Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes

Phosphate uptake in PhoX: Molecular mechanisms

PhoX is a high-affinity phosphate binding protein, present in Xanthomonas citri, a phytopathogen responsible for the citrus canker disease. Performing molecular dynamics simulations and different types of computational analyses, we study the molecular mechanisms at play in relation to phosphate binding, revealing the global functioning of the protein: PhoX naturally oscillates along its global normal modes, which allow it to explore both bound and unbound conformations, eventually attracting a nearby negative phosphate ion to the highly positive electrostatic potential on its surface, particularly close to the binding pocket. There, several hydrogen bonds are formed with the two main domains of the structure. Phosphate creates, in this way, a strong bridge that connects the domains, keeping itself between them, in a tight closed conformation, explaining its high binding affinity.

Influence of point mutations on PR65 conformational adaptability: Insights from molecular simulations and nanoaperture optical tweezers

PR65 is the HEAT repeat scaffold subunit of the heterotrimeric protein phosphatase 2A (PP2A) and an archetypal tandem repeat protein. Its conformational mechanics plays a crucial role in PP2A function by opening/closing substrate binding/catalysis interface. Using in silico saturation mutagenesis, we identified PR65 "hinge" residues whose substitutions could alter its conformational adaptability and thereby PP2A function, and selected six mutations that were verified to be expressed and soluble. Molecular simulations and nanoaperture optical tweezers revealed consistent results on the specific effects of the mutations on the structure and dynamics of PR65. Two mutants observed in simulations to stabilize extended/open conformations exhibited higher corner frequencies and lower translational scattering in experiments, indicating a shift toward extended conformations, whereas another displayed the opposite features, confirmed by both simulations and experiments. The study highlights the power of single-molecule nanoaperture-based tweezers integrated with in silico approaches for exploring the effect of mutations on protein structure and dynamics.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Allo-targeting of the kinase domain: Insights from in silico studies and comparison with experiments

The eukaryotic protein kinase domain has been a broadly explored target for drug discovery, despite limitations imposed by its high sequence conservation as a shared modular domain and the development of resistance to drugs. One way of addressing those limitations has been to target its potential allosteric sites, shortly called allo-targeting, in conjunction with, or separately from, its conserved catalytic/orthosteric site that has been widely exploited. Allosteric regulation has gained importance as an alternative to overcome the drawbacks associated with the indiscriminate effect of targeting the active site, and it turned out to be particularly useful for these highly promiscuous and broadly shared kinase domains. Yet, allo-targeting often faces challenges as the allosteric sites are not as clearly defined as its orthosteric sites, and the effect on the protein function may not be unambiguously assessed. A robust understanding of the consequence of site-specific allo-targeting on the conformational dynamics of the target protein is essential to design effective allo-targeting strategies. Recent years have seen important advances in in silico identification of druggable sites and distinguishing among them those sites expected to allosterically mediate conformational switches essential to signal transmission. The present opinion underscores the utility of such computational approaches applied to the kinase domain, with the help of comparison between computational predictions and experimental observations.

Computational analysis of long-range allosteric communications in CFTR

Malfunction of the CFTR protein results in cystic fibrosis, one of the most common hereditary diseases. CFTR functions as an anion channel, the gating of which is controlled by long-range allosteric communications. Allostery also has direct bearings on CF treatment: the most effective CFTR drugs modulate its activity allosterically. Herein, we integrated Gaussian network model, transfer entropy, and anisotropic normal mode-Langevin dynamics and investigated the allosteric communications network of CFTR. The results are in remarkable agreement with experimental observations and mutational analysis and provide extensive novel insight. We identified residues that serve as pivotal allosteric sources and transducers, many of which correspond to disease-causing mutations. We find that in the ATP-free form, dynamic fluctuations of the residues that comprise the ATP-binding sites facilitate the initial binding of the nucleotide. Subsequent binding of ATP then brings to the fore and focuses on dynamic fluctuations that were present in a latent and diffuse form in the absence of ATP. We demonstrate that drugs that potentiate CFTR's conductance do so not by directly acting on the gating residues, but rather by mimicking the allosteric signal sent by the ATP-binding sites. We have also uncovered a previously undiscovered allosteric 'hotspot' located proximal to the docking site of the phosphorylated regulatory (R) domain, thereby establishing a molecular foundation for its phosphorylation-dependent excitatory role. This study unveils the molecular underpinnings of allosteric connectivity within CFTR and highlights a novel allosteric 'hotspot' that could serve as a promising target for the development of novel therapeutic interventions.

Influence of Point Mutations on PR65 Conformational Adaptability: Insights from Nanoaperture Optical Tweezer Experiments and Molecular Simulations

PR65 is the HEAT-repeat scaffold subunit of the heterotrimeric protein phosphatase 2A (PP2A) and an archetypal tandem-repeat protein, forming a spring-like architecture. PR65 conformational mechanics play a crucial role in PP2A function by opening/closing the substrate-binding/catalysis interface. Using in-silico saturation mutagenesis we identified "hinge" residues of PR65, whose substitutions are predicted to restrict its conformational adaptability and thereby disrupt PP2A function. Molecular simulations revealed that a subset of hinge mutations stabilized the extended/open conformation, whereas another had the opposite effect. By trapping in nanoaperture optical tweezer, we characterized PR65 motion and showed that the former mutants exhibited higher corner frequencies and lower translational scattering, indicating a shift towards extended conformations, whereas the latter showed the opposite behavior. Thus, experiments confirm the conformations predicted computationally. The study highlights the utility of nanoaperture-based tweezers for exploring structure and dynamics, and the power of integrating this single-molecule method with in silico approaches.

HIV Protease Hinge Region Insertions at Codon 38 Affect Enzyme Kinetics, Conformational Stability and Dynamics

HIV-1 protease is essential for the production of mature, infectious virions and is a major target in antiretroviral therapy. We successfully purified a HIV-1 subtype C variant, L38↑N↑L- 4, containing an insertion of asparagine and leucine at position 38 without the four background mutations - K20R, E35D, R57K, V82I using a modified purification protocol. Isothermal titration calorimetry indicated that 50% of the variant protease sample was in the active conformation compared to 62% of the wild type protease. The secondary structure composition of the variant protease was unaffected by the double insertion. The specific activity and kcat values of the variant protease were approximately 50% lower than the wild type protease values. The variant protease also exhibited a 1.6-fold increase in kcat/KM when compared to the wild type protease. Differential scanning calorimetry showed a 5 °C increase in Tm of the variant protease, indicating the variant was more stable than the wild type. Molecular dynamics simulations indicated the variant was more stable and compact than the wild type protease. A 3-4% increase in the flexibility of the hinge regions of the variant protease was observed. In addition, increased flexibility of the flaps, cantilever and fulcrum regions of the variant protease B chain was observed. The variant protease sampled only the closed flap conformation indicating a potential mechanism for drug resistance. The present study highlights the direct impact of a double amino acid insertion in hinge region on enzyme kinetics, conformational stability and dynamics of an HIV-1 subtype C variant protease.

Normal mode analysis and comparative study of intrinsic dynamics of alcohol oxidase enzymes from GMC protein family

Glucose-Methanol-Choline (GMC) family enzymes are very important in catalyzing the oxidation of a wide range of structurally diverse substrates. Enzymes that constitute the GMC family, share a common tertiary fold but < 25% sequence identity. Cofactor FAD, FAD binding signature motif, and similar structural scaffold of the active site are common features of oxidoreductase enzymes of the GMC family. Protein functionality mainly depends on protein three-dimensional structures and dynamics. In this study, we used the normal mode analysis method to search the intrinsic dynamics of GMC family enzymes. We have explored the dynamical behavior of enzymes with unique substrate catabolism and active site characteristics from different classes of the GMC family. Analysis of individual enzymes and comparative ensemble analysis of enzymes from different classes has shown conserved dynamic motion at FAD binding sites. The present study revealed that GMC enzymes share a strong dynamic similarity (Bhattacharyya coefficient >90% and root mean squared inner product >52%) despite low sequence identity across the GMC family enzymes. The study predicts that local deformation energy between atoms of the enzyme may be responsible for the catalysis of different substrates. This study may help that intrinsic dynamics can be used to make meaningful classifications of proteins or enzymes from different organisms.Communicated by Ramaswamy H. Sarma.

Dynamic Domain Links Substrate Binding and Catalysis in the Factor-Inhibiting-HIF-1

The interplay between active-site chemistry and functionally relevant enzyme motions can provide useful insights into selective enzyme modulation. Modulation of the hypoxia-sensing function of factor-inhibiting-HIF-1 (FIH) enzyme is a potential therapeutic strategy in disease states such as ischemia and cancer. The hypoxia-sensing function of FIH relies in major part on the tight coupling of the first half of the catalytic mechanism which involves O2 activation and eventual succinate production to the second half which involves HIF-1α/CTAD substrate hydroxylation. In this study, we demonstrate the role of a loop hinge domain in FIH (FIH102-118) called the 100s loop in maintaining this particular tight coupling. Molecular dynamics patterns from Gaussian Network Model (iGNM) database analysis of FIH identified the 100s loop as one dynamic domain containing a hinge residue (Tyr102) with a potential substrate positioning role. Enzymological and biophysical studies of the 100s loop point mutants revealed altered enzyme kinetics with the exception of the conservative FIH mutant Y102F, which suggests a sterics-related role for this residue. Removal of the bulk of Tyr102 (Y102A) resulted in succinate production, autohydroxylation, and an O2 binding environment comparable to wild-type FIH. However, the HIF-1α/CTAD substrate hydroxylation of this mutant was significantly reduced which implies that (1) the FIH loop hinge residue Tyr102 does not affect O2 activation, (2) the stacking steric interaction of Tyr102 is important in substrate positioning for productive hydroxylation, and (3) Tyr102 is important for the synchronization of O2 activation and substrate hydroxylation.

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Evolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of 'peptidase' domain of Spt16

Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses µs scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of ∼50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62% sequence identity with Sc-Spt16N) was solved at 1.7 Å resolution. Cumulative MD simulations of 6 µs were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N.Communicated by Ramaswamy H. Sarma.

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.

Conformational and mechanical stability of the isolated large subunit of membrane-bound [NiFe]-hydrogenase from Cupriavidus necator

Comprising at least a bipartite architecture, the large subunit of [NiFe]-hydrogenase harbors the catalytic nickel-iron site while the small subunit houses an array of electron-transferring Fe-S clusters. Recently, some [NiFe]-hydrogenase large subunits have been isolated showing an intact and redox active catalytic cofactor. In this computational study we have investigated one of these metalloproteins, namely the large subunit HoxG of the membrane-bound hydrogenase from Cupriavidus necator (CnMBH), targeting its conformational and mechanical stability using molecular modelling and long all-atom Gaussian accelerated molecular dynamics (GaMD). Our simulations predict that isolated HoxG is stable in aqueous solution and preserves a large portion of its mechanical properties, but loses rigidity in regions around the active site, in contrast to the MBH heterodimer. Inspired by biochemical data showing dimerization of the HoxG protein and IR measurements revealing an increased stability of the [NiFe] cofactor in protein preparations with higher dimer content, corresponding simulations of homodimeric forms were also undertaken. While the monomeric subunit contains several flexible regions, our data predicts a regained rigidity in homodimer models. Furthermore, we computed the electrostatic properties of models obtained by enhanced sampling with GaMD, which displays a significant amount of positive charge at the protein surface, especially in solvent-exposed former dimer interfaces. These data offer novel insights on the way the [NiFe] core is protected from de-assembly and provide hints for enzyme anchoring to surfaces, which is essential information for further investigations on these minimal enzymes.

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand-protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein-ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand-protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein-ligand systems, namely, adenylate kinase-di(adenosine-5')pentaphosphate, ribose binding protein-β-d-ribopyranose, DNA β-glucosyltransferase-uridine-5'-diphosphate, and G-protein α subunit-guanosine-5'-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics

The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.

Sampling of Protein Conformational Space Using Hybrid Simulations: A Critical Assessment of Recent Methods

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.

Between Two Walls: Modeling the Adsorption Behavior of β-Glucosidase A on Bare and SAM-Functionalized Gold Surfaces

The efficient immobilization of enzymes on surfaces remains a complex but central issue in the biomaterials field, which requires us to understand this process at the atomic level. Using a multiscale approach combining all-atom molecular dynamics and coarse-grain Brownian dynamics simulations, we investigated the adsorption behavior of β-glucosidase A (βGA) on bare and self-assembled monolayer (SAM)-functionalized gold surfaces. We monitored the enzyme position and orientation during the molecular dynamics (MD) trajectories and measured the contacts it forms with both surfaces. While the adsorption process has little impact on the protein conformation, it can nonetheless perturb its mechanical properties and catalytic activity. Our results show that compared to the SAM-functionalized surface, the adsorption of βGA on bare gold is more stable, but less specific, and more likely to disrupt the enzyme's function. This observation emphasizes the fact that the structural organization of proteins at the solid interface is a key point when designing devices based on enzyme immobilization, as one must find an acceptable stability-activity trade-off.

Energy Bilocalization Effect and the Emergence of Molecular Functions in Proteins

Proteins are among the most complex molecular structures, which have evolved to develop broad functions, such as energy conversion and transport, information storage and processing, communication, and regulation of chemical reactions. However, the mechanisms by which these dynamical entities coordinate themselves to perform biological tasks remain hotly debated. Here, a physical theory is presented to explain how functional dynamical behavior possibly emerge in complex/macro molecules, thanks to the effect that we term bilocalization of thermal vibrations. More specifically, our approach allows us to understand how structural irregularities lead to a partitioning of the energy of the vibrations into two distinct sets of molecular domains, corresponding to slow and fast motions. This shape-encoded spectral allocation, associated to the genetic sequence, provides a close access to a wide reservoir of dynamical patterns, and eventually allows the emergence of biological functions by natural selection. To illustrate our approach, the SPIKE protein structure of SARS-COV2 is considered.

Catalytic and binding sites prediction in globular proteins through discrete Markov chains and network centrality measures

In this work we use a discrete Markov chain approach combined with network centrality measures to identify and predict the location of active sites in globular proteins. To accomplish this, we use a three-dimensional network of proteinC_αatoms as nodes connected through weighted edges which represent the varying interaction degree between protein's atoms. We compute the mean first passage time matrixH= {H_ji} for this Markov chain and evaluate the averaged number of steps ⟨H_j⟩ to reach single noden_jin order to identify such residues that, on the average, are at the least distant from every other node. We also carry out a graph theory analysis to evaluate closeness centralityC_c, betweenness centralityC_band eigenvector centralityC_emeasures which provide relevant information about the connectivity structure and topology of theC_αprotein networks. Finally we also performed an analysis of equivalent random and regular networks of the same sizeNin terms of the average path lengthLand the average clustering coefficient⟨C⟩comparing these with the corresponding values forC_αprotein networks. Our results show that the mean-first passage time matrixHand its related quantity ⟨H_j⟩ together withC_c,C_bandC_ecan not only predict with relative high accuracy the location of active sites in globular proteins but also exhibit a high feasibility to use them to predict the existence of new regions in protein's structure to identify new potential binding or catalytic activity or, in some cases, the presence of new allosteric pathways.

Dynamic Connection between Enzymatic Catalysis and Collective Protein Motions

Enzymes employ a wide range of protein motions to achieve efficient catalysis of chemical reactions. While the role of collective protein motions in substrate binding, product release, and regulation of enzymatic activity is generally understood, their roles in catalytic steps per se remain uncertain. Here, molecular dynamics simulations, enzyme kinetics, X-ray crystallography, and nuclear magnetic resonance spectroscopy are combined to elucidate the catalytic mechanism of adenylate kinase and to delineate the roles of catalytic residues in catalysis and the conformational change in the enzyme. This study reveals that the motions in the active site, which occur on a time scale of picoseconds to nanoseconds, link the catalytic reaction to the slow conformational dynamics of the enzyme by modulating the free energy landscapes of subdomain motions. In particular, substantial conformational rearrangement occurs in the active site following the catalytic reaction. This rearrangement not only affects the reaction barrier but also promotes a more open conformation of the enzyme after the reaction, which then results in an accelerated opening of the enzyme compared to that of the reactant state. The results illustrate a linkage between enzymatic catalysis and collective protein motions, whereby the disparate time scales between the two processes are bridged by a cascade of intermediate-scale motion of catalytic residues modulating the free energy landscapes of the catalytic and conformational change processes.

Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase: An Atomistic MD Simulation and a Mutual Information-Based Network Study

Aminoacyl-tRNA synthetases (aaRSs), a family of ubiquitous and essential enzymes, can bind target tRNAs and catalyze the aminoacylation reaction in genetic code translation. In this work, we explore the dynamic properties and allosteric communication of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS) in free and bound states to understand the mechanisms of its tRNA^Phe recognition and allostery using molecular dynamics simulations combined with the torsional mutual information-based network model. Our results reveal that hmPheRS's residue mobility and inter-residue motional coupling are significantly enhanced by tRNA^Phe binding, and there occurs a strong allosteric communication which is critical for the aminoacylation reaction, suggesting the vital role of tRNA^Phe binding in the enzyme's function. The identified signaling pathways mainly make the connections between the anticodon binding domain (ABD) and catalytic domain (CAD), as well as within the CAD composed of many functional fragments and active sites, revealing the co-regulation role of them to act coordinately and achieve hmPheRS's aminoacylation function. Besides, several key residues along the communication pathways are identified to be involved in mediating the coordinated coupling between anticodon recognition at the ABD and activation process at the CAD, showing their pivotal role in the allosteric network, which are well consistent with the experimental observation. This study sheds light on the allosteric communication mechanism in hmPheRS and can provide important information for the structure-based drug design targeting aaRSs.

Networks of Networks: An Essay on Multi-Level Biological Organization

The multi-level organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism, while in the same time each protein (a single node of such interaction network) is itself a network of interacting amino-acid residues allowing coordinated motion of the macromolecule and systemic effect as allosteric behavior. Similar pictures can be drawn for structure and function of cells, organs, tissues, and ecological systems. The majority of biologists are used to think that causally relevant events originate from the lower level (the molecular one) in the form of perturbations, that “climb up” the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such causative model, stemming from the usual genotype-phenotype distinction, is not the only one. As a matter of fact, one can observe top-down, bottom-up, as well as middle-out perturbation/control trajectories. The recent complex network studies allow to go further the pure qualitative observation of the existence of both non-linear and non-bottom-up processes and to uncover the deep nature of multi-level organization. Here, taking as paradigm protein structural and interaction networks, we review some of the most relevant results dealing with between networks communication shedding light on the basic principles of complex system control and dynamics and offering a more realistic frame of causation in biology.

Normal Mode Analysis of KRas4B Reveals Partner Specific Dynamics

Ras GTPase interacts with its regulators and downstream effectors for its critical function in cellular signaling. Targeting the disrupted mechanisms in Ras-related human cancers requires understanding the distinct dynamics of these protein-protein interactions. We performed normal mode analysis (NMA) of KRas4B in wild-type or mutant monomeric and neurofibromin-1 (NF1), Son of Sevenless 1 (SOS1) or Raf-1 bound dimeric conformational states to reveal partner-specific dynamics of the protein. Gaussian network model (GNM) analysis showed that the known KRas4B lobes further partition into subdomains upon binding to its partners. Furthermore, KRas4B interactions with different partners suppress the flexibility in not only their binding sites but also distant residues in the allosteric lobe in a partner-specific way. The conformational changes can be driven by intrinsic residue fluctuations of the open state KRas4B-GDP, as we illustrated with anisotropic network model (ANM) analysis. The allosteric paths connecting the nucleotide binding residues to the allosteric site at α3-L7 portray differences in the inactive and active states. These findings help in understanding the partner-specific KRas4B dynamics, which could be utilized for therapeutic targeting.

Global Dynamics Renders Protein Sites with High Functional Response

Deep mutational scanning enables examination of the effects of many mutations at each amino acid position in a query protein, readily disclosing positions that are particularly sensitive. Mutations in these positions alter protein function the most. Here, on the premise that dynamics underlie function, we explore to what extent the measured sensitivity to mutations could be linked to-perhaps be explained by-the structural dynamics of the protein. We employ a minimalist perturbation-response approach based on the Gaussian Network Model (GNM) on a data set of seven proteins with deep mutational scanning data. The analysis shows that the mutation-sensitive positions are often of capacity to modulate the global dynamics and to intermediate allosteric interactions in the structure. With that, upon strain perturbation, these positions decrease residue fluctuations the most, affecting function via entropy changes. This is particularly relevant for positions that are distant from binding sites or other functional regions of the protein and are sensitive to mutations, nevertheless. Our results indicate that mutations in these positions allosterically manipulate protein function.

Insight into Shared Properties and Differential Dynamics and Specificity of Secretory Phospholipase A2 Family Members

Understanding generic mechanisms of functions shared by the secretory phospholipase A₂ (sPLA₂) family involved in the lipid metabolism and cell signaling and the molecular basis of function specificity for family members is an intriguing but challenging problem for biologists. Here, we explore the issue through extensive analyses using a combination of structure-based methods and bioinformatics tools on130 sPLA₂ family members. The principal component analysis of the structure ensemble reveals that the enzyme has an open-close motion which helps widen the substrate binding channel, facilitating its binding to phospholipid. Performing elastic network model and sequence analyses found that the residues critical for family functions, such as cysteine and catalytic residues, are highly conserved and undergo minimal movements, which is evolutionarily essential as their perturbation would impact the function, while the four residue regions involved in the association with the calcium ion/membrane are lowly conserved and of high mobility and large variations in low-to-intermediate frequency modes, which reflects the specificity of members. The analyses from perturbation response scanning also reveal that the above four regions with high sensitivity to an external perturbation are member-specific, suggesting their different roles in allosteric modulation, while the minimal sensitive residues are the shared characteristics across family members, which play an important role in maintaining structural stability as the folding core. This study is helpful for understanding how sequences, structures, and dynamics of sPLA₂ family members evolve to ensure their common and specific functions and can provide a guide for accurate design of proteins with finely tuned activities.

Direct coupling of oligomerization and oligomerization-driven endocytosis of the dopamine transporter to its conformational mechanics and activity

Dopamine transporter (DAT) mediates the reuptake of synaptically released dopamine, and thus controls the duration and intensity of dopamine neurotransmission. Mammalian DAT has been observed to form oligomers, although the mechanisms of oligomerization and its role in DAT activity and trafficking remain largely unknown. We discovered a series of small molecule compounds that stabilize trimers and induce high-order oligomers of DAT and concomitantly promote its clathrin-independent endocytosis. Using a combination of chemical cross-linking, fluorescence resonance energy transfer microscopy, antibody-uptake endocytosis assay, live-cell lattice light sheet microscopy, ligand binding and substrate transport kinetics analyses, and molecular modeling and simulations, we investigated molecular basis of DAT oligomerization and endocytosis induced by these compounds. Our study showed that small molecule–induced DAT oligomerization and endocytosis are favored by the inward-facing DAT conformation and involve interactions of four hydrophobic residues at the interface between transmembrane (TM) helices TM4 and TM9. Surprisingly, a corresponding quadruple DAT mutant displays altered dopamine transport kinetics and increased cocaine-analog binding. The latter is shown to originate from an increased preference for outward-facing conformation and inward-to-outward transition. Taken together, our results demonstrate a direct coupling between conformational dynamics of DAT, functional activity of the transporter, and its oligomerization leading to endocytosis. The high specificity of such coupling for DAT makes the TM4-9 hub a new target for pharmacological modulation of DAT activity and subcellular localization.

Exposing the distinctive modular behavior of β-strands and α-helices in folded proteins

Although folded proteins are commonly depicted as simplistic combinations of β-strands and α-helices, the actual properties and functions of these secondary-structure elements in their native contexts are just partly understood. The principal reason is that the behavior of individual β- and α-elements is obscured by the global folding cooperativity. In this study, we have circumvented this problem by designing frustrated variants of the mixed α/β-protein S6, which allow the structural behavior of individual β-strands and α-helices to be targeted selectively by stopped-flow kinetics, X-ray crystallography, and solution-state NMR. Essentially, our approach is based on provoking intramolecular "domain swap." The results show that the α- and β-elements have quite different characteristics: The swaps of β-strands proceed via global unfolding, whereas the α-helices are free to swap locally in the native basin. Moreover, the α-helices tend to hybridize and to promote protein association by gliding over to neighboring molecules. This difference in structural behavior follows directly from hydrogen-bonding restrictions and suggests that the protein secondary structure defines not only tertiary geometry, but also maintains control in function and structural evolution. Finally, our alternative approach to protein folding and native-state dynamics presents a generally applicable strategy for in silico design of protein models that are computationally testable in the microsecond-millisecond regime.

The physical origin of rate promoting vibrations in enzymes revealed by structural rigidity

Enzymes are the most efficient catalysts known to date. However, decades of research have failed to fully explain the catalytic power of enzymes, and most of the current attempts to uncloak the details of atomic motions at active sites remain incomplete. Here, a straightforward manner for understanding the interplay between the complex or irregular enzyme topology and dynamical effects at catalytic sites is introduced, by revealing how fast localized vibrations form spontaneously in the stiffest parts of the scaffold. While shedding light on a physical mechanism that allowed the selection of the picosecond (ps) timescale to increase the catalytic proficiency, this approach exposes the functional importance of localized motions as a by-product of the stability-function tradeoff in enzyme evolution. From this framework of analysis—directly accessible from available diffraction data—experimental strategies for engineering the catalytic rate in enzymatic proteins are proposed.

Probing the Structural Dynamics of the Plasmodium falciparum Tunneling-Fold Enzyme 6-Pyruvoyl Tetrahydropterin Synthase to Reveal Allosteric Drug Targeting Sites

The de novo folate synthesis pathway is a well-established drug target in the treatment of many infectious diseases. Antimalarial antifolate drugs have proven to be effective against malaria, however, rapid drug resistance has emerged on the two primary targeted enzymes: dihydrofolate reductase and dihydroptoreate synthase. The need to identify alternative antifolate drugs and novel metabolic targets is of imminent importance. The 6-pyruvol tetrahydropterin synthase (PTPS) enzyme belongs to the tunneling fold protein superfamily which is characterized by a distinct central tunnel/cavity. The enzyme catalyzes the second reaction step of the parasite’s de novo folate synthesis pathway and is responsible for the conversion of 7,8-dihydroneopterin to 6-pyruvoyl-tetrahydropterin. In this study, we examine the structural dynamics of Plasmodium falciparum PTPS using the anisotropic network model, to elucidate the collective motions that drive the function of the enzyme and identify potential sites for allosteric modulation of its binding properties. Based on our modal analysis, we identified key sites in the N-terminal domains and central helices which control the accessibility to the active site. Notably, the N-terminal domains were shown to regulate the open-to-closed transition of the tunnel, via a distinctive wringing motion that deformed the core of the protein. We, further, combined the dynamic analysis with motif discovery which revealed highly conserved motifs that are unique to the Plasmodium species and are located in the N-terminal domains and central helices. This provides essential structural information for the efficient design of drugs such as allosteric modulators that would have high specificity and low toxicity as they do not target the PTPS active site that is highly conserved in humans.

Dynamics-function relationship in the catalytic domains of N-terminal acetyltransferases

N-terminal acetyltransferases (NATs) belong to the superfamily of acetyltransferases. They are enzymes catalysing the transfer of an acetyl group from acetyl coenzyme A to the N-terminus of polypeptide chains. N-terminal acetylation is one of the most common protein modifications. To date, not much is known on the molecular basis for the exclusive substrate specificity of NATs. All NATs share a common fold called GNAT. A characteristic of NATs is the β6β7 hairpin loop covering the active site and forming with the α1α2 loop a narrow tunnel surrounding the catalytic site in which cofactor and polypeptide meet and exchange an acetyl group. We investigated the dynamics-function relationships of all available structures of NATs covering the three domains of Life. Using an elastic network model and normal mode analysis, we found a common dynamics pattern conserved through the GNAT fold; a rigid V-shaped groove formed by the β4 and β5 strands and splitting the fold in two dynamical subdomains. Loops α1α2, β3β4 and β6β7 all show clear displacements in the low frequency normal modes. We characterized the mobility of the loops and show that even limited conformational changes of the loops along the low-frequency modes are able to significantly change the size and shape of the ligand binding sites. Based on the fact that these movements are present in most low-frequency modes, and common to all NATs, we suggest that the α1α2 and β6β7 loops may regulate ligand uptake and the release of the acetylated polypeptide.

Molecular dynamics simulations and linear response theories jointly describe biphasic responses of myoglobin relaxation and reveal evolutionarily conserved frequent communicators

In this study, we provide a time-dependent mechanical model, taking advantage of molecular dynamics simulations, quasiharmonic analysis of molecular dynamics trajectories, and time-dependent linear response theories to describe vibrational energy redistribution within the protein matrix. The theoretical description explained the observed biphasic responses of specific residues in myoglobin to CO-photolysis and photoexcitation on heme. The fast responses were found to be triggered by impulsive forces and propagated mainly by principal modes <40 cm⁻¹. The predicted fast responses for individual atoms were then used to study signal propagation within the protein matrix and signals were found to propagate ~8 times faster across helices (4076 m/s) than within the helices, suggesting the importance of tertiary packing in the sensitivity of proteins to external perturbations. We further developed a method to integrate multiple intramolecular signal pathways and discover frequent “communicators”. These communicators were found to be evolutionarily conserved including those distant from the heme.

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities

Drosha and Dicer are RNase III family members of classes II and III, respectively, which play a major role in the maturation of micro-RNAs. The two proteins share similar domain arrangement and overall fold despite no apparent sequence homology. The overall structural and catalytic reaction similarity of both proteins, on the one hand, and differences in the substrate and its binding mechanisms, on the other, suggest that both proteins also share dynamic similarities and dissimilarities. Since dynamics is essential for protein function, a comparison at their dynamics level is fundamental for a complete understanding of the overall relations between these proteins. In this study, we present a dynamical comparison between human Drosha and Giardia Dicer. Gaussian Network Model and Anisotropic Network Model modes of motion of the proteins are calculated. Dynamical comparison is performed using global and local dynamic programming algorithms for aligning modes of motion. These algorithms were recently developed based on the commonly used Needleman-Wunsch and Smith-Waterman algorithms for global and local sequence alignment. The slowest mode of Drosha is different from that of Dicer due to its more bended posture and allow the motion of the double-stranded RNA-binding domain toward and away from its substrate. Among the five slowest modes dynamics similarity exists only for the second slow mode of motion of Drosha and Dicer. In addition, high local dynamics similarity is observed at the catalytic domains, in the vicinity of the catalytic residues. The results suggest that the proteins exert a similar catalytic mechanism using similar motions, especially at the catalytic sites.

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance.

Normal Mode Analysis as a Routine Part of a Structural Investigation

Normal mode analysis (NMA) is a technique that can be used to describe the flexible states accessible to a protein about an equilibrium position. These states have been shown repeatedly to have functional significance. NMA is probably the least computationally expensive method for studying the dynamics of macromolecules, and advances in computer technology and algorithms for calculating normal modes over the last 20 years have made it nearly trivial for all but the largest systems. Despite this, it is still uncommon for NMA to be used as a component of the analysis of a structural study. In this review, we will describe NMA, outline its advantages and limitations, explain what can and cannot be learned from it, and address some criticisms and concerns that have been voiced about it. We will then review the most commonly used techniques for reducing the computational cost of this method and identify the web services making use of these methods. We will illustrate several of their possible uses with recent examples from the literature. We conclude by recommending that NMA become one of the standard tools employed in any structural study.

Universality of fold-encoded localized vibrations in enzymes

Enzymes speed up biochemical reactions at the core of life by as much as 15 orders of magnitude. Yet, despite considerable advances, the fine dynamical determinants at the microscopic level of their catalytic proficiency are still elusive. In this work, we use a powerful mathematical approach to show that rate-promoting vibrations in the picosecond range, specifically encoded in the 3D protein structure, are localized vibrations optimally coupled to the chemical reaction coordinates at the active site. Remarkably, our theory also exposes an hithertho unknown deep connection between the unique localization fingerprint and a distinct partition of the 3D fold into independent, foldspanning subdomains that govern long-range communication. The universality of these features is demonstrated on a pool of more than 900 enzyme structures, comprising a total of more than 10,000 experimentally annotated catalytic sites. Our theory provides a unified microscopic rationale for the subtle structure-dynamics-function link in proteins.

DynOmics: dynamics of structural proteome and beyond

DynOmics (dynomics.pitt.edu) is a portal developed to leverage rapidly growing structural proteomics data by efficiently and accurately evaluating the dynamics of structurally resolved systems, from individual molecules to large complexes and assemblies, in the context of their physiological environment. At the core of the portal is a newly developed server, ENM 1.0, which permits users to efficiently generate information on the collective dynamics of any structure in PDB format, user-uploaded or database-retrieved. ENM 1.0 integrates two widely used elastic network models (ENMs)—the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM), extended to take account of molecular environment. It enables users to assess potentially functional sites, signal transduction or allosteric communication mechanisms, and protein–protein and protein–DNA interaction poses, in addition to delivering ensembles of accessible conformers reconstructed at atomic details based on the global modes of motions predicted by the ANM. The ‘environment’ is defined in a flexible manner, from lipid bilayer and crystal contacts, to substrate or ligands bound to a protein, or surrounding subunits in a multimeric structure or assembly. User-friendly interactive features permit users to easily visualize how the environment alter the intrinsic dynamics of the query systems. ENM 1.0 can be accessed at http://enm.pitt.edu/ or http://dyn.life.nthu.edu.tw/oENM/.

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site

Substrates associate and products dissociate from enzyme catalytic sites rapidly, which hampers investigations of their trajectories. The high-resolution structure of the native Hordeum exo-hydrolase HvExoI isolated from seedlings reveals that non-covalently trapped glucose forms a stable enzyme-product complex. Here, we report that the alkyl β-d-glucoside and methyl 6-thio-β-gentiobioside substrate analogues perfused in crystalline HvExoI bind across the catalytic site after they displace glucose, while methyl 2-thio-β-sophoroside attaches nearby. Structural analyses and multi-scale molecular modelling of nanoscale reactant movements in HvExoI reveal that upon productive binding of incoming substrates, the glucose product modifies its binding patterns and evokes the formation of a transient lateral cavity, which serves as a conduit for glucose departure to allow for the next catalytic round. This path enables substrate-product assisted processive catalysis through multiple hydrolytic events without HvExoI losing contact with oligo- or polymeric substrates. We anticipate that such enzyme plasticity could be prevalent among exo-hydrolases.

Enzyme substrates and products often diffuse too rapidly to assess the catalytic implications of these movements. Here, the authors characterise the structural basis of product and substrate diffusion for an exo-hydrolase and discover a substrate-product assisted processive catalytic mechanism.

Understanding the Mechanism of Direct Activation of AMP-Kinase: Toward a Fine Allosteric Tuning of the Kinase Activity

Mammalian AMP-activated protein kinase (AMPK) is a Ser/Thr protein kinase with a key role as a sensor in cellular energy homeostasis. It has a major role in numerous metabolic disorders, such as type 2 diabetes, obesity, and cancer, and hence it has gained progressive interest as a potential therapeutic target. AMPK is a heterotrimeric enzyme composed by an α-catalytic subunit and two regulatory subunits, β and γ. It is regulated by several mechanisms, including indirect activators such as metformin and direct activators such as compound A-769662. The crystal structure of AMPK bound to A-769662 has been recently reported, suggesting a hypothetical allosteric mechanism of AMPK activation assisted by phosphorylated Ser108 at the β-subunit. Here, we have studied the direct activation mechanism of A-769662 by means of molecular dynamics simulations, suggesting that the activator may act as a glue, coupling the dynamical motion of the β-subunit and the N-terminal domain of the α-subunit, and assisting the preorganization of the ATP-binding site. This is achieved through the formation of an allosteric network that connects the activator and ATP-binding sites, particularly through key interactions formed between αAsp88 and βArg83 and between βpSer108 and αLys29. Overall, these studies shed light into key mechanistic determinants of the allosteric regulation of this cellular energy sensor, and pave the way for the fine-tuning of the rational design of direct activators of this cellular energy sensor.

Characterization of Differential Dynamics, Specificity, and Allostery of Lipoxygenase Family Members

Accurate modeling of structural dynamics of proteins and their differentiation across different species can help us understand generic mechanisms of function shared by family members and the molecular basis of the specificity of individual members. We focused here on the family of lipoxygenases, enzymes that catalyze lipid oxidation, the mammalian and bacterial structures of which have been elucidated. We present a systematic method of approach for characterizing the sequence, structure, dynamics, and allosteric signaling properties of these enzymes using a combination of structure-based models and methods and bioinformatics tools applied to a data set of 88 structures. The analysis elucidates the signature dynamics of the lipoxygenase family and its differentiation among members, as well as key sites that enable its adaptation to specific substrate binding and allosteric activity.

Use multiscale simulation to explore the effects of the homodimerizations between different conformation states on the activation and allosteric pathway for the μ-opioid receptor

Recently, oligomers of G-protein coupled receptors (GPCRs) have been an important topic in the GPCR fields. However, knowledge about their structures and activation mechanisms is very limited due to the absence of crystal structures reported. In this work, we used multiscale simulations to study the effects of homodimerization between different conformation states on their activation, dynamic behaviors, and allosteric communication pathways for μ-OR. The results indicated that the dimerization of one inactive monomer with either one inactive monomer or one active one could enhance its constitutive activation. However, the conformation state of the other protomer (e.g., active or inactive) can influence the activated extent. The dimerization between the two inactive protomers leads to a negative cooperativity for their activation, which should contribute to the asymmetric activation of GPCR dimers observed in some experiments. On the other hand, for the active monomer, its dimerization with one inactive receptor could alleviate its deactivation, whereby negative and positive cooperativities can be observed between the two subunits of the dimer, depending on the different regions. Observations from protein structure network (PSN) analysis indicated that the dimerization of one inactive monomer with one active one would cause a significant drop in the number of main pathways from the ligand binding pocket to the G-protein coupled region for the inactive protomer, while the impact is minor for the active protomer. But, for the active monomer or the inactive one, its dimerization with one inactive monomer would significantly change the types of residues participating in the pathway with the highest frequency.

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

Flexibility of the "rigid" classics or rugged bottom of the folding funnels of myoglobin, lysozyme, RNase A, chymotrypsin, cytochrome c, and carboxypeptidase A1

The abilities to crystalize of a globular protein and to solve its crystal structure seem to represent triumph of the lock-and-key model of protein functionality, where the presence of unique 3D structure resembling aperiodic crystal is considered as a prerequisite for a given protein to possess specific biologic activity. The history of protein crystallography has its roots in first crystal structures of myoglobin, lysozyme, RNase A, chymotrypsin, cytochrome c, and carboxypeptidase A1 solved more than 50 y ago. This article briefly considers extensive structural information currently available for these proteins and shows that the bottoms of their folding funnels (i.e., the lowest parts of their potential energy landscapes) are not smoothed but rugged. In other words, these crystallization classics are characterized by significant conformational flexibility and are not rigid (immobile) crystal-like entities.

Node-Weighted Amino Acid Network Strategy for Characterization and Identification of Protein Functional Residues

The study of functional residues (FRs) is essential for understanding protein functions and biological processes. The amino acid network (AAN) has become an emerging paradigm for studying FRs during the past decade. Current AAN models ignore the heterogeneity of nodes and treat amino acids in the AAN as the same. However, the properties of each amino acid node are of fundamental importance. We here proposed a node-weighted AAN strategy termed the node-weighted amino acid contact energy network (NACEN) to characterize and predict three types of FRs, namely, hot spots, catalytic residues, and allosteric residues. We first constructed NACENs with their nodes weighted based on structural, sequence, physicochemical, and dynamical properties of the amino acids and then characterized the FRs with the NACEN parameters. We finally built machine learning predictors to identify each type of FR. The results revealed that residues characterized with NACEN parameters are more distinguishable between FRs and non-FRs than those with unweighted network ones. With few features for classification, NACEN yields comparable performance for FR identification and provides residue level prediction for allosteric regulation. The proposed strategy can be easily implemented to other functional residue identification. An R package is also provided for NACEN construction and analysis at http://sysbio.suda.edu.cn/NACEN/index.html .

Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database

Background

Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

Results

In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.

Conclusions

Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49.

Functional Layer-by-Layer Thin Films of Inducible Nitric Oxide (NO) Synthase Oxygenase and Polyethylenimine: Modulation of Enzyme Loading and NO-Release Activity

Nitric oxide (NO) release counteracts platelet aggregation and prevents the thrombosis cascade in the inner walls of blood vessels. NO-release coatings also prevent thrombus formation on the surface of blood-contacting medical devices. Our previous work has shown that inducible nitric oxide synthase (iNOS) films release NO fluxes upon enzymatic conversion of the substrate l-arginine. In this work, we report on the modulation of enzyme loading in layer-by-layer (LbL) thin films of inducible nitric oxide synthase oxygenase (iNOSoxy) on polyethylenimine (PEI). The layer of iNOSoxy is electrostatically adsorbed onto the PEI layer. The pH of the iNOSoxy solution affects the amount of enzyme adsorbed. The overall negative surface charge of iNOSoxy in solution depends on the pH and hence determines the density of adsorbed protein on the positively charged PEI layer. We used buffered iNOSoxy solutions adjusted to pHs 8.6 and 7.0, while saline PEI solution was used at pH 7.0. Atomic force microscopy imaging of the outermost layer shows higher protein adsorption with iNOSoxy at pH 8.6 than with a solution of iNOSoxy at pH 7.0. Graphite electrodes with PEI/iNOSoxy films show higher catalytic currents for nitric oxide reduction mediated by iNOSoxy. The higher enzyme loading translates into higher NO flux when the enzyme-modified surface is exposed to a solution containing the substrate and a source of electrons. Spectrophotometric assays showed higher NO fluxes with iNOSoxy/PEI films built at pH 8.6 than with films built at pH 7.0. Fourier transform infrared analysis of iNOSoxy adsorbed on PEI at pH 8.6 and 7.0 shows structural differences of iNOSoxy in films, which explains the observed changes in enzymatic activity. Our findings show that pH provides a strategy to optimize the NOS loading and enzyme activity in NOS-based LbL thin films, which enables improved NO release with minimum layers of PEI/NOS.