Biophysical and computational methods to analyze amino acid interaction networks in proteins

Progress in Protein-Based Hydrogels for Flexible Sensors: Insights from Casein

In recent years, the rapid advancement of flexible sensors as the cornerstone of flexible electronics has propelled a flourishing evolution within the realm of flexible electronics. Unlike traditional flexible devices, hydrogel flexible sensors have characteristic advantages such as biocompatibility, adhesion, and adjustable mechanical properties and have similar properties to human skin. Especially, biobased hydrogels have become the preferred substrate material for flexible sensors due to increased environmental pressures caused by the scarcity of petrochemical resources. In this regard, proteins possess advantages such as diverse amino acid compositions, adjustable advanced structures, chemical modifiability, the application of protein engineering techniques, and the ability to respond to various external stimuli. These enable the hydrogels constructed from them to have greater designability, flexibility, and adaptability. As a result, their applications in manufacturing various types of sensors have experienced rapid growth. This work systematically reviews the sensing mechanism of protein-based hydrogels, focusing on the preparation of protein-based hydrogels and the optimization of flexible sensors mainly from the perspective of a typical type of animal-derived protein casein. In addition, while the potential of casein is recognized, the limitations of casein-based hydrogels in flexible sensor applications are explored, and insights are provided into the development trends of next-generation sensors based on casein-based hydrogel materials.

Networks of ion-pairs are responsible for the large differences in the thermal stability of two structurally similar aminopeptidases

Aminopeptidases are an important class of enzymes for protein metabolism. Leucyl aminopeptidase (PepL) preferably removes leucine from the N-terminus of small peptides. PepL of Lacticaseibacillus casei was observed to be thermally unstable, while a structurally similar aminopeptidase T (AmpT) of Thermus thermophilus is highly stable. To understand the molecular interaction responsible for the large difference in their stability, molecular dynamics simulations were carried out to study the thermal stability of PepL and AmpT at 300 K to 450 K temperature range over 100 ns. PepL sampled a larger conformational space with a rugged free-energy landscape, while AmpT navigated a smoother energy landscape to reach the global minimum. The RMSD, RMSF, radius of gyration and principal component analysis suggested large movements in PepL than in AmpT with an increase in temperature. Analysis of residue-interaction network revealed AmpT possessing a greater number of low, medium and high energy contacts in comparison to PepL. AmpT showed a higher abundance of ion-pair clusters and ionic residues per cluster compared to PepL. Moreover, AmpT retained a greater number of high-energy contacts at elevated temperatures. These findings showed that the inherently lower stability of PepL originates from a comparatively smaller number of contacts and can be pivotal in engineering PepL for higher stability.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

Structural and binding studies of 2'- and 3-fucosyllactose and its complexes with norovirus capsid protein by molecular dynamics simulations

Human breast milk contains free oligosaccharides (Human Milk Oligosaccharides-HMOs) that help to protect breastfed infants against a variety of infectious diseases and act as decoy receptors. In breast milk, HMOs are the third most abundant compounds after lactose and lipids. Structural and conformational models of HMOs are quite crucial to studying the interaction with proteins and molecular recognition phenomenon. Molecular dynamics simulations for two trisaccharides HMOs (2'-FL and 3-FL) were carried out for 250 ns and the conformational models were subsequently substantiated by three replicate simulations. The conformer models of HMOs 2'-FL and 3-FL were deposited in the 3-Dimensional Structural Database for Sialic acid-containing CARbohydrates (3DSDSCAR) database website (www.3dsdscar.in). HMOs were then docked into the active site of norovirus capsid protein and are simulated for 100 ns duration. Each complex system was stabilized by direct and water-mediated hydrogen bonding interactions. Binding free energy calculations predict two possible binding modes for each complex system. The conformational flexibility and binding stability of the complex systems were calculated. The protein folding/unfolding and compactness seem to be better for the two HMOs. From a general perspective, we found that both 2'-FL and 3-FL exhibited higher binding efficacy towards norovirus capsid protein and according to the structural stability, 3-FL might be used as a preventive inhibitor for norovirus infection.Communicated by Ramaswamy H. Sarma.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

A computational method for the prediction and functional analysis of potential Mycobacterium tuberculosis adhesin-related proteins

OBJECTIVES

Mycobacterial adherence plays a major role in the establishment of infection within the host. Adhesin-related proteins attach to host receptors and cell-surface components. The current study aimed to utilize in-silico strategies to determine the adhesin potential of conserved hypothetical (CH) proteins.

METHODS

Computational analysis was performed on the whole Mycobacterium tuberculosis H37Rv proteome using a software program for the prediction of adhesin and adhesin-like proteins using neural networks (SPAAN) to determine the adhesin potential of CH proteins. A robust pipeline of computational analysis tools: Phyre2 and pFam for homology prediction; Mycosub, PsortB, and Loctree3 for subcellular localization; SignalP-5.0 and SecretomeP-2.0 for secretory prediction, were utilized to identify adhesin candidates.

RESULTS

SPAAN revealed 776 potential adhesins within the whole MTB H37Rv proteome. Comprehensive analysis of the literature was cross-tabulated with SPAAN to verify the adhesin prediction potential of known adhesin (n = 34). However, approximately a third of known adhesins were below the probability of adhesin (Pad) threshold (Pad ≥0.51). Subsequently, 167 CH proteins of interest were categorized using essential in-silico tools.

CONCLUSION

The use of SPAAN with supporting in-silico tools should be fundamental when identifying novel adhesins. This study provides a pipeline to identify CH proteins as functional adhesin molecules.

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.

N-glycosylation induced changes in tau protein dynamics reveal its role in tau misfolding and aggregation: A microsecond long molecular dynamics study

Various posttranslational modifications like hyperphosphorylation, O-GlcNAcylation, and acetylation have been attributed to induce the abnormal folding in tau protein. Recent in vitro studies revealed the possible involvement of N-glycosylation of tau protein in the abnormal folding and tau aggregation. Hence, in this study, we performed a microsecond long all atom molecular dynamics simulation to gain insights into the effects of N-glycosylation on Asn-359 residue which forms part of the microtubule binding region. Trajectory analysis of the stimulations coupled with essential dynamics and free energy landscape analysis suggested that tau, in its N-glycosylated form tends to exist in a largely folded conformation having high beta sheet propensity as compared to unmodified tau which exists in a large extended form with very less beta sheet propensity. Residue interaction network analysis of the lowest energy conformations further revealed that Phe378 and Lys353 are the functionally important residues in the peptide which helped in initiating the folding process and Phe378, Lys347, and Lys370 helped to maintain the stability of the protein in the folded state.

Comparison of allosteric signaling in DnaK and BiP using mutual information between simulated residue conformations

The heat shock protein 70 kDa (Hsp70) chaperone system serves as a critical component of protein quality control across a wide range of prokaryotic and eukaryotic organisms. Divergent evolution and specialization to particular organelles have produced numerous Hsp70 variants which share similarities in structure and general function, but differ substantially in regulatory aspects, including conformational dynamics and activity modulation by cochaperones. The human Hsp70 variant BiP (also known as GRP78 or HSPA5) is of therapeutic interest in the context of cancer, neurodegenerative diseases, and viral infection, including for treatment of the pandemic virus SARS-CoV-2. Due to the complex conformational rearrangements and high sequential variance within the Hsp70 protein family, it is in many cases poorly understood which amino acid mutations are responsible for biochemical differences between protein variants. In this study, we predicted residues associated with conformational regulation of human BiP and Escherichia coli DnaK. Based on protein structure networks obtained from molecular dynamics simulations, we analyzed the shared information between interaction timelines to highlight residue positions with strong conformational coupling to their environment. Our predictions, which focus on the binding processes of the chaperone's substrate and cochaperones, indicate residues filling potential signaling roles specific to either DnaK or BiP. By combining predictions of individual residues into conformationally coupled chains connecting ligand binding sites, we predict a BiP specific secondary signaling pathway associated with substrate binding. Our study sheds light on mechanistic differences in signaling and regulation between Hsp70 variants, which provide insights relevant to therapeutic applications of these proteins.

Effects of Site-Directed Mutations on the Communicability between Local Segments and Binding Pocket Distortion of Engineered GH11 Xylanases Visualized through Network Topology Analysis

Mutations occurred within the binding pocket of enzymes directly modified the interaction network between an enzyme and its substrate. However, some mutations affecting the catalytic efficiency occurred far from the binding pocket and the explanation regarding mechanisms underlying the transmission of the mechanical signal from the mutated site to the binding pocket was lacking. In this study, network topology analysis was used to characterize and visualize the changes of interaction networks caused by site-directed mutations on a GH11 xylanase from our previous study. For each structure, coordinates from molecular dynamics (MD) trajectory were obtained to create networks of representative atoms from all protein and xylooligosaccharide substrate residues, in which edges were defined between pairs of residues within a cutoff distance. Then, communicability matrices were extracted from the network to provide information on the mechanical signal transmission from the number of possible paths between any residue pairs or local protein segments. The analysis of subgraph centrality and communicability clearly showed that site-direct mutagenesis at non-reducing or reducing ends caused binding pocket distortion close to the opposite ends and created denser interaction networks. However, site-direct mutagenesis at both ends cancelled the binding pocket distortion, while enhancing the thermostability. Therefore, the network topology analysis tool on the atomistic simulations of engineered proteins could play some roles in protein design for the minimization to the correction of binding pocket tilting, which could affect the functionality and efficacy of enzymes.

Revealing the Organization of Catalytic Sequence-Defined Oligomers via Combined Molecular Dynamics Simulations and Network Analysis

Similar to biological macromolecules such as DNA and proteins, the precise control over the monomer position in sequence-defined polymers is of paramount importance for tuning their structures and properties toward achieving specific functions. Here, we apply molecular network analysis on three-dimensional structures issued from molecular dynamics simulations to decipher how the chain organization of trifunctional catalytic oligomers is influenced by the oligomer sequence and the length of oligo(ethylene oxide) spacers. Our findings demonstrate that the tuning of their primary structures is crucial for favoring cooperative interactions between the catalytic units and thus higher catalytic activities. This combined approach can assist in establishing structure-property relationships, leading to a more rational design of sequence-defined catalytic oligomers via computational chemistry.

Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations

Protein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid change at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allosteric effect. However, current MD simulations cannot reach the time scales of whole allosteric processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference model based on a graph neural network, which adopts an encoder-decoder architecture to simultaneously infer latent interactions for probing protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between distant sites in the Pin1, SOD1, and MEK1 systems. Furthermore, the model can discover allostery-related interactions earlier in the MD simulation trajectories and predict relative free energy changes upon mutations more accurately than other methods.

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet (“Structure ENSEmble NETworks”) framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.

Elucidating the Activation Mechanism of AMPK by Direct Pan-Activator PF-739

Adenosine monophosphate-activated protein kinase (AMPK) is a key energy sensor regulating the cell metabolism in response to energy supply and demand. The evolutionary adaptation of AMPK to different tissues is accomplished through the expression of distinct isoforms that can form up to 12 heterotrimeric complexes, which exhibit notable differences in the sensitivity to direct activators. To comprehend the molecular factors of the activation mechanism of AMPK, we have assessed the changes in the structural and dynamical properties of β1- and β2-containing AMPK complexes formed upon binding to the pan-activator PF-739. The analysis revealed the molecular basis of the PF-739-mediated activation of AMPK and enabled us to identify distinctive features that may justify the slightly higher affinity towards the β1−isoform, such as the β1−Asn111 to β2−Asp111 substitution, which seems to be critical for modulating the dynamical sensitivity of β1- and β2 isoforms. The results are valuable in the design of selective activators to improve the tissue specificity of therapeutic treatment.

Compensatory epistasis explored by molecular dynamics simulations

A non-negligible proportion of human pathogenic variants are known to be present as wild type in at least some non-human mammalian species. The standard explanation for this finding is that molecular mechanisms of compensatory epistasis can alleviate the mutations' otherwise pathogenic effects. Examples of compensated variants have been described in the literature but the interacting residue(s) postulated to play a compensatory role have rarely been ascertained. In this study, the examination of five human X-chromosomally encoded proteins (FIX, GLA, HPRT1, NDP and OTC) allowed us to identify several candidate compensated variants. Strong evidence for a compensated/compensatory pair of amino acids in the coagulation FIXa protein (involving residues 270 and 271) was found in a variety of mammalian species. Both amino acid residues are located within the 60-loop, spatially close to the 39-loop that performs a key role in coagulation serine proteases. To understand the nature of the underlying interactions, molecular dynamics simulations were performed. The predicted conformational change in the 39-loop consequent to the Glu270Lys substitution (associated with hemophilia B) appears to impair the protein's interaction with its substrate but, importantly, such steric hindrance is largely mitigated in those proteins that carry the compensatory residue (Pro271) at the neighboring amino acid position.

Protein conformational switch discerned via network centrality properties

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Structural basis of the selective activation of enzyme isoforms: Allosteric response to activators of β1- and β2-containing AMPK complexes

AMP-activated protein kinase (AMPK) is a key energy sensor regulating the cell metabolism in response to energy supply and demand. The evolutionary adaptation of AMPK to different tissues is accomplished through the expression of distinct isoforms that can form up to 12 complexes, which exhibit notable differences in the sensitivity to allosteric activators. To shed light into the molecular determinants of the allosteric regulation of this energy sensor, we have examined the structural and dynamical properties of β1- and β2-containing AMPK complexes formed with small molecule activators A-769662 and SC4, and dissected the mechanical response leading to active-like enzyme conformations through the analysis of interaction networks between structural domains. The results reveal the mechanical sensitivity of the α2β1 complex, in contrast with a larger resilience of the α2β2 species, especially regarding modulation by A-769662. Furthermore, binding of activators to α2β1 consistently promotes the pre-organization of the ATP-binding site, favoring the adoption of activated states of the enzyme. These findings are discussed in light of the changes in the residue content of β-subunit isoforms, particularly regarding the β1Asn111 → β2Asp111 substitution as a key factor in modulating the mechanical sensitivity of β1- and β2-containing AMPK complexes. Our studies pave the way for the design of activators tailored for improving the therapeutic treatment of tissue-specific metabolic disorders.

Substitution of a Surface-Exposed Residue Involved in an Allosteric Network Enhances Tryptophan Synthase Function in Cells

Networks of noncovalent amino acid interactions propagate allosteric signals throughout proteins. Tryptophan synthase (TS) is an allosterically controlled bienzyme in which the indole product of the alpha subunit (αTS) is transferred through a 25 Å hydrophobic tunnel to the active site of the beta subunit (βTS). Previous nuclear magnetic resonance and molecular dynamics simulations identified allosteric networks in αTS important for its function. We show here that substitution of a distant, surface-exposed network residue in αTS enhances tryptophan production, not by activating αTS function, but through dynamically controlling the opening of the indole channel and stimulating βTS activity. While stimulation is modest, the substitution also enhances cell growth in a tryptophan-auxotrophic strain of Escherichia coli compared to complementation with wild-type αTS, emphasizing the biological importance of the network. Surface-exposed networks provide new opportunities in allosteric drug design and protein engineering, and hint at potential information conduits through which the functions of a metabolon or even larger proteome might be coordinated and regulated.

Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

The main protease, M^pro/3CL^pro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, '13b' (α-ketoamide inhibitor), opened up opportunities to understand the M^pro mechanism of action. A comparative analysis of both forms of M^pro was carried out to elucidate the binding site architectural differences in the presence and absence of '13b'. Molecular dynamics simulations suggest that the presence of '13b' enhances the stability of M^pro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of M^pro. In comparison to monomer, the relatively high affinity of '13b' is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of '13b' binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent M^pro inhibitors.Communicated by Ramaswamy H. Sarma.

Computational study on the allosteric mechanism of Leishmania major IF4E-1 by 4E-interacting protein-1: Unravelling the determinants of m7GTP cap recognition

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Atomistic details on perturbations induced by Lm4E-IP1 binding are described.

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The modulation of LmIF4E-1 affinity for the cap is confirmed by energetic analyses.

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Signaling paths between the allosteric and othosteric sites of LmIF4E-1 are predicted.

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Lm4E-IP1 binding increases the side-chain entropy of W83 and R172 of LmIF4E-1.

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A mechanism of dynamic allostery is proposed for the regulation mediated by Lm4E-IP1.

During their life cycle, Leishmania parasites display a fine-tuned regulation of the mRNA translation through the differential expression of isoforms of eukaryotic translation initiation factor 4E (LeishIF4Es). The interaction between allosteric modulators such as 4E-interacting proteins (4E-IPs) and LeishIF4E affects the affinity of this initiation factor for the mRNA cap. Here, several computational approaches were employed to elucidate the molecular bases of the previously-reported allosteric modulation in L. major exerted by 4E-IP1 (Lm4E-IP1) on eukaryotic translation initiation factor 4E 1 (LmIF4E-1). Molecular dynamics (MD) simulations and accurate binding free energy calculations (ΔG_bind) were combined with network-based modeling of residue-residue correlations. We also describe the differences in internal motions of LmIF4E-1 apo form, cap-bound, and Lm4E-IP1-bound systems. Through community network calculations, the differences in the allosteric pathways of allosterically-inhibited and active forms of LmIF4E-1 were revealed. The ΔG_bind values show significant differences between the active and inhibited systems, which are in agreement with the available experimental data. Our study thoroughly describes the dynamical perturbations of LmIF4E-1 cap-binding site triggered by Lm4E-IP1. These findings are not only essential for the understanding of a critical process of trypanosomatids’ gene expression but also for gaining insight into the allostery of eukaryotic IF4Es, which could be useful for structure-based design of drugs against this protein family.

A crucial residue in the hydrophobic core of the solenoid structure of leucine rich repeats

Leucine rich repeats (LRRs) with 20-30 residues form a super helix arrangement. Individual LRRs are separated into a highly conserved segment with a highly conserved (HCS) and a variable segment (VS). In LRRs short β-strands in HCS stack in parallel, while VS adopts various secondary structures. Among eleven classes recognized, only RI-like, Cysteine-containing (CC), and GALA classes adopt an α-helix. However, the repeat unit lengths are usually different from each other. We performed some analyses based on the atomic coordinates in the known LRR structures. In the VS consensuses of the three classes, position 8 in the VS part is, in common, occupied by conserved aliphatic residue adopting an α-helix. This aliphatic residue is near to the two conserved hydrophobic residues at position 4 (in the center of β-strands) in two adjacent HCS parts. The conserved aliphatic residue plays a crucial role to preserve two parallel β-strands.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Exploring protein phosphorylation by combining computational approaches and biochemical methods

Post-translational modifications of proteins expand their functional diversity, regulating the response of cells to a variety of stimuli. Among these modifications, phosphorylation is the most ubiquitous and plays a prominent role in cell signaling. The addition of a phosphate often affects the function of a protein by altering its structure and dynamics. However, these alterations are often difficult to study and the functional and structural implications remain unresolved. New approaches are emerging to overcome common obstacles related to the production and manipulation of these samples. Here, we summarize the available methods for phosphoprotein purification and phosphomimetic engineering, highlighting the advantages and disadvantages of each. We propose a general workflow for protein phosphorylation analysis combining computational and biochemical approaches, building on recent advances that enable user-friendly and easy-to-access Molecular Dynamics simulations. We hope this innovative workflow will inform the best experimental approach to explore such post-translational modifications. We have applied this workflow to two different human protein models: the hemeprotein cytochrome c and the RNA binding protein HuR. Our results illustrate the usefulness of Molecular Dynamics as a decision-making tool to design the most appropriate phosphomimetic variant.

Network analysis of hydroxymethylbilane synthase dynamics

Hydroxymethylbilane synthase (HMBS) is one of the key enzymes of the heme biosynthetic pathway that catalyzes porphobilinogen to form the linear tetrapyrrole 1-hydroxymethylbilane through four intermediate steps. Mutations in the human HMBS (hHMBS) can lead to acute intermittent porphyria (AIP), a lethal metabolic disorder. The molecular basis of importance of the amino acid residues at the catalytic site of hHMBS has been well studied. However, the role of non-active site residues toward the activity of the enzyme and hence the association of their mutations with AIP is not known. Network-based analyses of protein structures provide a systems approach to understand the correlations of the residues through a series of inter-residue interactions. We analyzed the dynamic network representation of HMBS protein derived from five molecular dynamics trajectories corresponding to the five steps of pyrrole polymerization. We analyzed the network clusters for each stage and identified the amino acid residues and interactions responsible for the structural stability and catalytic function of the protein. The analysis of high betweenness nodes and interaction paths from the active site help in understanding the molecular basis of the effect of non-active site AIP-causing mutations on the catalytic activity.

A beginner's guide to molecular dynamics simulations and the identification of cross-correlation networks for enzyme engineering

The functional properties of proteins are decided not only by their relatively rigid overall structures, but even more importantly, by their dynamic properties. In a protein, some regions of structure exhibit highly correlated or anti-correlated motions with others, some are highly dynamic but uncorrelated, while other regions are relatively static. The residues with correlated or anti-correlated motions can form a so-called dynamic cross-correlation network, through which information can be transmitted. Such networks have been shown to be critical to allosteric transitions, and ligand binding, and have also been shown to be able to mediate epistatic interactions between mutations. As a result, they are likely to play a significant role in the development of new enzyme engineering strategies. In this chapter, protocols are provided for the assessment of dynamic cross-correlation networks, and for their application in protein engineering. Transketolase from E. coli is used as a model and the software GROMACS is applied for carrying out MD simulations to generate trajectories containing structural ensembles. The trajectory is then used for a dynamic cross correlation analysis using the R package, Bio3D. A matrix of all atom-wise cross-correlation coefficients is finally obtained, which can be displayed in a graphical representation termed a dynamical cross-correlation matrix.

Protein Interaction with Dendrimer Monolayers: Energy and Surface Topology

Protein interaction with polymers layers is a keystone in designing bio-nano devices. Polyamidoamines (PAMAMs) are well-known polymers. Zero aromatic core dendrimers (ZAC) are molecules with no proven toxic effect in cultured cells. When coating nanodevices with enzymatic systems, active sites are disturbed by an interaction with the biosystem surface. Computational methods were used in order to simulate, characterize, and quantify protein–polymer interaction. Protein corona, i.e., surface proteins disposed on a viral membrane or nanodevice outer surface, are crucial in interactions with a potential pharmacological target or receptor. Corona symmetry has been observed in the Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As a protein alpha 1 antitrypsin’s a crystallographic structure was chosen. Protein–mono dendrimer layer systems were generated using in silico methods in order to simulate their interaction. Interactions were quantified using topological and quantum mechanical strategies. Results showed that PAMAM and ZAC interact differently with alpha 1 antitrypsin. Energy and topological surfaces of protein vary accordingly with the dendrimer monolayer. Topological surfaces have a higher sensibility in describing the interactions.

Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s

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Dynamics and allosteric potentials of the RFTS domain are proposed.

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Hinge sites located at the RFTS-CD interface are key regulators for inter-domain interactions.

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Network analysis reveals local allosteric networks and inter-domain communication pathways in DNMT1.

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A potential allosteric site at the TRD interface for DNMT1 is identified.

DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.

Multi-state design of flexible proteins predicts sequences optimal for conformational change

Computational protein design of an ensemble of conformations for one protein–i.e., multi-state design–determines the side chain identity by optimizing the energetic contributions of that side chain in each of the backbone conformations. Sampling the resulting large sequence-structure search space limits the number of conformations and the size of proteins in multi-state design algorithms. Here, we demonstrated that the REstrained CONvergence (RECON) algorithm can simultaneously evaluate the sequence of large proteins that undergo substantial conformational changes. Simultaneous optimization of side chain conformations across all conformations increased sequence conservation when compared to single-state designs in all cases. More importantly, the sequence space sampled by RECON MSD resembled the evolutionary sequence space of flexible proteins, particularly when confined to predicting the mutational preferences of limited common ancestral descent, such as in the case of influenza type A hemagglutinin. Additionally, we found that sequence positions which require substantial changes in their local environment across an ensemble of conformations are more likely to be conserved. These increased conservation rates are better captured by RECON MSD over multiple conformations and thus multiple local residue environments during design. To quantify this rewiring of contacts at a certain position in sequence and structure, we introduced a new metric designated ‘contact proximity deviation’ that enumerates contact map changes. This measure allows mapping of global conformational changes into local side chain proximity adjustments, a property not captured by traditional global similarity metrics such as RMSD or local similarity metrics such as changes in φ and ψ angles.

Multi-state design can be used to engineer proteins that need to exist in multiple conformations or that bind to multiple partner molecules. In essence, multi-state design selects a compromise of protein sequences that allow for an ensemble of protein conformations, or states, associated with a particular biological function. In this paper, we used the REstrained CONvergence (RECON) algorithm with Rosetta to show that multi-state design of flexible proteins predicts sequences optimal for conformational change, mimicking mutation preferences sampled in evolution. Modeling optimal local side chain physicochemical environments within an ensemble selected significantly more native-like sequences than selections performed when all conformations states are designed independently. This outcome was particularly true for amino acids whose local side chain environment change between conformations. To quantify such contact map changes, we introduced a novel metric to show that sequence conservation is dependent on protein flexibility, i.e., changes in local side chain environments between stated limit the space of tolerated mutations. Additionally, such positions in sequence and structure are more likely to be energetically frustrated, at least in some states. Importantly, we showed that multi-state design over an ensemble of conformations (space) can explore evolutionary tolerated sequence space (time), thus enabling RECON to not only design proteins that require multiple states for function but also predict mutations that might be tolerated in native proteins but have not yet been explored by evolution. The latter aspect can be important to anticipate escape mutations, for example in pathogens or oncoproteins.

The Glu102 mutation disrupts higher-order oligomerization of the sigma 1 receptor

The sigma 1 receptor (σ1R) is a unique endoplasmic reticulum membrane protein. Its ligands have been shown to possess therapeutic potential for neurological and substance use disorders among others. The E102Q mutation of σ1R has been found to elicit familial cases of amyotrophic lateral sclerosis (ALS). Despite reports of its downstream signaling consequences, the mechanistic details of the functional impact of E102Q at molecular level are not clear. Here, we investigate the molecular mechanism of the E102Q mutation with a spectrum of biochemical, biophysical, and pharmacological approaches. Our analysis of the interaction network of σ1R indicates that a set of residues near E102 is critical for the integrity of C-terminal ligand-binding domain. However, this integrity is not affected by the E102Q and E102A mutations, which is confirmed by the radioligand binding results. Instead, the E102 mutations disrupt the connection between the C-terminal domain and the N-terminal transmembrane helix (NT-helix). Results from bioluminescence resonance energy transfer and western blot assays demonstrate that these mutations destabilize higher-order σ1R oligomers, while our molecular dynamics simulations based on a σ1R crystal structure reveal a potential mechanism by which the mutations perturb the NT-helix dynamics. Thus, we propose that E102 is at a critical position in propagating the effects of ligand binding from the C-terminal domain to the NT-helix, while the latter may be involved in forming alternative oligomer interfaces, separate from the previously reported trimer interface. Together, these results provide the first account of the molecular mechanism of σ1R dysfunction caused by E102Q.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

The impact of long-distance mutations on the Ω-loop conformation in TEM type β-lactamases

β-lactamases are hydrolytic enzymes primarily responsible for occurrence and abundance of bacteria resistant to β-lactam antibiotics. TEM type β-lactamases are formed by the parent enzyme TEM-1 and more than two hundred of its mutants. Positions for the known amino acid substitutions cover ∼30% of TEM type enzyme's sequence. These substitutions are divided into the key mutations that lead to changes in catalytic properties of β-lactamases, and the secondary ones, which role is poorly understood. In this study, Residue Interaction Networks were constructed from molecular dynamic trajectories of β-lactamase TEM-1 and its variants with two key substitutions, G238S and E240K, and their combinations with secondary ones (M182T and Q39K). Particular attention was paid to a detailed analysis of the interactions that affect conformation and mobility of the Ω-loop, representing a part of the β-lactamase active site. It was shown that key mutations weakened the stability of contact inside the Ω-loop thus increasing its mobility. Combination of three amino acid substitutions, including the 182 residue, leads to the release of R65 promoting its new contacts with N175 and D176. As a result, Ω-loop is fixed on the protein globule. The second distal mutation Q39K prevents changes in spatial position of R65, which lead to the weakening of the effect of M182T substitution and the recovery of the Ω-loop mobility. Thus, the distal secondary mutations are directed for recovering the mobility of enzyme disturbed by the key mutations responsible for expansion of substrate specificity. AbbreviationsESBLextended spectrum beta-lactamasesIRinhibitor resistant beta-lactamasesMDmolecular dynamicsRINresidue interaction networksRMSDroot mean square deviationRMSFroot mean square fluctuations.Communicated by Ramaswamy H. Sarma.

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.

Engineering Allostery into Proteins

Our ability to engineer protein structure and function has grown dramatically over recent years. Perhaps the next level in protein design is to develop proteins whose function can be regulated in response to various stimuli, including ligand binding, pH changes, and light. Endeavors toward these goals have tested and expanded on our understanding of protein function and allosteric regulation. In this chapter, we provide examples from different methods for developing new allosterically regulated proteins. These methods range from whole insertion of regulatory domains into new host proteins, to covalent attachment of photoswitches to generate light-responsive proteins, and to targeted changes to specific amino acid residues, especially to residues identified to be important for relaying allosteric information across the protein framework. Many of the examples we discuss have already found practical use in medical and biotechnology applications.

Millisecond Timescale Motions Connect Amino Acid Interaction Networks in Alpha Tryptophan Synthase

Tryptophan synthase is a model system for understanding allosteric regulation within enzyme complexes. Amino acid interaction networks were previously delineated in the isolated alpha subunit (αTS) in the absence of the beta subunit (βTS). The amino acid interaction networks were different between the ligand-free enzyme and the enzyme actively catalyzing turnover. Previous X-ray crystallography studies indicated only minor localized changes when ligands bind αTS, and so, structural changes alone could not explain the changes to the amino acid interaction networks. We hypothesized that the network changes could instead be related to changes in conformational dynamics. As such, we conducted nuclear magnetic resonance relaxation studies on different substrate- and products-bound complexes of αTS. Specifically, we collected ¹⁵N R₂ relaxation dispersion data that reports on microsecond-to-millisecond timescale motion of backbone amide groups. These experiments indicated that there are conformational exchange events throughout αTS. Substrate and product binding change specific motional pathways throughout the enzyme, and these pathways connect the previously identified network residues. These pathways reach the αTS/βTS binding interface, suggesting that the identified dynamic networks may also be important for communication with the βTS subunit.

A 5‑lipoxygenase-specific sequence motif impedes enzyme activity and confers dependence on a partner protein

Leukotrienes (LT) are lipid mediators of the inflammatory response that play key roles in diseases such as asthma and atherosclerosis. The precursor leukotriene A₄ (LTA₄) is synthesized from arachidonic acid (AA) by 5‑lipoxygenase (5-LOX), a membrane-associated enzyme, with the help of 5‑lipoxygenase-activating protein (FLAP), a nuclear transmembrane protein. In lipoxygenases the main chain carboxylate of the C-terminus is a ligand for the non-heme iron and thus part of the catalytic center. We investigated the role of a lysine-rich sequence (KKK^653-655) 20 amino acids upstream of the C-terminus unique to 5-LOX that might displace the main-chain carboxylate in the iron coordination sphere. A 5-LOX mutant in which KKK^653-655 is replaced by ENL was transfected into HEK293 cells in the absence and presence of FLAP. This mutant gave ~20-fold higher 5-LOX product levels in stimulated HEK cells relative to the wild-type 5-LOX. Co-expression of the enzymes with FLAP led to an equalization of 5-LOX products detected, with wild-type 5-LOX product levels increased and those from the mutant enzyme decreased. These data suggest that the KKK motif limits 5-LOX activity and that this attenuated activity must be compensated by the presence of FLAP as a partner protein for effective LT biosynthesis.

Singular value decomposition for the correlation of atomic fluctuations with arbitrary angle

Many proteins exhibit a critical property called allostery, which enables intra-molecular transmission of information between distal sites. Microscopically, allosteric response is closely related to correlated atomic fluctuations. Conventional correlation analysis correlates the atomic fluctuations at two sites by taking the dot product (DP) between the fluctuations, which accounts only for the parallel and antiparallel components. Here, we present a singular value decomposition (SVD) method that analyzes the correlation coefficient of fluctuation dynamics with an arbitrary angle between the correlated directions. In a model allosteric system, the second PDZ domain (PDZ2) in the human PTP1E protein, approximately one third of the strong correlations have near-perpendicular directions, which are underestimated in the conventional method. The discrimination becomes more prominent for residue pairs with larger separation. The results of the proposed SVD method are more consistent with the experimentally determined PDZ2 dynamics than those of conventional method. In addition, the SVD method improved the prediction accuracy of the allosteric sites in a dataset of 23 known allosteric monomer proteins. The proposed method may inspire extended investigation not only into allostery, but also into protein dynamics and drug design.

Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics

Dynamic communities in proteins comprise the cohesive structural units that individually exhibit rigid body motions. These can correspond to structural domains, but are usually smaller parts that move with respect to one another in a protein's internal motions, key to its functional dynamics. Previous studies emphasized their importance to understand the nature of ligand-induced allosteric regulation. These studies reported that mutations to key community residues can hinder transmission of allosteric signals among the communities. Usually molecular dynamic (MD) simulations (~ 100 ns or longer) have been used to identify the communities-a demanding task for larger proteins. In the present study, we propose that dynamic communities obtained from MD simulations can also be obtained alternatively with simpler models-the elastic network models (ENMs). To verify this premise, we compare the specific communities obtained from MD and ENMs for 44 proteins. We evaluate the correspondence in communities from the two methods and compute the extent of agreement in the dynamic cross-correlation data used for community detection. Our study reveals a strong correspondence between the communities from MD and ENM and also good agreement for the residue cross-correlations. Importantly, we observe that the dynamic communities from MD can be closely reproduced with ENMs. With ENMs, we also compare the community structures of stable and unstable mutant forms of T4 Lysozyme with its wild-type. We find that communities for unstable mutants show substantially poorer agreement with the wild-type communities than do stable mutants, suggesting such ENM-based community structures can serve as a means to rapidly identify deleterious mutants.

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

Engineered control of enzyme structural dynamics and function

Enzymes undergo a range of internal motions from local, active site fluctuations to large-scale, global conformational changes. These motions are often important for enzyme function, including in ligand binding and dissociation and even preparing the active site for chemical catalysis. Protein engineering efforts have been directed towards manipulating enzyme structural dynamics and conformational changes, including targeting specific amino acid interactions and creation of chimeric enzymes with new regulatory functions. Post-translational covalent modification can provide an additional level of enzyme control. These studies have not only provided insights into the functional role of protein motions, but they offer opportunities to create stimulus-responsive enzymes. These enzymes can be engineered to respond to a number of external stimuli, including light, pH, and the presence of novel allosteric modulators. Altogether, the ability to engineer and control enzyme structural dynamics can provide new tools for biotechnology and medicine.

Long Distance Modulation of Disorder-to-Order Transitions in Protein Allostery

Elucidation of the molecular details of allosteric communication between distant sites in a protein is key to understanding and manipulating many biological regulatory processes. Although protein disorder is acknowledged to play an important thermodynamic role in allostery, the molecular mechanisms by which this disorder is harnessed for long distance communication are known for a limited number of systems. Transcription repression by the Escherichia coli biotin repressor, BirA, is allosterically activated by binding of the small molecule effector biotinoyl-5'-AMP. The effector acts by promoting BirA dimerization, which is a prerequisite for sequence-specific binding to the biotin biosynthetic operon operator sequence. A 30 Å distance separates the effector binding and dimerization surfaces in BirA, and previous studies indicate that allostery is mediated, in part, by disorder-to-order transitions on the two coupled sites. In this work, combined experimental and computational methods have been applied to investigate the molecular basis of allosteric communication in BirA. Double-mutant cycle analysis coupled with thermodynamic measurements indicates functional coupling between residues in disordered loops on the two distant surfaces. All atom molecular dynamics simulations reveal that this coupling occurs through long distance reciprocal modulation of the structure and dynamics of disorder-to-order transitions on the two surfaces.