the residues as the nodes and the interactions between residues as the links. The networks followed an exponential decay degree distribution, implying an absence of hubs and nodes with few links. Such layout provides robustness to changes. Few links per nodes do not restrict the choices of amino acids capable of making an interface and maintain high sequence plasticity. Few links reduce the "bonding" cost of making an interface. Finally, few links moderate the vulnerability to amino acid mutation because it entails limited communication between the nodes. This confines the effects of a mutation to few residues instead of propagating them to many residues via hubs. We propose that intermolecular ß-strand interfaces are organized in networks that tolerate amino acid mutation to avoid chain dissociation, the first step towards fiber formation. This is tested by looking at the intermolecular ß-strand network of the p53 tetramer.

Beta-strand interfaces of non-dimeric protein oligomers are characterized by scattered charged residue patterns.

Protein oligomers are formed either permanently, transiently or even by default. The protein chains are associated through intermolecular interactions constituting the protein interface. The protein interfaces of 40 soluble protein oligomers of stœchiometries above two are investigated using a quantitative and qualitative methodology, which analyzes the x-ray structures of the protein oligomers and considers their interfaces as interaction networks. The protein oligomers of the dataset share the same geometry of interface, made by the association of two individual ß-strands (ß-interfaces), but are otherwise unrelated. The results show that the ß-interfaces are made of two interdigitated interaction networks. One of them involves interactions between main chain atoms (backbone network) while the other involves interactions between side chain and backbone atoms or between only side chain atoms (side chain network). Each one has its own characteristics which can be associated to a distinct role. The secondary structure of the ß-interfaces is implemented through the backbone networks which are enriched with the hydrophobic amino acids favored in intramolecular ß-sheets (MCWIV). The intermolecular specificity is provided by the side chain networks via positioning different types of charged residues at the extremities (arginine) and in the middle (glutamic acid and histidine) of the interface. Such charge distribution helps discriminating between sequences of intermolecular ß-strands, of intramolecular ß-strands and of ß-strands forming ß-amyloid fibers. This might open new venues for drug designs and predictive tool developments. Moreover, the ß-strands of the cholera toxin B subunit interface, when produced individually as synthetic peptides, are capable of inhibiting the assembly of the toxin into pentamers. Thus, their sequences contain the features necessary for a ß-interface formation. Such ß-strands could be considered as 'assemblons', independent associating units, by homology to the foldons (independent folding unit). Such property would be extremely valuable in term of assembly inhibitory drug development.

Oligomeric interfaces under the lens: gemini.

The assembly of subunits in protein oligomers is an important topic to study as a vast number of proteins exists as stable or transient oligomer and because it is a mechanism used by some protein oligomers for killing cells (e.g., perforin from the human immune system, pore-forming toxins from bacteria, phage, amoeba, protein misfolding diseases, etc.). Only a few of the amino acids that constitute a protein oligomer seem to regulate the capacity of the protein to assemble (to form interfaces), and some of these amino acids are localized at the interfaces that link the different chains. The identification of the residues of these interfaces is rather difficult. We have developed a series of programs, under the common name of Gemini, that can select the subset of the residues that is involved in the interfaces of a protein oligomer of known atomic structure, and generate a 2D interaction network (or graph) of the subset. The graphs generated for several oligomers demonstrate the accuracy of the selection of subsets that are involved in the geometrical and the chemical properties of interfaces. The results of the Gemini programs are in good agreement with those of similar programs with an advantage that Gemini programs can perform the residue selection much more rapidly. Moreover, Gemini programs can also perform on a single protein oligomer without the need of comparison partners. The graphs are extremely useful for comparative studies that would help in addressing questions not only on the sequence specificity of protein interfaces but also on the mechanism of the assembly of unrelated protein oligomers.

Cholera toxin B subunits assemble into pentamers--proposition of a fly-casting mechanism.

The cholera toxin B pentamer (CtxB(5)), which belongs to the AB(5) toxin family, is used as a model study for protein assembly. The effect of the pH on the reassembly of the toxin was investigated using immunochemical, electrophoretic and spectroscopic methods. Three pH-dependent steps were identified during the toxin reassembly: (i) acquisition of a fully assembly-competent fold by the CtxB monomer, (ii) association of CtxB monomer into oligomers, (iii) acquisition of the native fold by the CtxB pentamer. The results show that CtxB(5) and the related heat labile enterotoxin LTB(5) have distinct mechanisms of assembly despite sharing high sequence identity (84%) and almost identical atomic structures. The difference can be pinpointed to four histidines which are spread along the protein sequence and may act together. Thus, most of the toxin B amino acids appear negligible for the assembly, raising the possibility that assembly is driven by a small network of amino acids instead of involving all of them.

A rivet model for channel formation by aerolysin-like pore-forming toxins.

The bacterial toxin aerolysin kills cells by forming heptameric channels, of unknown structure, in the plasma membrane. Using disulfide trapping and cysteine scanning mutagenesis coupled to thiol-specific labeling on lipid bilayers, we identify a loop that lines the channel. This loop has an alternating pattern of charged and uncharged residues, suggesting that the transmembrane region has a beta-barrel configuration, as observed for Staphylococcal alpha-toxin. Surprisingly, we found that the turn of the beta-hairpin is composed of a stretch of five hydrophobic residues. We show that this hydrophobic turn drives membrane insertion of the developing channel and propose that, once the lipid bilayer has been crossed, it folds back parallel to the plane of the membrane in a rivet-like fashion. This rivet-like conformation was modeled and sequence alignments suggest that such channel riveting may operate for many other pore-forming toxins.

Positively charged amino acids are essential for electron transfer and protein-protein interactions in the soluble methane monooxygenase complex from Methylococcus capsulatus (Bath).

The soluble methane monooxygenase (sMMO) complex from Methylococcus capsulatus (Bath) catalyses oxygen- and NAD(P)H-dependent oxygenation of methane, propene, and other substrates. Whole-complex sMMO oxygenase activity requires all three sMMO components: the hydroxylase, the reductase, and protein B. Also, in the presence of hydrogen peroxide, the hydroxylase alone catalyzes substrate oxygenation via the peroxide shunt reaction. We investigated the effect of amine cross-linking on hydroxylase activity to probe the role of a gross conformational change that occurs in the hydroxylase upon binding of the other protein components. The cross-linker inhibited hydroxylase activity in the whole complex, but this effect was due to covalent modification of primary amine groups rather than cross-linking. Covalent modification of arginine side-chains on the hydroxylase had a similar effect, but, most remarkably, neither form of modification affected the activity of the hydroxylase via the peroxide shunt reaction. It was shown that covalent modification of positively charged groups on the hydroxylase, which occurred at multiple sites, interfered with its physical and functional interactions with protein B and with the passage of electrons from the reductase. These results indicate that protein B and the reductase of the sMMO complex interact via positively charged groups on the surface of the hydroxylase to induce a conformational change that is necessary for delivery of electrons into the active site of the hydroxylase. Modification of positively charged groups on protein B had no effect on its function, consistent with the hypothesis that positively charged groups on the hydroxylase interact with negative charges on protein B. Thus, we have discovered a means of specifically inactivating the interactions between the sMMO complex while preserving the catalytic activity of the hydroxylase active site which provides a new method of studying intercomponent interactions within sMMO.