Association of putative concave protein-binding sites with the fluctuation behavior of residues

Surface plasmon resonance aptasensing and computational analysis of Staphylococcus aureus IsdA surface protein

Staphylococcus aureus (S. aureus), a common foodborne pathogen, poses significant public health challenges due to its association with various infectious diseases. A key player in its pathogenicity, which is the IsdA protein, is an essential virulence factor in S. aureus infections. In this work, we present an integrated in-silico and experimental approach using MD simulations and surface plasmon resonance (SPR)-based aptasensing measurements to investigate S. aureus biorecognition via IsdA surface protein binding. SPR, a powerful real-time and label-free technique, was utilized to characterize interaction dynamics between the aptamer and IsdA protein, and MD simulations was used to characterize the stable and dynamic binding regions. By characterizing and optimizing pivotal parameters such as aptamer concentration and buffer conditions, we determined the aptamer's binding performance. Under optimal conditions of pH 7.4 and 150 mM NaCl concentration, the kinetic parameters were determined; ka = 3.789 × 104/Ms, kd = 1.798 × 103/s, and KD = 4.745 × 10-8 M. The simulations revealed regions of interest in the IsdA-aptamer complex. Region I, which includes interactions between amino acid residues H106 and R107 and nucleotide residues 9G, 10U, 11G and 12U of the aptamer, had the strongest interaction, based on ΔG and B-factor values, and hence contributed the most to the stability of the interaction. Region II, which covers residue 37A reflects the dynamic nature of the interaction due to frequent contacts. The approach presents a rigorous characterization of aptamer-IsdA binding behavior, supporting the potential application of the IsdA-binding aptamer system for S. aureus biosensing.

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures

Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains challenging. In this study, we introduce an easy-to-use web server BindWeb for ligand-specific and ligand-general binding residue and pocket prediction from protein structures. BindWeb integrates a graph neural network GraphBind with a hybrid convolutional neural network and bidirectional long short-term memory network DELIA to identify binding residues. Furthermore, BindWeb clusters the predicted binding residues to binding pockets with mean shift clustering. The experimental results and case study demonstrate that BindWeb benefits from the complementarity of two base methods. BindWeb is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/BindWeb/.

Adaptive Steered Molecular Dynamics of the Long-Distance Unfolding of Neuropeptide Y

Neuropeptide Y (NPY) has been found to adopt two stable conformations in vivo: (1) a monomeric form called the PP-fold in which a polyproline tail is folded onto an α-helix via a β-turn and (2) a dimeric form of the unfolded proteins in which the α-helices interact with each other via side chains. The transition pathway and rates between the two conformations remain unknown and are important to the nature of the binding of the protein. Toward addressing this question, the present work suggests that the unfolding of the PP-fold is too slow to play a role in NPY monomeric binding unless the receptor catalyzes it to do so. Specifically, the dynamics and structural changes of the unfolding of a monomeric NPY protein have been investigated in this work. Temperature accelerated molecular dynamics (MD) simulations at 500 K under constant (N,V,E) conditions suggests a hinge-like unraveling of the tail rather than a random unfolding. The free energetics of the proposed unfolding pathway have been described using an adaptive steered MD (SMD) approach at various temperatures. This approach generalizes the use of Jarzynski's equality through a series of stages that allows for better convergence along nonlinear and long-distance pathways. Results acquired using this approach provide a potential of mean force (PMF) with narrower error bars and are consistent with some of the earlier reports on the qualitative behavior of NPY binding.

Analysis of core-periphery organization in protein contact networks reveals groups of structurally and functionally critical residues

The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core-periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers - having higher core order - with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core-periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.

Cutoff lensing: predicting catalytic sites in enzymes

Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.

Structural and dynamics aspects of ASC speck assembly

Activation of the inflammasome is accompanied by rapid formation of a micrometer-sized perinuclear structure called the ASC speck, a platform for caspase-1 activity. The ASC speck is often referred to as an aggregate and shares certain features with aggresomes. It is thus an open question whether the ASC speck formation takes place via nonspecific aggregation of hydrophobic patches or specific interactions of its domains; PYD and CARD, which belong to the death fold superfamily. Bringing together structure and dynamics studies using the Gaussian network model of PYD and CARD, and molecular dynamics simulations of the wild-type and in silico mutated PYD, with the mutational analysis on the ASC structure and its separate domains in human cells, we show that the ASC speck is an organized structure with at least two levels of distinct compaction mechanisms based on the specific interactions of PYD and CARD.

Population shift of binding pocket size and dynamic correlation analysis shed new light on the anticooperative mechanism of PII protein

PII protein is one of the largest families of signal transduction proteins in archaea, bacteria, and plants, controlling key processes of nitrogen assimilation. An intriguing characteristic for many PII proteins is that the three ligand binding sites exhibit anticooperative allosteric regulation. In this work, PII protein from Synechococcus elongatus, a model for cyanobacteria and plant PII proteins, is utilized to reveal the anticooperative mechanism upon binding of 2-oxoglutarate (2-OG). To this end, a method is proposed to define the binding pocket size by identifying residues that contribute greatly to the binding of 2-OG. It is found that the anticooperativity is realized through population shift of the binding pocket size in an asymmetric manner. Furthermore, a new algorithm based on the dynamic correlation analysis is developed and utilized to discover residues that mediate the anticooperative process with high probability. It is surprising to find that the T-loop, which is believed to be responsible for mediating the binding of PII with its target proteins, also takes part in the intersubunit signal transduction process. Experimental results of PII variants further confirmed the influence of T-loop on the anticooperative regulation, especially on binding of the third 2-OG. These discoveries extend our understanding of the PII T-loop from being essential in versatile binding of target protein to signal-mediating in the anticooperative allosteric regulation.

Hot spots in a network of functional sites

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.

Tackling the challenges posed by target flexibility in drug design

IMPORTANCE OF THE FIELD

Current computational docking methods are often effective in predicting accurate drug-binding geometries in cases of relatively rigid target structures. However, binding of drug-like ligands to protein receptor molecules frequently involves or even requires conformational adaptation. Realistic prediction of ligand-receptor binding geometries and complex stability needs in many cases an appropriate inclusion of conformational changes, not only for the ligand, but also for the receptor molecule.

AREAS COVERED IN THIS REVIEW

Recent approaches to efficiently account for target receptor flexibility during docking simulations are reviewed.

WHAT THE READER WILL GAIN

The reader will gain insights into methods to efficiently treat protein side-chain flexibility and approaches for continuous adaptation of backbone conformations in pre-calculated essential or soft collective degrees of freedom. In addition, molecular dynamics or Monte Carlo based methods providing simultaneous inclusion of receptor and ligand flexibility are discussed as well as promising new developments to generate conformationally diverse ensembles of a protein structure. The large variety of possible conformational changes in proteins on ligand binding is illustrated for the enzyme reverse transcriptase of HIV-1, which is an important drug target.

TAKE HOME MESSAGE

If the backbone conformation of the binding site does not change, current docking programs can perform well by taking side-chain reorientations into account only. Future progress to account for full target flexibility in docking requires both accurate prediction of the essential modes of backbone motion and improvements in scoring to enhance selectivity. Thus, the scoring function should realistically cover energetic and particularly entropic contributions to binding, which would allow more realistic estimates of binding free energies.

Predicting important residues and interaction pathways in proteins using Gaussian Network Model: binding and stability of HLA proteins

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.

We propose a statistical thermodynamics model for determining structurally and functionally important residues in ligand-protein interactions. Our method identifies the path that the protein uses in transferring information from one point to the other. We show that a few energetically active residues are most efficient in energy exchange with the surroundings acting as ‘energy gates’. The remaining important residues that we identify are situated along the interaction path. These are the hub residues. Strong correlations exist between energy gates and hub residues along the interaction path, thus relating to allostery and cooperative binding. We studied the structure-function, ligand binding and allosteric activities of ten models of HLA Class I proteins of the immune system. Five of these models belong to the HLA-B*2705 allele and are strongly associated with a chronic inflammatory rheumatic disease. The remaining five from the HLA-B*2709 allele of the same protein are the corresponding properly functioning ones. We show that differences in the contact maps of the two types lead to significant and consistent changes in the fluctuation profile, making the HLA-B*2705 alleles respond too strongly to perturbation.

DNABINDPROT: fluctuation-based predictor of DNA-binding residues within a network of interacting residues

DNABINDPROT is designed to predict DNA-binding residues, based on the fluctuations of residues in high-frequency modes by the Gaussian network model. The residue pairs that display high mean-square distance fluctuations are analyzed with respect to DNA binding, which are then filtered with their evolutionary conservation profiles and ranked according to their DNA-binding propensities. If the analyses are based on the exact outcome of fluctuations in the highest mode, using a conservation threshold of 5, the results have a sensitivity, specificity, precision and accuracy of 9.3%, 90.5%, 18.1% and 78.6%, respectively, on a dataset of 36 unbound–bound protein structure pairs. These values increase up to 24.3%, 93.4%, 45.3% and 83.3% for the respective cases, when the neighboring two residues are considered. The relatively low sensitivity appears with the identified residues being selective and susceptible more for the binding core residues rather than all DNA-binding residues. The predicted residues that are not tagged as DNA-binding residues are those whose fluctuations are coupled with DNA-binding sites. They are in close proximity as well as plausible for other functional residues, such as ligand and protein–protein interaction sites. DNABINDPROT is free and open to all users without login requirement available at: http://www.prc.boun.edu.tr/appserv/prc/dnabindprot/.

Energy based approach for understanding the recognition mechanism in protein-protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

Sequence and structural analysis of binding site residues in protein-protein complexes

The binding sites in protein-protein complexes have been identified with different methods including atomic contacts, reduction in solvent accessibility and interaction energy between the interacting partners. In our earlier work, we have developed an energy-based criteria for identifying the binding sites in protein-protein complexes, which showed that the interacting residues are different from that obtained with distance-based methods. In this work, we analyzed the binding site residues based on sequence and structural properties, such as, neighboring residues, secondary structure, solvent accessibility, conservation of residues, medium and long-range contacts and surrounding hydrophobicity. Our results showed that the neighboring residues of binding sites in proteins and ligands are different from each other although the interacting pairs of residues have a common behavior. The analysis on surrounding hydrophobicity reveals that the binding residues are less hydrophobic than non-binding sites, which suggests that the hydrophobic core are important for folding and stability whereas the surface seeking residues play a critical role in binding. This tendency has been verified with the number of contacts in binding sites. In addition, the binding site residues are highly conserved compared with non-binding residues. We suggest that the incorporation of sequence and structure-based features may improve the prediction accuracy of binding sites in protein-protein complexes.

Analysis of correlations between energy and residue fluctuations in native proteins and determination of specific sites for binding

The Gaussian network model is used to derive the correlations between energy and residue fluctuations in native proteins. Residues are identified that respond strongly to energy fluctuations and that display correlations with the remaining residues of the protein at the highest modes. We postulate that these residues are located at specific sites for drug binding. We test the validity of this postulate on a data set of 33 structurally distinct proteins in the unbound state. Detailed results are presented for drug binding to the HIV protease.

Prediction of binding sites in receptor-ligand complexes with the Gaussian Network Model

Residues at the binding sites of the ligand and receptor of several enzyme-inhibitor and antibody-antigen complexes are predicted from the slowest (for the ligand) and fastest (for the receptor) modes of motion by the Gaussian Network Model applied to unbound molecules.

Predicting the complex structure and functional motions of the outer membrane transporter and signal transducer FecA

Escherichia coli requires an efficient transport and signaling system to successfully sequester iron from its environment. FecA, a TonB-dependent protein, serves a critical role in this process: first, it binds and transports iron in the form of ferric citrate, and second, it initiates a signaling cascade that results in the transcription of several iron transporter genes in interaction with inner membrane proteins. The structure of the plug and barrel domains and the periplasmic N-terminal domain (NTD) are separately available. However, the linker connecting the plug and barrel and the NTD domains is highly mobile, which may prevent the determination of the FecA structure as a whole assembly. Here, we reduce the conformation space of this linker into most probable structural models using the modeling tool CABS, then apply normal-mode analysis to investigate the motions of the whole structure of FecA by using elastic network models. We relate the FecA domain motions to the outer-inner membrane communication, which initiates transcription. We observe that the global motions of FecA assign flexibility to the TonB box and the NTD, and control the exposure of the TonB box for binding to the TonB inner membrane protein, suggesting how these motions relate to FecA function. Our simulations suggest the presence of a communication between the loops on both ends of the protein, a signaling mechanism by which a signal could be transmitted by conformational transitions in response to the binding of ferric citrate.

Dynamic property is a key determinant for protein-protein interactions

Dynamic property is highly correlated with the biological functions of macromolecules, such as the activity and specificity of enzymes and the allosteric regulation in the signal transduction process. Applications of the dynamic property to protein function researches have been discussed and encouraging progresses have been achieved, for example, in enzyme activity and protein-protein docking studies. However, how the global dynamic property contributes to protein-protein interaction was still unclear. We have studied the dynamic property in protein-protein interactions based on Gaussian Network Model and applied it to classify biological and nonbiological protein-protein complexes in crystal structures. The global motion correlation between residues from the two protomers was found to be remarkably different for biological and nonbiological complexes. This correlation has been used to discriminate biological and nonbiological complexes in crystal and gave a classification rate of 86.9% in the cross-validation test. The innovation of this feature is that it is a global dynamic property which does not rely directly on the interfacial properties of the complex. In addition, the correlation of the global motions was found to be weakly correlated with the dissociation rate constant of protein complexes. We suggest that the dynamic property is a key determinant for protein-protein interaction, which can be used to discriminate native and crystal complexes and potentially be applied in protein-protein dynamic rate constants estimations.