Intra and Inter-Molecular Communications Through Protein Structure Network

Effect of short- and long-range interactions on trp rotamer populations determined by site-directed tryptophan fluorescence of tear lipocalin

In the lipocalin family, the conserved interaction between the main α-helix and the β-strand H is an ideal model to study protein side chain dynamics. Site-directed tryptophan fluorescence (SDTF) has successfully elucidated tryptophan rotamers at positions along the main alpha helical segment of tear lipocalin (TL). The rotamers assigned by fluorescent lifetimes of Trp residues corroborate the restriction expected based on secondary structure. Steric conflict constrains Trp residues to two (t, g⁻) of three possible χ₁ (t, g⁻, g⁺) canonical rotamers. In this study, investigation focused on the interplay between rotamers for a single amino acid position, Trp 130 on the α-helix and amino acids Val 113 and Leu 115 on the H strand, i.e. long range interactions. Trp130 was substituted for Phe by point mutation (F130W). Mutations at positions 113 and 115 with combinations of Gly, Ala, Phe residues alter the rotamer distribution of Trp130. Mutations, which do not distort local structure, retain two rotamers (two lifetimes) populated in varying proportions. Replacement of either long range partner with a small amino acid, V113A or L115A, eliminates the dominance of the t rotamer. However, a mutation that distorts local structure around Trp130 adds a third fluorescence lifetime component. The results indicate that the energetics of long-range interactions with Trp 130 further tune rotamer populations. Diminished interactions, evident in W130G113A115, result in about a 22% increase of α-helix content. The data support a hierarchic model of protein folding. Initially the secondary structure is formed by short-range interactions. TL has non-native α-helix intermediates at this stage. Then, the long-range interactions produce the native fold, in which TL shows α-helix to β-sheet transitions. The SDTF method is a valuable tool to assess long-range interaction energies through rotamer distribution as well as the characterization of low-populated rotameric states of functionally important excited protein states.

Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.

Network and atomistic simulations unveil the structural determinants of mutations linked to retinal diseases

A number of incurable retinal diseases causing vision impairments derive from alterations in visual phototransduction. Unraveling the structural determinants of even monogenic retinal diseases would require network-centered approaches combined with atomistic simulations. The transducin G38D mutant associated with the Nougaret Congenital Night Blindness (NCNB) was thoroughly investigated by both mathematical modeling of visual phototransduction and atomistic simulations on the major targets of the mutational effect. Mathematical modeling, in line with electrophysiological recordings, indicates reduction of phosphodiesterase 6 (PDE) recognition and activation as the main determinants of the pathological phenotype. Sub-microsecond molecular dynamics (MD) simulations coupled with Functional Mode Analysis improve the resolution of information, showing that such impairment is likely due to disruption of the PDEγ binding cavity in transducin. Protein Structure Network analyses additionally suggest that the observed slight reduction of theRGS9-catalyzed GTPase activity of transducin depends on perturbed communication between RGS9 and GTP binding site. These findings provide insights into the structural fundamentals of abnormal functioning of visual phototransduction caused by a missense mutation in one component of the signaling network. This combination of network-centered modeling with atomistic simulations represents a paradigm for future studies aimed at thoroughly deciphering the structural determinants of genetic retinal diseases. Analogous approaches are suitable to unveil the mechanism of information transfer in any signaling network either in physiological or pathological conditions.

Identification of functionally key residues in AMPA receptor with a thermodynamic method

AMPA receptor mediates the fast excitatory synaptic transmission in the central nervous system, and it is activated by the binding of glutamate that results in the opening of the transmembrane ion channel. In the present work, the thermodynamic method developed by our group was improved and then applied to identify the functionally key residues that regulate the glutamate-binding affinity of AMPA receptor. In our method, the key residues are identified as those whose perturbation largely changes the ligand binding free energy of the protein. It is found that besides the ligand binding sites, other residues distant from the binding cleft can also influence the glutamate binding affinity through a long-range allosteric regulation. These allosteric sites include the hinge region of the ligand binding cleft, the dimer interface of the ligand binding domain, the linkers between the ligand binding domain and the transmembrane domain, and the interface between the N-terminal domain and the ligand binding domain. Our calculation results are consistent with the available experimental data. The results are helpful for our understanding of the mechanism of long-range allosteric communication in the AMPA receptor and the mechanism of channel opening triggered by glutamate binding.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

A Mixed Protein Structure Network and Elastic Network Model Approach to Predict the Structural Communication in Biomolecular Systems: The PDZ2 Domain from Tyrosine Phosphatase 1E As a Case Study

Graph theory is being increasingly used to study the structural communication in biomolecular systems. This requires incorporating information on the system's dynamics, which is time-consuming and not suitable for high-throughput investigations. We propose a mixed Protein Structure Network (PSN) and Elastic Network Model (ENM)-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems. PSN analysis and ENM-Normal Mode Analysis (ENM-NMA) are implemented in the structural analysis software Wordom, freely available at http://wordom.sourceforge.net/ . The method performs a systematic search of the shortest communication pathways that traverse a protein structure. A number of strategies to compare the structure networks of a protein in different functional states and to get a global picture of communication pathways are presented as well. The approach was validated on the PDZ2 domain from tyrosine phosphatase 1E (PTP1E) in its free (APO) and peptide-bound states. PDZ domains are, indeed, the systems whose structural communication and allosteric features are best characterized both in vitro and in silico. The agreement between predictions by the PSN-ENM method and in vitro evidence is remarkable and comparable to or higher than that reached by more time-consuming computational approaches tested on the same biological system. Finally, the PSN-ENM method was able to reproduce the salient communication features of unbound and bound PTP1E inferred from molecular dynamics simulations. High speed makes this method suitable for high throughput investigation of the communication pathways in large sets of biomolecular systems in different functional states.

Modeling the structural communication in supramolecular complexes involving GPCRs

This article describes a computational strategy aimed at studying the structural communication in G-Protein Coupled Receptors (GPCRs) and G proteins. The strategy relies on comparative Molecular Dynamics (MD) simulations and analyses of wild-type (i.e., reference state) vs. mutated (i.e., perturbed state), or free (i.e., reference state) vs. bound (i.e., perturbed state) forms of a GPCR or a G protein. Bound forms of a GPCR include complexes with small ligands and/or receptor dimers/oligomers, whereas bound forms of heterotrimeric GDP-bound G proteins concern the complex with a GPCR. The computational strategy includes structure prediction of a receptor monomer (in the absence of high-resolution structure), a receptor dimer/oligomer, and a receptor-G protein complex, which constitute the inputs of MD simulations. Finally, the analyses of the MD trajectories are instrumental in inferring the structural/dynamics differences between reference and perturbed states of a GPCR or a G protein. In this respect, focus will be put on the analysis of protein structure networks and communication paths.

Functional annotation of the mesophilic-like character of mutants in a cold-adapted enzyme by self-organising map analysis of their molecular dynamics

Multiple comparison of the Molecular Dynamics (MD) trajectories of mutants in a cold-adapted α-amylase (AHA) could be used to elucidate functional features required to restore mesophilic-like activity. Unfortunately it is challenging to identify the different dynamic behaviors and correctly relate them to functional activity by routine analysis. We here employed a previously developed and robust two-stage approach that combines Self-Organising Maps (SOMs) and hierarchical clustering to compare conformational ensembles of proteins. Moreover, we designed a novel strategy to identify the specific mutations that more efficiently convert the dynamic signature of the psychrophilic enzyme (AHA) to that of the mesophilic counterpart (PPA). The SOM trained on AHA and its variants was used to classify a PPA MD ensemble and successfully highlighted the relationships between the flexibilities of the target enzyme and of the different mutants. Moreover the local features of the mutants that mostly influence their global flexibility in a mesophilic-like direction were detected. It turns out that mutations of the cold-adapted enzyme to hydrophobic and aromatic residues are the most effective in restoring the PPA dynamic features and could guide the design of more mesophilic-like mutants. In conclusion, our strategy can efficiently extract specific dynamic signatures related to function from multiple comparisons of MD conformational ensembles. Therefore, it can be a promising tool for protein engineering.

Network analysis to uncover the structural communication in GPCRs

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system dynamics can be provided by atomistic molecular dynamics simulations or coarse-grained Elastic Network Models paired with Normal Mode Analysis (ENM-NMA). This chapter describes the application of PSN analysis to uncover the structural communication in G protein-coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding mutations, dimerization, and activation are described. Focus is put on the ENM-NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signaling active (meta II (MII)) states, highlighting clear changes in the PSN and the centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Intramolecular interactions stabilizing compact conformations of the intrinsically disordered kinase-inhibitor domain of Sic1: a molecular dynamics investigation

Cyclin-dependent kinase inhibitors (CKIs) are key regulatory proteins of the eukaryotic cell cycle, which modulate cyclin-dependent kinase (Cdk) activity. CKIs perform their inhibitory effect by the formation of ternary complexes with a target kinase and its cognate cyclin. These regulators generally belong to the class of intrinsically disordered proteins (IDPs), which lack a well-defined and organized three-dimensional (3D) structure in their free state, undergoing folding upon binding to specific partners. Unbound IDPs are not merely random-coil structures, but can present intrinsically folded structural units (IFSUs) and collapsed conformations. These structural features can be relevant to protein function in vivo. The yeast CKI Sic1 is a 284-amino acid IDP that binds to Cdk1 in complex with the Clb5,6 cyclins, preventing phosphorylation of G1 substrates and, therefore, entrance to the S phase. Sic1 degradation, triggered by multiple phosphorylation events, promotes cell-cycle progression. Previous experimental studies pointed out a propensity of Sic1 and its isolated domains to populate both extended and compact conformations. The present contribution provides models for compact conformations of the Sic1 kinase-inhibitory domain (KID) by all-atom molecular dynamics (MD) simulations in explicit solvent and in the absence of interactors. The results are integrated by spectroscopic and spectrometric data. Helical IFSUs are identified, along with networks of intramolecular interactions. The results identify a group of putative hub residues and networks of electrostatic interactions, which are likely to be involved in the stabilization of the globular states.

Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments

The growing interest for comparing protein internal dynamics owes much to the realisation that protein function can be accompanied or assisted by structural fluctuations and conformational changes. Analogously to the case of functional structural elements, those aspects of protein flexibility and dynamics that are functionally oriented should be subject to evolutionary conservation. Accordingly, dynamics-based protein comparisons or alignments could be used to detect protein relationships that are more elusive to sequence and structural alignments. Here we provide an account of the progress that has been made in recent years towards developing and applying general methods for comparing proteins in terms of their internal dynamics and advance the understanding of the structure-function relationship.

Light on the structural communication in Ras GTPases

The graph theory was combined with fluctuation dynamics to investigate the structural communication in four small G proteins, Arf1, H-Ras, RhoA, and Sec4. The topology of small GTPases is such that it requires the presence of the nucleotide to acquire a persistent structural network. The majority of communication paths involves the nucleotide and does not exist in the unbound forms. The latter are almost devoid of high-frequency paths. Thus, small Ras GTPases acquire the ability to transfer signals in the presence of nucleotide, suggesting that it modifies the intrinsic dynamics of the protein through the establishment of regions of hyperlinked nodes with high occurrence of correlated motions. The analysis of communication paths in the inactive (S(GDP)) and active (S(GTP)) states of the four G proteins strengthened the separation of the Ras-like domain into two dynamically distinct lobes, i.e. lobes 1 and 2, representing, respectively, the N-terminal and C-terminal halves of the domain. In the framework of this separation, interfunctional states and interfamily differences could be inferred. The structure network undergoes a reshaping depending on the bound nucleotide. Nucleotide-dependent divergences in structural communication reach the maximum in Arf1 and the minimum in RhoA. In Arf1, the nucleotide-dependent paths essentially express a communication between the G box 4 (G4) and distal portions of lobe 1. In the S(GDP) state, the G4 communicates with the N-term, while, in the S(GTP) state, the G4 communicates with the switch II. Clear differences could be also found between Arf1 and the other three G proteins. In Arf1, the nucleotide tends to communicate with distal portions of lobe 1, whereas in H-Ras, RhoA, and Sec4 it tends to communicate with a cluster of aromatic/hydrophobic amino acids in lobe 2. These differences may be linked, at least in part, to the divergent membrane anchoring modes that would involve the N-term for the Arf family and the C-term for the Rab/Ras/Rho families.

Local motifs in proteins combine to generate global functional moves

Literature on the topological properties of folded proteins that has emerged as a field in its own right in the past decade is reviewed. Physics-based construction of coarse-grained models of proteins from knowledge of all-atom coordinates of the average structure is discussed. Once network is thus obtained with the node and link information, local motifs provide plethora of information on protein function. The hierarchical structure of the proteins manifested in the interrelations of local motifs is emphasized. Motifs are also related to modularity of the structure, and they quantify shifts in the landscapes upon conformational changes induced by, e.g. ligand binding. Redundancy emerges as a balance between local and global network descriptors and is related to the collectivity of the protein motions. Introducing weight on links followed by sequential removal of least cohesive contacts allows interactions in proteins to be represented as the superposition of essential and redundant sets. Lack of the former makes the network non-functional, while the latter ensures robust functioning under a wide range of perturbation scenarios.

xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures

A versatile method to directly identify and analyze short- or long-range coupled or communicating residues in a protein conformational ensemble is of extreme relevance to achieve a complete understanding of protein dynamics and structural communication routes. Here, we present xPyder, an interface between one of the most employed molecular graphics systems, PyMOL, and the analysis of dynamical cross-correlation matrices (DCCM). The approach can also be extended, in principle, to matrices including other indexes of communication propensity or intensity between protein residues, as well as the persistence of intra- or intermolecular interactions, such as those underlying protein dynamics. The xPyder plugin for PyMOL 1.4 and 1.5 is offered as Open Source software via the GPL v2 license, and it can be found, along with the installation package, the user guide, and examples, at http://linux.btbs.unimib.it/xpyder/.

Mechanisms of intramolecular communication in a hyperthermophilic acylaminoacyl peptidase: a molecular dynamics investigation

Protein dynamics and the underlying networks of intramolecular interactions and communicating residues within the three-dimensional (3D) structure are known to influence protein function and stability, as well as to modulate conformational changes and allostery. Acylaminoacyl peptidase (AAP) subfamily of enzymes belongs to a unique class of serine proteases, the prolyl oligopeptidase (POP) family, which has not been thoroughly investigated yet. POPs have a characteristic multidomain three-dimensional architecture with the active site at the interface of the C-terminal catalytic domain and a β-propeller domain, whose N-terminal region acts as a bridge to the hydrolase domain. In the present contribution, protein dynamics signatures of a hyperthermophilic acylaminoacyl peptidase (AAP) of the prolyl oligopeptidase (POP) family, as well as of a deletion variant and alanine mutants (I12A, V13A, V16A, L19A, I20A) are reported. In particular, we aimed at identifying crucial residues for long range communications to the catalytic site or promoting the conformational changes to switch from closed to open ApAAP conformations. Our investigation shows that the N-terminal α1-helix mediates structural intramolecular communication to the catalytic site, concurring to the maintenance of a proper functional architecture of the catalytic triad. Main determinants of the effects induced by α1-helix are a subset of hydrophobic residues (V16, L19 and I20). Moreover, a subset of residues characterized by relevant interaction networks or coupled motions have been identified, which are likely to modulate the conformational properties at the interdomain interface.

Topological analysis and interactive visualization of biological networks and protein structures

Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.

Network-based models as tools hinting at nonevident protein functionality

Network-based models of proteins are popular tools employed to determine dynamic features related to the folded structure. They encompass all topological and geometric computational approaches idealizing proteins as directly interacting nodes. Topology makes use of neighborhood information of residues, and geometry includes relative placement of neighbors. Coarse-grained approaches efficiently predict alternative conformations because of inherent collectivity in the protein structure. Such collectivity is moderated by topological characteristics that also tune neighborhood structure: That rich residues have richer neighbors secures robustness toward random loss of interactions/nodes due to environmental fluctuations/mutations. Geometry conveys the additional information of force balance to network models, establishing the local shape of the energy landscape. Here, residue and/or bond perturbations are critically evaluated to suggest new experiments, as network-based computational techniques prove useful in capturing domain movements and conformational shifts resulting from environmental alterations. Evolutionarily conserved residues are optimally connected, defining a subnetwork that may be utilized for further coarsening.

Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces

Wordom is a versatile, user-friendly, and efficient program for manipulation and analysis of molecular structures and dynamics. The following new analysis modules have been added since the publication of the original Wordom paper in 2007: assignment of secondary structure, calculation of solvent accessible surfaces, elastic network model, motion cross correlations, protein structure network, shortest intra-molecular and inter-molecular communication paths, kinetic grouping analysis, and calculation of mincut-based free energy profiles. In addition, an interface with the Python scripting language has been built and the overall performance and user accessibility enhanced. The source code of Wordom (in the C programming language) as well as documentation for usage and further development are available as an open source package under the GNU General Purpose License from http://wordom.sf.net.

Conserved amino acids participate in the structure networks deputed to intramolecular communication in the lutropin receptor

The luteinizing hormone receptor (LHR) is a G protein-coupled receptor (GPCR) particularly susceptible to spontaneous pathogenic gain-of-function mutations. Protein structure network (PSN) analysis on wild-type LHR and two constitutively active mutants, combined with in vitro mutational analysis, served to identify key amino acids that are part of the regulatory network responsible for propagating communication between the extracellular and intracellular poles of the receptor. Highly conserved amino acids in the rhodopsin family GPCRs participate in the protein structural stability as network hubs in both the inactive and active states. Moreover, they behave as the most recurrent nodes in the communication paths between the extracellular and intracellular sides in both functional states with emphasis on the active one. In this respect, non-conservative loss-of-function mutations of these amino acids is expected to impair the most relevant way of communication between activating mutation sites or hormone-binding domain and G protein recognition regions.

Interaction energy based protein structure networks

The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis.

Analyzing and visualizing residue networks of protein structures

The study of individual amino acid residues and their molecular interactions in protein structures is crucial for understanding structure-function relationships. Recent work has indicated that residue networks derived from 3D protein structures provide additional insights into the structural and functional roles of interacting residues. Here, we present the new software tools RINerator and RINalyzer for the automatized generation, 2D visualization, and interactive analysis of residue interaction networks, and highlight their use in different application scenarios.