Ligand binding and circular permutation modify residue interaction network in DHFR

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Unraveling the Molecular Mechanism of Recognition of Human Interferon-Stimulated Gene Product 15 by Coronavirus Papain-Like Proteases: A Multiscale Simulation Study

The papain-like protease (PLpro) of the coronavirus (CoV) family plays an essential role in processing the viral polyprotein and immune evasion. Additional proteolytic activities of PLpro include deubiquitination and deISGylation, which can reverse the post-translational modification of cellular proteins conjugated with ubiquitin or (Ub) or Ub-like interferon-stimulated gene product 15 (ISG15). These activities regulate innate immune responses against viral infection. Thus, PLpro is a potential antiviral target. Here, we have described the structural and energetic basis of recognition of PLpro by the human ISG15 protein (hISG15) using atomistic molecular dynamics simulation across the CoV family, i.e., MERS-CoV (MCoV), SARS-CoV (SCoV), and SARS-CoV-2 (SCoV2). The cumulative simulation length for all trajectories was 32.0 μs. In the absence of the complete crystal structure of complexes, protein-protein docking was used. A mutation (R167E) was introduced across all three PLpro to study the effect of mutation on the protein-protein binding. Our study reveals that the apo-ISG15 protein remains closed while it adopts an open conformation when bound to PLpro, although the degree of openness varies across the CoV family. The binding free energy analysis suggests that hISG15 binds more strongly with SCoV2-PLpro compared to SCoV or MCoV. The intermolecular electrostatic interaction drives the hISG15-PLpro complexation. Our study showed that SCoV or MCoV-PLpro binds more strongly with the C-domain of hISG15, while SCoV2-PLpro binds more favorably the N-domain of hISG15. Overall, our study explains the molecular basis of differential deISGylating activities of PLpro among the CoV family and the specificity of SCoV2-PLpro toward hISG15.

Rosetta design with co-evolutionary information retains protein function

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

In homologous proteins, functionally or structurally important residues are strongly conserved. Thus, the consideration of conservation signals during protein design protocols can help to create sequences that are more native-like. However, the number of conserved residues is small in many proteins and not all important residues can be captured by conservation analysis. Residues are forming networks whose composition is dictated by protein structure function and thus is visible through the co-evolutionary analysis. Nowadays, advanced methods allow us to deduce these networks from multiple sequence alignments. Thus, we have implemented the novel Rosetta method termed ‘ResCue’ that informs the design protocol with co-evolutionary signals. Recapitulation designs based on ten difficult benchmarks made clear that this protocol creates sequences that are more native-like than three other, state-of-the-art design protocols.

The Allosteric Effect in Antibody-Antigen Recognition

We studied the molecular details of the recognition of antigens by the variable domain of their cognate antibodies in as well as those elicited by the constant domains, which do not directly interact with antigens. Such effects are difficult to study experimentally; however, molecular dynamics simulations and subsequent residue interaction network analysis provide insight into the allosteric communication between the antigen-binding CDR region and the constant domain. We performed MD simulations of the complex of Fab and prion-associated peptide in the apo and bound forms and follow the conformational changes in the antibody and cross-talk between its subunits and with antigens. These protocols could be generally applied for studies of other antigens-antibody recognition systems.

Structural Basis of Design and Engineering for Advanced Plant Optogenetics

In optogenetics, light-sensitive proteins are specifically expressed in target cells and light is used to precisely control the activity of these proteins at high spatiotemporal resolution. Optogenetics initially used naturally occurring photoreceptors to control neural circuits, but has expanded to include carefully designed and engineered photoreceptors. Several optogenetic constructs are based on plant photoreceptors, but their application to plant systems has been limited. Here, we present perspectives on the development of plant optogenetics, considering different levels of design complexity. We discuss how general principles of light-driven signal transduction can be coupled with approaches for engineering protein folding to develop novel optogenetic tools. Finally, we explore how the use of computation, networks, circular permutation, and directed evolution could enrich optogenetics.

Investigating Phosphorylation-Induced Conformational Changes in WNK1 Kinase by Molecular Dynamics Simulations

The With-No-Lysine (WNK) kinase is considered to be a master regulator for various cation-chloride cotransporters involved in maintaining cell-volume and ion homeostasis. Here, we have investigated the phosphorylation-induced structural dynamics of the WNK1 kinase bound to an inhibitor via atomistic molecular dynamics simulations. Results from our simulations show that the phosphorylation at Ser³⁸² could stabilize the otherwise flexible activation loop (A-loop). The intrahelix salt-bridge formed between Arg²⁶⁴ and Glu²⁶⁸ in the unphosphorylated system is disengaged after the phosphorylation, and Glu²⁶⁸ reorients itself and forms a stable salt-bridge with Arg³⁴⁸. The dynamic cross-correlation analysis shows that phosphorylation diminishes anticorrelated motions and increases correlated motions between different domains. Structural network analysis reveals that the phosphorylation causes structural rearrangements and shortens the communication path between the αC-helix and catalytic loop, making the binding pocket more suitable for accommodating the ligand. Overall, we have characterized the structural changes in the WNK kinase because of phosphorylation in the A-loop, which might help in designing rational drugs.

Tandem domain swapping: determinants of multidomain protein misfolding

•

Domain swapping refers to the exchange of structural elements between protein domains.

•

Experiments show that tandem homologous domains are prone to domain swapping.

•

Recent studies establish a framework to understand the formation of tandem domain swaps.

•

Prediction of tandem domain swaps is possible but hindered by the amount of available data.

Tandem homologous domains in proteins are susceptible to misfolding through the formation of domain swaps, non-native conformations involving the exchange of equivalent structural elements between adjacent domains. Cutting-edge biophysical experiments have recently allowed the observation of tandem domain swapping events at the single molecule level. In addition, computer simulations have shed light into the molecular mechanisms of domain swap formation and serve as the basis for methods to systematically predict them. At present, the number of studies on tandem domain swaps is still small and limited to a few domain folds, but they offer important insights into the folding and evolution of multidomain proteins with applications in the field of protein design.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles.

Understanding the structural and energetic basis of PD-1 and monoclonal antibodies bound to PD-L1: A molecular modeling perspective

BACKGROUND

The inhibitors blocking the interaction between programmed cell death protein 1(PD-1) and programmed death-ligand 1(PD-L1) can activate the immune response of T cell and eliminate cancer cells. The crystallographic studies have provided structural insights of the interactive interfaces between PD-L1 and its protein ligands. However, the hotspot residues on PD-L1 as well as structural and energetic basis for different protein ligands still need to be further investigated.

METHODS

Molecular modeling methods including molecular dynamics simulation, per-residue free energy decomposition, virtual alanine scanning mutagenesis and residue-residue contact analysis were used to qualitatively and quantitatively analyze the interactions between PD-L1 and different protein ligands.

RESULTS

The results of virtual alanine scanning mutagenesis suggest that Y56, Q66, M115, D122, Y123, R125 are the hotspot residues on PD-L1. The residue-residue contact analysis further shows that PD-1 interacts with PD-L1 mainly by F and G strands while monoclonal antibodies like avelumab and BMS-936559 mainly interact with PD-L1 by CDR2 and CDR3 loops of the heavy chain.

CONCLUSIONS

A structurally similar β-hairpin peptide with 13 or 14 residues was extracted from each protein ligand and these β-hairpin peptides were found tightly binding to the putative hotspot residues on PD-L1.

GENERAL SIGNIFICANCE

This study recognizes the hotspot residues on PD-L1 and uncovers the common structural and energetic basis of different protein ligands binding to PD-L1. These results will be valuable for the design of small molecule or peptide inhibitors targeting on PD-L1.

Selective inhibition mechanism of RVX-208 to the second bromodomain of bromo and extraterminal proteins: insight from microsecond molecular dynamics simulations

RVX-208 is a recently reported inhibitor of bromo and extraterminal (BET) family proteins (including BRD2-4 and BRDT) with selectivity for the second bromodomain (BD2), currently in phase III clinical trials. Despite of its promising antitumor activity, due to the conserved folds of the first and second bromodomains (BD1 and BD2), the detailed selectivity mechanism of RVX-208 towards BD2 over BD1 is still unknown. To elucidate selective inhibition mechanism of RVX-208 to BD2, microsecond molecular dynamics simulations were performed in this study for BRD2-BD1, BRD2-BD2 and BRD4-BD1 with and without RVX-208, respectively. Binding free energy calculations show that there exists strongest interaction between RVX-208 and BRD2-BD2. Leu383 and Asn429 are two most important residues of BRD2-BD2 for binding to RVX-208. Structural network analysis reveals that RVX-208 can shorten the communication path of ZA and BC loops in BRD2-BD2 pocket, making pocket more suitable to accommodate RVX-208. Additionally, different behaviors of His433 (Asp160 in BRD2-BD1) and Val435 (Ile162 in BRD2-BD1) in BRD2-BD2 are key factors responsible for selective binding of RVX-208 to BRD2-BD2. The proposed selective inhibition mechanism of RVX-208 to BRD2-BD2 can be helpful for rational design of novel selective inhibitors of the second bromodomain of BET family proteins.

Molecular docking as a popular tool in drug design, an in silico travel

New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

Insights Into the Allosteric Inhibition of the SUMO E2 Enzyme Ubc9

Conjugation of the small ubiquitin-like modifier (SUMO) to protein substrates is an important disease-associated posttranslational modification, although few inhibitors of this process are known. Herein, we report the discovery of an allosteric small-molecule binding site on Ubc9, the sole SUMO E2 enzyme. An X-ray crystallographic screen was used to identify two distinct small-molecule fragments that bind to Ubc9 at a site distal to its catalytic cysteine. These fragments and related compounds inhibit SUMO conjugation in biochemical assays with potencies of 1.9-5.8 mm. Mechanistic and biophysical analyses, coupled with molecular dynamics simulations, point toward ligand-induced rigidification of Ubc9 as a mechanism of inhibition.

Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation

The computational analysis of residue interaction networks dissects the allosteric effects of inhibitor-induced BRAF kinase dimerization and paradoxical activation.

Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins

Hsp70 and Hsp110 chaperones play an important role in regulating cellular processes that involve protein folding and stabilization, which are essential for the integrity of signaling networks. Although many aspects of allosteric regulatory mechanisms in Hsp70 and Hsp110 chaperones have been extensively studied and significantly advanced in recent experimental studies, the atomistic picture of signal propagation and energetics of dynamics-based communication still remain unresolved. In this work, we have combined molecular dynamics simulations and protein stability analysis of the chaperone structures with the network modeling of residue interaction networks to characterize molecular determinants of allosteric mechanisms. We have shown that allosteric mechanisms of Hsp70 and Hsp110 chaperones may be primarily determined by nucleotide-induced redistribution of local conformational ensembles in the inter-domain regions and the substrate binding domain. Conformational dynamics and energetics of the peptide substrate binding with the Hsp70 structures has been analyzed using free energy calculations, revealing allosteric hotspots that control negative cooperativity between regulatory sites. The results have indicated that cooperative interactions may promote a population-shift mechanism in Hsp70, in which functional residues are organized in a broad and robust allosteric network that can link the nucleotide-binding site and the substrate-binding regions. A smaller allosteric network in Hsp110 structures may elicit an entropy-driven allostery that occurs in the absence of global structural changes. We have found that global mediating residues with high network centrality may be organized in stable local communities that are indispensable for structural stability and efficient allosteric communications. The network-centric analysis of allosteric interactions has also established that centrality of functional residues could correlate with their sensitivity to mutations across diverse chaperone functions. This study reconciles a wide spectrum of structural and functional experiments by demonstrating how integration of molecular simulations and network-centric modeling may explain thermodynamic and mechanistic aspects of allosteric regulation in chaperones.

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.

Small-world networks of residue interactions in the Abl kinase complexes with cancer drugs: topology of allosteric communication pathways can determine drug resistance effects

Computational modelling of efficiency and robustness of the residue interaction networks and allosteric pathways in kinase structures can characterize protein kinase sensitivity to drug binding and drug resistance effects.

Graphlet signature-based scoring method to estimate protein-ligand binding affinity

Over the years, various computational methodologies have been developed to understand and quantify receptor-ligand interactions. Protein-ligand interactions can also be explained in the form of a network and its properties. The ligand binding at the protein-active site is stabilized by formation of new interactions like hydrogen bond, hydrophobic and ionic. These non-covalent interactions when considered as links cause non-isomorphic sub-graphs in the residue interaction network. This study aims to investigate the relationship between these induced sub-graphs and ligand activity. Graphlet signature-based analysis of networks has been applied in various biological problems; the focus of this work is to analyse protein-ligand interactions in terms of neighbourhood connectivity and to develop a method in which the information from residue interaction networks, i.e. graphlet signatures, can be applied to quantify ligand affinity. A scoring method was developed, which depicts the variability in signatures adopted by different amino acids during inhibitor binding, and was termed as GSUS (graphlet signature uniqueness score). The score is specific for every individual inhibitor. Two well-known drug targets, COX-2 and CA-II and their inhibitors, were considered to assess the method. Residue interaction networks of COX-2 and CA-II with their respective inhibitors were used. Only hydrogen bond network was considered to calculate GSUS and quantify protein-ligand interaction in terms of graphlet signatures. The correlation of the GSUS with pIC50 was consistent in both proteins and better in comparison to the Autodock results. The GSUS scoring method was better in activity prediction of molecules with similar structure and diverse activity and vice versa. This study can be a major platform in developing approaches that can be used alone or together with existing methods to predict ligand affinity from protein-ligand complexes.

Energetic pathway sampling in a protein interaction domain

The affinity and specificity of protein-ligand interactions are influenced by energetic crosstalk within the protein domain. However, the molecular details of such intradomain allostery are still unclear. Here, we have experimentally detected and computationally predicted interaction pathways in the postsynaptic density 95/discs large/zonula occludens 1 (PDZ)-peptide ligand model system using wild-type and circularly permuted PDZ proteins. The circular permutant introduced small perturbations in the tertiary structure and a concomitant rewiring of allosteric pathways, allowing us to describe how subtle changes may reshape energetic signaling. The results were analyzed in the context of other members of the PDZ family, which were found to contain distinct interaction pathways for different peptide ligands. The data reveal a fascinating scenario whereby several energetic pathways are sampled within one single domain and distinct pathways are activated by specific protein ligands.

Frequency response of a protein to local conformational perturbations

Signals created by local perturbations are known to propagate long distances through proteins via backbone connectivity and nonbonded interactions. In the current study, signal propagation from the flexible ligand binding loop to the rest of Protein Tyrosine Phosphatase 1B (PTP1B) was investigated using frequency response techniques. Using restrained Targeted Molecular Dynamics (TMD) potential on WPD and R loops, PTP1B was driven between its crystal structure conformations at different frequencies. Propagation of the local perturbation signal was manifested via peaks at the fundamental frequency and upper harmonics of 1/f distributed spectral density of atomic variables, such as Cα atoms, dihedral angles, or polar interaction distances. Frequency of perturbation was adjusted high enough (simulation length >∼10×period of a perturbation cycle) not to be clouded by random diffusional fluctuations, and low enough (<∼0.8 ns(-1)) not to attenuate the propagating signal and enhance the contribution of the side-chains to the dissipation of the signals. Employing Discrete Fourier Transform (DFT) to TMD simulation trajectories of 16 cycles of conformational transitions at periods of 1.2 to 5 ns yielded Cα displacements consistent with those obtained from crystal structures. Identification of the perturbed atomic variables by statistical t-tests on log-log scale spectral densities revealed the extent of signal propagation in PTP1B, while phase angles of the filtered trajectories at the fundamental frequency were used to cluster collectively fluctuating elements. Hydrophobic interactions were found to have a higher contribution to signal transduction between side-chains compared to the role of polar interactions. Most of in-phase fluctuating residues on the signaling pathway were found to have high identity among PTP domains, and located over a wide region of PTP1B including the allosteric site. Due to its simplicity and efficiency, the suggested technique may find wide applications in identification of signaling pathways of different proteins.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

Protein conformational disorder and enzyme catalysis

Though lacking a well-defined three-dimensional structure, intrinsically unstructured proteins are ubiquitous in nature. These molecules play crucial roles in many cellular processes, especially signaling and regulation. Surprisingly, even enzyme catalysis can tolerate substantial disorder. This observation contravenes conventional wisdom but is relevant to an understanding of how protein dynamics modulates enzyme function. This chapter reviews properties and characteristics of disordered proteins, emphasizing examples of enzymes that lack defined structures, and considers implications of structural disorder for catalytic efficiency and evolution.

Structured crowding and its effects on enzyme catalysis

Macromolecular crowding decreases the diffusion rate, shifts the equilibrium of protein-protein and protein-substrate interactions, and changes protein conformational dynamics. Collectively, these effects contribute to enzyme catalysis. Here we describe how crowding may bias the conformational change and dynamics of enzyme populations and in this way affect catalysis. Crowding effects have been studied using artificial crowding agents and in vivo-like environments. These studies revealed a correlation between protein dynamics and function in the crowded environment. We suggest that crowded environments be classified into uniform crowding and structured crowding. Uniform crowding represents random crowding conditions created by synthetic particles with a narrow size distribution. Structured crowding refers to the highly coordinated cellular environment, where proteins and other macromolecules are clustered and organized. In structured crowded environments the perturbation of protein thermal stability may be lower; however, it may still be able to modulate functions effectively and dynamically. Dynamic, allosteric enzymes could be more sensitive to cellular perturbations if their free energy landscape is flatter around the native state; on the other hand, if their free energy landscape is rougher, with high kinetic barriers separating deep minima, they could be more robust. Above all, cells are structured; and this holds both for the cytosol and for the membrane environment. The crowded environment is organized, which limits the search, and the crowders are not necessarily inert. More likely, they too transmit allosteric effects, and as such play important functional roles. Overall, structured cellular crowding may lead to higher enzyme efficiency and specificity.

Prediction of protein-binding areas by small-world residue networks and application to docking

BACKGROUND

Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.

RESULTS

Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.

CONCLUSIONS

The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.

Molecular evolution of protein conformational changes revealed by a network of evolutionarily coupled residues

An improved understanding of protein conformational changes has broad implications for elucidating the mechanisms of various biological processes and for the design of protein engineering experiments. Understanding rearrangements of residue interactions is a key component in the challenge of describing structural transitions. Evolutionary properties of protein sequences and structures are extensively studied; however, evolution of protein motions, especially with respect to interaction rearrangements, has yet to be explored. Here, we investigated the relationship between sequence evolution and protein conformational changes and discovered that structural transitions are encoded in amino acid sequences as coevolving residue pairs. Furthermore, we found that highly coevolving residues are clustered in the flexible regions of proteins and facilitate structural transitions by forming and disrupting their interactions cooperatively. Our results provide insight into the evolution of protein conformational changes and help to identify residues important for structural transitions.

Enzyme dynamics point to stepwise conformational selection in catalysis

Recent data increasingly reveal that conformational dynamics are indispensable to enzyme function throughout the catalytic cycle, in substrate recruiting, chemical transformation, and product release. Conformational transitions may involve conformational selection and induced fit, which can be viewed as a special case in the catalytic network. NMR, X-ray crystallography, single-molecule FRET, and simulations clearly demonstrate that the free enzyme dynamics already encompass all the conformations necessary for substrate binding, preorganization, transition-state stabilization, and product release. Conformational selection and substate population shift at each step of the catalytic turnover can accommodate enzyme specificity and efficiency. Within such a framework, entropy can have a larger role in conformational dynamics than in direct energy transfer in dynamically promoted catalysis.

Unravelling the design principles for single protein mechanical strength

In recent years single molecule manipulation techniques have improved to the extent that measurements of the mechanical strength of single proteins can now be undertaken routinely. This powerful new tool, coupled with theoretical frameworks to characterise the unfolding process, has enabled significant progress to be made in understanding the physical mechanisms that underlie protein mechanical strength. These design concepts have allowed the search for proteins with novel, mechanically strong folds to be automated and for previously mechanically characterised proteins to be engineered rationally. Methods to achieve the latter are diverse and include re-engineering of specific hydrophobic core residues, changing solvent conditions and the 'cross-linking' of side-chains that are separated in the rate-limiting unfolding transition. Predicting the mechanical behaviour of larger proteins and those with more complex structures remains a significant challenge while on-going instrument development is beginning to allow the examination of mechanical strength of protein across a wide range of force loading rates. The integral role of force in biology and the potential for exploitation of catalytic and structural proteins as functional bio-materials makes this a particularly important area of research.

Graphlet kernels for prediction of functional residues in protein structures

We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence-based predictor and our implementation of the FEATURE framework. On both tasks, the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs.

Allostery and conformational free energy changes in human tryptophanyl-tRNA synthetase from essential dynamics and structure networks

The interdependence of the concept of allostery and enzymatic catalysis, and they being guided by conformational mobility is gaining increased prominence. However, to gain a molecular level understanding of allostery and hence of enzymatic catalysis, it is of utter importance that the networks of amino acids participating in allostery be deciphered. Our lab has been exploring the methods of network analysis combined with molecular dynamics simulations to understand allostery at molecular level. Earlier we had outlined methods to obtain communication paths and then to map the rigid/flexible regions of proteins through network parameters like the shortest correlated paths, cliques, and communities. In this article, we advance the methodology to estimate the conformational populations in terms of cliques/communities formed by interactions including the side-chains and then to compute the ligand-induced population shift. Finally, we obtain the free-energy landscape of the protein in equilibrium, characterizing the free-energy minima accessed by the protein complexes. We have chosen human tryptophanyl-tRNA synthetase (hTrpRS), a protein responsible for charging tryptophan to its cognate tRNA during protein biosynthesis for this investigation. This is a multidomain protein exhibiting excellent allosteric communication. Our approach has provided valuable structural as well as functional insights into the protein. The methodology adopted here is highly generalized to illuminate the linkage between protein structure networks and conformational mobility involved in the allosteric mechanism in any protein with known structure.

The origin of allosteric functional modulation: multiple pre-existing pathways

Although allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications, and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the observed functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.

Functional correlation of bacterial LuxS with their quaternary associations: interface analysis of the structure networks

Background

The genome of a wide variety of prokaryotes contains the luxS gene homologue, which encodes for the protein S-ribosylhomocysteinelyase (LuxS). This protein is responsible for the production of the quorum sensing molecule, AI-2 and has been implicated in a variety of functions such as flagellar motility, metabolic regulation, toxin production and even in pathogenicity. A high structural similarity is present in the LuxS structures determined from a few species. In this study, we have modelled the structures from several other species and have investigated their dimer interfaces. We have attempted to correlate the interface features of LuxS with the phenotypic nature of the organisms.

Results

The protein structure networks (PSN) are constructed and graph theoretical analysis is performed on the structures obtained from X-ray crystallography and on the modelled ones. The interfaces, which are known to contain the active site, are characterized from the PSNs of these homodimeric proteins. The key features presented by the protein interfaces are investigated for the classification of the proteins in relation to their function. From our analysis, structural interface motifs are identified for each class in our dataset, which showed distinctly different pattern at the interface of LuxS for the probiotics and some extremophiles. Our analysis also reveals potential sites of mutation and geometric patterns at the interface that was not evident from conventional sequence alignment studies.

Conclusion

The structure network approach employed in this study for the analysis of dimeric interfaces in LuxS has brought out certain structural details at the side-chain interaction level, which were elusive from the conventional structure comparison methods. The results from this study provide a better understanding of the relation between the luxS gene and its functional role in the prokaryotes. This study also makes it possible to explore the potential direction towards the design of inhibitors of LuxS and thus towards a wide range of antimicrobials.

Revealing unique properties of the ribosome using a network based analysis

The ribosome is a complex molecular machine that offers many potential sites for functional interference, therefore representing a major target for antibacterial drugs. The growing number of high-resolution structures of ribosomes from different organisms, in free form and in complex with various ligands, provides unique data for structural and comparative analyses of RNA structures. We model the ribosome structure as a network, where nucleotides are represented as nodes and intermolecular interactions as edges. As shown previously for proteins, we found that the major functional sites of the ribosome exhibit significantly high centrality measures. Specifically, we demonstrate that mutations that strongly affect ribosome function and assembly can be distinguished from mild mutations based on their network properties. Furthermore, we observed that closeness centrality of the rRNA nucleotides is highly conserved in the bacteria, suggesting the network representation as a comparative tool for the ribosome analysis. Finally, we suggest a global topology perspective to characterize functional sites and to reveal the unique properties of the ribosome.

Computer-based screening of functional conformers of proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.

Proteins participate in most of the doings of the cells through a variety of interactions. There is an intimate relationship between the function of a protein and its three-dimensional structure, but understanding this relationship remains an unsolved problem, in part due to the limited information on protein structures bound to other biological molecules. On the other hand, thousands of protein structures in the unbound or free form, are made public every year and these differ from those of the bound structures. How to predict the protein structure in the bound form may assist researchers in understanding the structure/function relationship. Here we report that protein structures bound to other molecules tend to present, as central amino acids, those that are critical for binding other molecules. This feature allowed us to identify the protein structures known to be involved in protein interactions from a screening of thousands of structures derived from the free form.