Understanding the determinants of stability and folding of small globular proteins from their energetics

Conformational Dynamics, Energetics, and the Divergent Evolution of Allosteric Regulation: The Case of the Yeast MAPK Family

Allosteric mechanisms provide finely-tuned control over signalling proteins. Proteins of the same family may share high sequence identity and structural similarity but show distinct traits of allosteric control and evolutionary divergent regulation. Revealing the determinants of such properties may be important to understand the molecular bases of different regulatory pathways. Herein, we investigate whether and how evolutionarily-divergent traits of allosteric regulation in homologous proteins can be decoded in terms of internal dynamics and interaction networks that support functionally oriented conformations. In this framework, we start from the comparative analysis of the dynamics and energetics of the yeast MAP Kinases (MAPKs) Fus3 and Kss1 in their native basins. Importantly, distinctive dynamic and energetic stabilization features emerge, which can be related to the two proteins' differential ability to be phosphorylated and engage with the allosteric activator Ste5. We then expanded our study to other evolutionarily-related MAPKs. We show that the dynamical and energetical traits defining the distinct regulatory profiles of Fus3 and Kss1 can be traced along their evolutionary tree. Overall, our approach is able to reconnect (latent) allostery with the principal elements of protein structural stabilization and dynamics, showing how allosteric regulation was encrypted in MAPKs structure well before Ste5 appearance.

Cefoperazone targets D-alanyl-D-alanine carboxypeptidase (DAC) to control Morganella morganii-mediated infection: a subtractive genomic and molecular dynamics approach

Morganella morganii is a Gram-negative bacterial pathogen that causes bacteremia, urinary tract infections, intra-abdominal infections, chorioamnionitis, neonatal sepsis, and newborn meningitis. To control this bacterial pathogen a total of 3565 putative proteins targets in Morganella morganii were screened using comparative subtractive analysis of biochemical pathways annotated by the KEGG that did not share any similarities with human proteins. One of the targets, D-alanyl-D-alanine carboxypeptidase DacB [Morganella] was observed to be implicated in the majority of cell wall synthesis pathways, leading to its selection as a novel pharmacological target. The drug that interacted optimally with the identified target was observed to be Cefoperazone (DB01329) with the estimated free energy of binding -8.9 Kcal/mol. During molecular dynamics simulations; it was observed that DB01328-2exb and DB01329-2exb complexes showed similar values as the control FMX-2exb complex near 0.2 nm with better stability. Furthermore, MMPBSA total free energy calculation showed better binding energy than the control complex for DB01329-2exb interaction i.e. -31.50 (±0.93) kcal/mol. Our presented research suggested that D-alanyl-D-alanine carboxypeptidase DacB could be a therapeutic target and cefoperazone could be a promising ligand to inhibit the D-alanyl-D-alanine carboxypeptidase DacB protein of Morganella morganii. To identify prospective therapeutic and vaccine targets in Morganella morganii, this is the first computational and subtractive genomics investigation of various metabolic pathways exploring other therapeutic targets of Morganella morganii. In vitro/in vivo experimental validation of the identified target D-alanyl-D-alanine carboxypeptidase and the design of its inhibitors is suggested to figure out the best dose, the drug's effectiveness, and its toxicity.Communicated by Ramaswamy H. Sarma.

An ensemble docking-based virtual screening and molecular dynamics simulation of phytochemical compounds from Malaysian Kelulut Honey (KH) against SARS-CoV-2 target enzyme, human angiotensin-converting enzyme 2 (ACE-2)

The human angiotensin-converting enzyme 2 (ACE-2) receptor is a metalloenzyme that plays an important role in regulating blood pressure by modulating angiotensin II. This receptor facilitates SARS-CoV-2 entry into human cells via receptor-mediated endocytosis, causing the global COVID-19 pandemic and a major health crisis. Kelulut honey (KH), one of Malaysian honey recently gained attention for its distinct flavour and taste while having many nutritional and medicinal properties. Recent study demonstrates the antiviral potential of KH against SARS-CoV-2 by inhibiting ACE-2 in vitro, but the bioactive compound pertaining to the ACE-2 inhibition is yet unknown. An ensemble docking-based virtual screening was employed to screen the phytochemical compounds from KH with high binding affinity against the 10 best representative structures of ACE-2 that mostly formed from MD simulation. From 110 phytochemicals previously identified in KH, 27 compounds passed the ADMET analysis and proceeded to docking. Among the docked compound, SDC and FMN consistently exhibited strong binding to ACE-2's active site (-9.719 and -9.473 kcal/mol) and allosteric site (-7.305 and -7.464 kcal/mol) as compared to potent ACE-2 inhibitor, MLN 4760. Detailed trajectory analysis of MD simulation showed stable binding interaction towards active and allosteric sites of ACE-2. KH's compounds show promise in inhibiting SARS-CoV-2 binding to ACE-2 receptors, indicating potential for preventive use or as a supplement to other COVID-19 treatments. Additional research is needed to confirm KH's antiviral effects and its role in SARS-CoV-2 therapy, including prophylaxis and adjuvant treatment with vaccination.Communicated by Ramaswamy H. Sarma.

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

Computational Epitope Prediction and Design for Antibody Development and Detection

The design of optimized protein antigens is a fundamental step in the development of new vaccine candidates and in the detection of therapeutic antibodies. A fundamental prerequisite is the identification of antigenic regions that are most prone to interact with antibodies, namely, B-cell epitopes. Here, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.

Exploration of natural product database for the identification of potent inhibitor against IDH2 mutational variants for glioma therapy

Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting toxicity and modest brain penetrating capability restrict its use as a plausible mIDH2 inhibitor. Furthermore, secondary site mutations (Q316E and I319M) were identified in patients with enasidenib treatments resulting in acquired therapeutic resistance. Hence, in the present investigation, we aimed to identify novel and potent drug-like compounds to overcome the existing drawbacks using an integrated in-silico strategy. A sum of 1574 natural compounds from the naturally occurring plant-based anti-cancerous compound activity target (NPACT) database was proclaimed and subjected to molecular docking. The binding affinities of the resultant natural compounds were rescored using MM-GBSA scoring functions. The resultant lead molecules were subjected to anticancer activity prediction using the machine-learning model. Furthermore, the toxicity and drug-likeliness of the lead compounds were investigated using ADMET properties. Eventually, the integrated in silico approach resulted in a lead molecule, namely squalene (NPACT00954) against mIDH2 protein. The screened compound was subjected to mutational analysis accomplishing second-site mutations. Interestingly, squalene exhibited appreciable binding affinity alongside good brain penetrating potential than enasidenib. Indeed, the reproducibility and significance of our results are examined by running 3 replicas of 100-ns simulations per system using the random initial velocities of the atoms generated by Maxwell distribution at a given temperature. Thus, we hypothesize from our results that further optimization of squalene could be beneficial for the treatment and management of glioma in the near future.

Evaluation of docking procedures reliability in affitins-partners interactions

Affitins constitute a class of small proteins belonging to Sul7d family, which, in microorganisms such as Sulfolobus acidocaldarius, bind DNA preventing its denaturation. Thanks to their stability and small size (60–66 residues in length) they have been considered as ideal candidates for engineering and have been used for more than 10 years now, for different applications. The individuation of a mutant able to recognize a specific target does not imply the knowledge of the binding geometry between the two proteins. However, its identification is of undoubted importance but not always experimentally accessible. For this reason, computational approaches such as protein-protein docking can be helpful for an initial structural characterization of the complex. This method, which produces tens of putative binding geometries ordered according to a binding score, needs to be followed by a further reranking procedure for finding the most plausible one. In the present paper, we use the server ClusPro for generating docking models of affitins with different protein partners whose experimental structures are available in the Protein Data Bank. Then, we apply two protocols for reranking the docking models. The first one investigates their stability by means of Molecular Dynamics simulations; the second one, instead, compares the docking models with the interacting residues predicted by the Matrix of Local Coupling Energies method. Results show that the more efficient way to deal with the reranking problem is to consider the information given by the two protocols together, i.e. employing a consensus approach.

Key interaction patterns in proteins revealed by cluster expansion of the partition function

The native conformation of structured proteins is stabilized by a complex network of interactions. We analyzed the elementary patterns that constitute such network and ranked them according to their importance in shaping protein sequence design. To achieve this goal, we employed a cluster expansion of the partition function in the space of sequences and evaluated numerically the statistical importance of each cluster. An important feature of this procedure is that it is applied to a dense finite system. We found that patterns that contribute most to the partition function are cycles with even numbers of nodes, while cliques are typically detrimental. Each cluster also gives a contribute to the sequence entropy, which is a measure of the evolutionary designability of a fold. We compared the entropies associated with different interaction patterns to their abundances in the native structures of real proteins.

Identifying the Novel Inhibitors Against the Mycolic Acid Biosynthesis Pathway Target "mtFabH" of Mycobacterium tuberculosis

Mycolic acids are the key constituents of mycobacterial cell wall, which protect the bacteria from antibiotic susceptibility, helping to subvert and escape from the host immune system. Thus, the enzymes involved in regulating and biosynthesis of mycolic acids can be explored as potential drug targets to kill Mycobacterium tuberculosis (Mtb). Herein, Kyoto Encyclopedia of Genes and Genomes is used to understand the fatty acid metabolism signaling pathway and integrative computational approach to identify the novel lead molecules against the mtFabH (β-ketoacyl-acyl carrier protein synthase III), the key regulatory enzyme of the mycolic acid pathway. The structure-based virtual screening of antimycobacterial compounds from ChEMBL library against mtFabH results in the selection of 10 lead molecules. Molecular binding and drug-likeness properties of lead molecules compared with mtFabH inhibitor suggest that only two compounds, ChEMBL414848 (C1) and ChEMBL363794 (C2), may be explored as potential lead molecules. However, the spatial stability and binding free energy estimation of thiolactomycin (TLM) and compounds C1 and C2 with mtFabH using molecular dynamics simulation, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) indicate the better activity of C2 (ΔG = -14.18 kcal/mol) as compared with TLM (ΔG = -9.21 kcal/mol) and C1 (ΔG = -13.50 kcal/mol). Thus, compound C1 may be explored as promising drug candidate for the structure-based drug designing of mtFabH inhibitors in the therapy of Mtb.

Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model

We recently proposed a computational procedure to simulate the dissociation of protein/ligand complexes using the dissociation Parallel Cascade Selection Molecular Dynamics simulation (dPaCS-MD) method and to analyze the generated trajectories using the Markov state model (MSM). This procedure, called dPaCS-MD/MSM, enables calculation of the dissociation free energy profile and the standard binding free energy. To examine whether this method can reproduce experimentally determined binding free energies for a variety of systems, we used it to investigate the dissociation of three protein/ligand complexes: trypsin/benzamine, FKBP/FK506, and adenosine A_2A receptor/T4E. First, dPaCS-MD generated multiple dissociation pathways within a reasonable computational time for all the complexes, although the complexes differed significantly in the size of the molecules and in intermolecular interactions. Subsequent MSM analyses produced free energy profiles for the dissociations, which provided insights into how each ligand dissociates from the protein. The standard binding free energies obtained by dPaCS-MD/MSM are in good agreement with experimental values for all the complexes. We conclude that dPaCS-MD/MSM can accurately calculate the binding free energies of these complexes.

Targeting Cytotoxin-Associated Antigen A, a Virulent Factor of Helicobacter pylori-Associated Gastric Cancer: Structure-Based In Silico Screening of Natural Compounds

Gastric cancer is the fifth most frequent cancer and the third major cause of mortality worldwide. Helicobacter pylori, a bacterial infection linked with GC, injects the cytotoxin-associated antigen A (CagA; an oncoprotein) into host cells. When the phosphorylated CagA protein enters the cell, it attaches to other cellular components, interfering with normal cellular signaling pathways. CagA plays an important role in the progression of GC by interacting with phosphatidylserine of the host cell membrane. Therefore, disrupting the CagA-phosphatidylserine connection using small molecules appears to be a promising therapeutic approach. In this report, we screened the natural compounds from ZINC database against the CagA protein using the bioinformatics tools. Hits were initially chosen based on their physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics, as well as other drug-like characteristics. To locate safe and effective hits, the PAINS filter, binding affinities estimation, and interaction analysis were used. Three compounds with high binding affinity and specificity for the CagA binding pocket were discovered. The final hits, ZINC153731, ZINC69482055, and ZINC164387, were found to bind strongly with CagA protein, with binding energies of -11.53, -10.67, and -9.21 kcal/mol, respectively, which were higher than that of the control compound (-7.25 kcal/mol). Further, based on binding affinity and interaction pattern, two leads (ZINC153731, ZINC69482055) were chosen for molecular dynamics (MD) simulation analysis. MD results showed that they displayed stability in their vicinity at 100 ns. This study suggested that these compounds could be used as possible inhibitors of CagA protein in the fight against GC. However, additional benchwork tests are required to validate them as CagA protein inhibitors.

Identification of Potent Natural Resource Small Molecule Inhibitor to Control Vibrio cholera by Targeting Its Outer Membrane Protein U: An In Silico Approach

Vibrio cholerae causes the diarrheal disease cholera which affects millions of people globally. The outer membrane protein U (OmpU) is the outer membrane protein that is most prevalent in V. cholerae and has already been recognized as a critical component of pathogenicity involved in host cell contact and as being necessary for the survival of pathogenic V. cholerae in the host body. Computational approaches were used in this study to screen a total of 37,709 natural compounds from the traditional Chinese medicine (TCM) database against the active site of OmpU. Following a sequential screening of the TCM database, we report three lead compounds-ZINC06494587, ZINC85510056, and ZINC95910434-that bind strongly to OmpU, with binding affinity values of -8.92, -8.12, and -8.78 kcal/mol, which were higher than the control ligand (-7.0 kcal/mol). To optimize the interaction, several 100 ns molecular dynamics simulations were performed, and the resulting complexes were shown to be stable in their vicinity. Additionally, these compounds were predicted to have good drug-like properties based on physicochemical properties and ADMET assessments. This study suggests that further research be conducted on these compounds to determine their potential use as cholera disease treatment.

Unraveling the Allosteric Cross-Talk between the Coactivator Peptide and the Ligand-Binding Site in the Glucocorticoid Receptor

The glucocorticoid receptor (GR) is a nuclear receptor that controls critical biological processes by regulating the transcription of specific genes. There is a known allosteric cross-talk between the ligand and coregulator binding sites within the GR ligand-binding domain that is crucial for the control of the functional response. However, the molecular mechanisms underlying such an allosteric control remain elusive. Here, molecular dynamics (MD) simulations, bioinformatic analysis, and biophysical measurements are integrated to capture the structural and dynamic features of the allosteric cross-talk within the GR. We identified a network of evolutionarily conserved residues that enables the allosteric signal transduction, in agreement with experimental data. MD simulations clarify how such a network is dynamically interconnected and offer a mechanistic explanation of how different peptides affect the intensity of the allosteric signal. This study provides useful insights to elucidate the GR allosteric regulation, ultimately providing a foundation for designing novel drugs.

Evolution of frustrated and stabilising contacts in reconstructed ancient proteins

Energetic properties of a protein are a major determinant of its evolutionary fitness. Using a reconstruction algorithm, dating the reconstructed proteins and calculating the interaction network between their amino acids through a coevolutionary approach, we studied how the interactions that stabilise 890 proteins, belonging to five families, evolved for billions of years. In particular, we focused our attention on the network of most strongly attractive contacts and on that of poorly optimised, frustrated contacts. Our results support the idea that the cluster of most attractive interactions extends its size along evolutionary time, but from the data, we cannot conclude that protein stability or that the degree of frustration tends always to decrease.

Markov state models and NMR uncover an overlooked allosteric loop in p53

The tumor suppressor p53 is the most frequently mutated gene in human cancer, and thus reactivation of mutated p53 is a promising avenue for cancer therapy. Analysis of wildtype p53 and the Y220C cancer mutant long-timescale molecular dynamics simulations with Markov state models and validation by NMR relaxation studies has uncovered the involvement of loop L6 in the slowest motions of the protein. Due to its distant location from the DNA-binding surface, the conformational dynamics of this loop has so far remained largely unexplored. We observe mutation-induced stabilization of alternate L6 conformations, distinct from all experimentally-determined structures, in which the loop is both extended and located further away from the DNA-interacting surface. Additionally, the effect of the L6-adjacent Y220C mutation on the conformational landscape of the functionally-important loop L1 suggests an allosteric role to this dynamic loop and the inactivation mechanism of the mutation. Finally, the simulations reveal a novel Y220C cryptic pocket that can be targeted for p53 rescue efforts. Our approach exemplifies the power of the MSM methodology for uncovering intrinsic dynamic and kinetic differences among distinct protein ensembles, such as for the investigation of mutation effects on protein function.

Wildtype and Y220C L1 and L6 loops conformational landscape, with MSM-identified L6 states highlighted on the right.

The Answer Lies in the Energy: How Simple Atomistic Molecular Dynamics Simulations May Hold the Key to Epitope Prediction on the Fully Glycosylated SARS-CoV-2 Spike Protein

SARS-CoV-2 is a health threat with dire socioeconomical consequences. As the crucial mediator of infection, the viral glycosylated spike protein (S) has attracted the most attention and is at the center of efforts to develop therapeutics and diagnostics. Herein, we use an original decomposition approach to identify energetically uncoupled substructures as antibody binding sites on the fully glycosylated S. Crucially, all that is required are unbiased MD simulations; no prior knowledge of binding properties or ad hoc parameter combinations is needed. Our results are validated by experimentally confirmed structures of S in complex with anti- or nanobodies. We identify poorly coupled subdomains that are poised to host (several) epitopes and potentially involved in large functional conformational transitions. Moreover, we detect two distinct behaviors for glycans: those with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes, and those with weaker coupling could themselves be prone to antibody recognition.

Simple Model of Protein Energetics To Identify Ab Initio Folding Transitions from All-Atom MD Simulations of Proteins

A fundamental requirement to predict the native conformation, address questions of sequence design and optimization, and gain insights into the folding mechanisms of proteins lies in the definition of an unbiased reaction coordinate that reports on the folding state without the need to compare it to reference values, which might be unavailable for new (designed) sequences. Here, we introduce such a reaction coordinate, which does not depend on previous structural knowledge of the native state but relies solely on the energy partition within the protein: the spectral gap of the pair nonbonded energy matrix (ENergy Gap, ENG). This quantity can be simply calculated along unbiased MD trajectories. We show that upon folding the gap increases significantly, while its fluctuations are reduced to a minimum. This is consistently observed for a diverse set of systems and trajectories. Our approach allows one to promptly identify residues that belong to the folding core as well as residues involved in non-native contacts that need to be disrupted to guide polypeptides to the folded state. The energy gap and fluctuations criteria are then used to develop an automatic detection system which allows us to extract and analyze folding transitions from a generic MD trajectory. We speculate that our method can be used to detect conformational ensembles in dynamic and intrinsically disordered proteins, revealing potential preorganization for binding.

Structure based drug designing and discovery of promising lead molecules against UDP-N-acetylenolpyruvoylglucosamine reductase (MurB): A potential drug target in multi-drug resistant Acinetobacter baumannii

According to the world health organization (WHO) reports, Acinetobacter baumannii was considered as one of the significant and first-line priority pathogens, which causes hospital-acquired nosocomial infections in human. The enzymes involved in the peptidoglycan biosynthetic pathway are critical for the survival of this bacterium. Therefore, these enzymes are ideal drug target since they are conserved among most of the species and non-homologous to human. Here, we utilized the structure-based virtual screening (SBVS) technique to identify the promising lead molecules against MurB (UDP-N-acetylenolpyruvoylglucosamine reductase) protein using computational approaches. Initially, the three-dimensional structure of MurB was predicted based on MurB from P. aeruginosa (PDB ID: 4JAY), which is used as a structural template for homology modeling. During the High-throughput Virtual screening (HTVS) analysis, we started with 30,792 molecules against MurB model, among these; only 5238 molecules could be considered suitable for further step. Finally, only twenty molecules were able to pass Lipinski's and ADMET properties. After a thorough examination of interaction analysis, higher ΔG and Ki values, we had chosen five promising molecules (ZINC IDs: ZINC12530134, ZINC15675540, ZINC15675762, ZINC15675624 and ZINC15707270) and three control molecules (PubChem IDs: 54682555, 729933 and 39964628) for Molecular dynamics (MD) simulation to understand the effect of ligands towards the structural stability, structural integrity and structural compactness of MurB protein. Further, the MM/PBSA binding free energy analysis was performed for eight ligands bound MurB structures. Together the results obtained from global dynamics, essential dynamics and MM-PBSA binding free energy analysis, we concluded that apart from the control molecules, ZINC12530134 should be considered as one of the most promising ones and it could be the potent inhibitor against A baumannii and provide valuable insight for further experimental studies.

Mechanistic Model for the Hsp90-Driven Opening of Human Argonaute

The assembly of RNA-induced silencing complex (RISC) is a key process in small RNA-mediated gene silencing. Loading of small RNAs into Argonaute (Ago), the key player protein in the process, has been shown to depend on the Hsp90 chaperone machinery. Experimental single-molecule data indicate that ATP binding to the chaperone facilitates the conformational changes leading to the open state of Ago essential to form a complex with small-RNA duplexes. Yet, no atomic-level description of the dynamic mechanisms and protein-protein interactions underpinning Hsp90-mediated Ago conformational activation is available. Here we investigate the functionally oriented structural and dynamic features of Hsp90-human Ago (hAgo2) complexes in different ligand states by integrating protein-protein docking techniques, all-atom MD simulations, and novel methods of analysis of protein internal dynamics and energetics. On this basis, we develop a structural-dynamic model of the mechanisms underlying the chaperone-assisted human RISC assembly. Our approach unveils the large conformational variability displayed by hAgo2 in the unbound vs the Hsp90-bound states. In this context, several hAgo2 states are found to coexist in isolation, while Hsp90 selects and stabilizes the active form. Hsp90 binding modulates the conformational plasticity of hAgo2 (favoring its opening) by modifying the patterns of hAgo2 intramolecular interactions. Finally, we identify a series of experimentally verifiable key sites that can be mutated to modulate Hsp90-mediated hAgo2 conformational response and ability to bind RNA.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

Functional implications of pH-induced conformational changes in the Sphingosine kinase 1

Sphingosine kinase 1 (SphK1) catalyzes the conversion of sphingosine to sphingosine-1-phosphate that acts as a bioactive signalling molecule, and regulates various cellular processes including lymphocyte trafficking, angiogenesis and response to apoptotic stimuli. Abnormal expression of SphK1 has been observed in a wide range of cancers highlighting their role in tumour growth and metastasis. This enzyme also plays a critical role in metabolic and inflammatory diseases, including pulmonary fibrosis, diabetic neuropathy and Alzheimer's disease. In the present study, we have investigated the structural and conformational changes in SphK1 at varying pH using various spectroscopic techniques. Consistent results were observed with the function of SphK1 at corresponding pH values. SphK1 maintains its secondary and tertiary structure in the pH range of 7.5-10.0. However, protein aggregation was observed in the acidic pH range (4.0-6.5). At pH 2.0, the SphK1 exists in the molten-globule state. Kinase assay also shows that SphK1 activity was optimal in the pH range of 7.5-8.5. To complement in vitro results, we have performed 100 ns molecular dynamics simulation to examine the effect of pH on the structural stability of SphK1 at molecular level. SphK1 maintains its native conformation in the alkaline pH range with some residual fluctuations detected at acidic pH. A considerable correlation was noticed between spectroscopic, enzymatic activity and MD simulation studies. pH dependent structural changes can be further implicated to understand its association with disease condition, and cellular homeostasis with respect to protein function under variable pH conditions.

Identifying novel sphingosine kinase 1 inhibitors as therapeutics against breast cancer

Sphingosine kinase 1 (SphK1) is a promising therapeutic target against several diseases including mammary cancer. The aim of present work is to identify a potent lead compound against breast cancer using ligand-based virtual screening, molecular docking, MD simulations, and the MMPBSA calculations. The LBVS in molecular and virtual libraries yielded 20,800 hits, which were reduced to 621 by several parameters of drug-likeness, lead-likeness, and PAINS. Furthermore, 55 compounds were selected by ADMET descriptors carried forward for molecular interaction studies with SphK1. The binding energy (ΔG) of three screened compounds namely ZINC06823429 (–11.36 kcal/mol), ZINC95421501 (–11.29 kcal/mol), and ZINC95421070 (–11.26 kcal/mol) exhibited stronger than standard drug PF-543 (–9.9 kcal/mol). Finally, it was observed that the ZINC06823429 binds tightly to catalytic site of SphK1 and remain stable during MD simulations. This study provides a significant understanding of SphK1 inhibitors that can be used in the development of potential therapeutics against breast cancer.

Impact of Mutations on NPAC Structural Dynamics: Mechanistic Insights from MD Simulations

NPAC is a cytokine-like nuclear factor involved in chromatin modification and regulation of gene expression. In humans, the C-terminal domain of NPAC has the conserved structure of the β-hydroxyacid dehydrogenases (β-HAD) protein superfamily, which forms a stable tetrameric core scaffold for demethylase enzymes and organizes multiple sites for chromatin interactions. In spite of the close structural resemblance to other β-HAD family members, the human NPAC dehydrogenase domain lacks a highly conserved catalytic lysine, substituted by a methionine. The reintroduction of the catalytic lysine by M437 K mutation results in a significant decrease of stability of the tetramer. Here, we have computationally investigated the molecular determinants of the functional differences between methionine and lysine-containing NPAC proteins. We find that the single mutation can determine strong consequences in terms of dynamics, stability, and ultimately ability to assemble in supramolecular complexes: the higher stability and lower flexibility of the methionine variant structurally preorganizes the monomer for tetramerization, whereas lysine increases flexibility and favors conformations that, while catalytically active, are not optimal for tetrameric assembly. We combine structure-dynamics analysis to an evolutionary study of NPAC sequences, showing that the methionine mutation occurs in a specifically flexible region of the lysine-containing protein, flanked by two domains that concentrate most of the stabilizing interactions. In our model, such separation of stability nuclei and flexible regions appears to favor the functional innovability of the protein.

Peptides for Infectious Diseases: From Probe Design to Diagnostic Microarrays

Peptides and peptidomimetics have attracted revived interest regarding their applications in chemical biology over the last few years. Their chemical versatility, synthetic accessibility and the ease of storage and management compared to full proteins have made peptides particularly interesting in diagnostic applications, where they proved to efficiently recapitulate the molecular recognition properties of larger protein antigens, and were proven to be able to capture antibodies circulating in the plasma and serum of patients previously exposed to bacterial or viral infections. Here, we describe the development, integration and application of strategies for computational prediction and design, advanced chemical synthesis, and diagnostic deployment in multiplexed assays of peptide-based materials which are able to bind antibodies of diagnostic as well as therapeutic interest. By presenting successful applications of such an integrated strategy, we argue that they will have an ever-increasing role in both basic and clinical realms of research, where important advances can be expected in the next few years.

Identification and Evaluation of Inhibitors of Lipase from Malassezia restricta using Virtual High-Throughput Screening and Molecular Dynamics Studies

Recent studies revealed the role of lipase in the pathogenicity of Malassezia restricta in dandruff and seborrheic dermatitis (D/SD). The lipase from M. restricta (Mrlip1) is considered a potential target for dandruff therapy. In this work, we performed structure-based virtual screening in Zinc database to find the natural bioactive inhibitors of Mrlip1. We identified three compounds bearing superior affinity and specificity from the Traditional Chinese Medicine database (~60,000 compounds), and their binding patterns with Mrlip1 were analyzed in detail. Additionally, we performed three sets of 100 ns MD simulations of each complex in order to understand the interaction mechanism of Mrlip1 with known inhibitor RHC80267 and the newly identified compounds such as ZINC85530919, ZINC95914464 and ZINC85530320, respectively. These compounds bind to the active site cavity and cause conformational changes in Mrlip1. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) studies suggested that the average binding energy was stronger in the case of Mrlip1-ZINC85530919 and Mrlip1-ZINC95914464. The selected natural inhibitors might act as promising lead drugs against Mrlip1. Further, the present study will contribute to various steps involved in developing and creating potent drugs for several skin diseases including dandruff.

Binding mechanism of caffeic acid and simvastatin to the integrin linked kinase for therapeutic implications: a comparative docking and MD simulation studies

Integrin linked kinase (ILK) is a Ser/Thr kinase, which regulates various integrin mediated signaling pathways, and is involved in cell adhesion, migration and differentiation. Alteration in the ILK is responsible for abnormal functioning of the cell system, which may lead to the cancer progression and metastasis. Caffeic acid (CA) and simvastatin are used as antioxidant and possess anticancer properties. Thus, inhibiting the kinase activity of ILK by CA and simvastatin may be implicated in the cancer therapy. In this study, we have performed molecular docking followed by 100 ns MD simulations to understand the interaction mechanism of ILK protein with the CA and simvastatin. Average potential energy was found to be highest in case of ILK-CA complex (-770,949 kJ/mol). Binding free energy was found to be higher in case of simvastatin than CA. Our results indicate that simvastatin binds more effectively to the active pocket of ILK. We further performed MTT assay to understand its anticancer potential. Simvastatin shows the IC₅₀ values for HepG2 and MCF-7 as 19.18 ± 0.12 and 13.84 ± 0.22 µM, respectively. However, the IC₅₀ value of CA on HepG2 and MCF-7 was reported as 175.50 ± 1.44 and 144.90 ± 1.53 µM, respectively. Our study provides a deeper insight into the binding mechanism of simvastatin and CA to ILK, which further opens a promising channel for their implications in cancer therapy.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.

How the Ligand-Induced Reorganization of Protein Internal Energies Is Coupled to Conformational Events

Here, we introduce a novel computational method to identify the protein substructures most likely to support the functionally oriented structural deformations that occur upon ligand-binding. To this aim, we study the modulation of protein energetics along the trajectory of a molecular dynamics simulation of different proteins in the presence and in the absence of their respective ligands, namely, human FGF, human second PDZ from human PTP1E/PTPL1, and the N terminal domain of human Hsp90. The method is based on the idea that a subset of protein residues (hotspots) may initiate the global response via the disassembly and reassembly of interactions, which is reflected in the modulation of the overall protein energetics. To identify structural hotspots and dynamic states linked to the onset of functionally relevant conformational transitions, we define an energy profile to monitor the protein energetics, based on a previously introduced approach that highlights the essential nonbonded couplings among all residues. The energy profiles are calculated along the trajectory to yield a time-dependent evolution, and their relative population in the presence and absence of the ligand is evaluated by means of a clustering procedure. It is found that interconversion between clusters, as well as their population and the density of specific energy profiles in the vicinity of structural transitions, provides specific information on the impact of the ligand in driving the protein conformational response. This analysis also highlights the hotspot residues that are most responsive to the presence of the ligand. Importantly, identified hotspots are in agreement with experimental evidence in the three considered systems. We propose that this approach can be generally used in the prediction of "allosteric hotspots" and ligand-induced conformational responses, as well as to select conformations more likely to support functional transitions (e.g., in the framework of adaptive sampling approaches).

Elucidation of interaction mechanism of ellagic acid to the integrin linked kinase

Integrin-linked kinase (ILK) is a member of Ser/Thr kinase which interacts to the cytoplasmic domain of β-integrins, and thereby induces apoptosis. ILK is considered as potential drug target because it's direct involvement in the tumor progression. Here, we have performed molecular docking followed by 100 ns MD simulation to understand the mechanism of interaction of ILK with the ellagic acid (EA). EA is well known for its antiproliferative and antioxidant properties in cancer cell lines and animal models. We have observed that EA binds to the active site cavity of ILK and causes conformational changes in the ILK structure. The orientation of EA in the active pocket of ILK showed to have least RMSD values and stable. The average binding energy ILK-EA complex calculated during MMPBSA was -191.267 kJ/mol, indicating a relatively strong binding affinity. The actual binding affinity of EA to ILK was measured by fluorescence spectroscopy and K_b and n values were 9.28 μM and 1.9264 (~2), respectively. The IC₅₀ values for EA were 26.22 ± 0.12 μM for MCF-7 and 38.45 ± 2.42 μM for HepG2 cells, estimated by MTT assay. Our findings are helpful to design EA-based novel inhibitors of ILK which have potential to attenuate tumor progression.

Targeting Difficult Protein-Protein Interactions with Plain and General Computational Approaches

Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology.

Identification and evaluation of bioactive natural products as potential inhibitors of human microtubule affinity-regulating kinase 4 (MARK4)

Microtubule affinity-regulating kinase 4 (MARK4) has recently been identified as a potential drug target for several complex diseases including cancer, diabetes and neurodegenerative disorders. Inhibition of MARK4 activity is an appealing therapeutic option to treat such diseases. Here, we have performed structure-based virtual high-throughput screening of 100,000 naturally occurring compounds from ZINC database against MARK4 to find its potential inhibitors. The resulted hits were selected, based on the binding affinities, docking scores and selectivity. Further, binding energy calculation, Lipinski filtration and ADMET prediction were carried out to find safe and better hits against MARK4. Best 10 compounds bearing high specificity and binding efficiency were selected, and their binding pattern to MARK4 was analyzed in detail. Finally, 100 ns molecular dynamics simulation was performed to evaluate; the dynamics stability of MARK4-compound complex. In conclusion, these selected natural compounds from ZINC database might be potential leads against MARK4, and can further be exploited in drug design and development for associated diseases.

Investigation of molecular mechanism of recognition between citral and MARK4: A newer therapeutic approach to attenuate cancer cell progression

Microtubule affinity regulating kinase 4 (MARK4) is a member of AMP-activated protein kinase, found to be involved in apoptosis, inflammation and many other regulatory pathways. Since, its aberrant expression is directly associated with the cell cycle and thus cancer. Therefore, MARK4 is being considered as a potential drug target for cancer therapy. Here, we investigated the mechanism of inhibition of MARK4 activity by citral. Docking studies suggested that citral effectively binds to the active site cavity, and complex is stabilized by several interactions. We further performed molecular dynamics simulation of MARK4-citral complex under explicit water condition for 100ns and observed that binding of citral to MARK4 was quite stable. Fluorescence binding studies suggested that citral strongly binds to MARK4 and thereby inhibits its enzyme activity which was measured by the kinase inhibition assay. We further performed MTT assay and observed that citral inhibits proliferation of breast cancer cell line MCF-7. This work provides a newer insight into the use of citral as novel cancer therapeutics through the MARK4 inhibition. Results may be employed to design novel therapeutic molecule using citral as a scaffold for MARK4 inhibition to fight related diseases.

Structural modeling of the AhR:ARNT complex in the bHLH-PASA-PASB region elucidates the key determinants of dimerization

Elucidation of the dimerization process of the aryl hydrocarbon receptor (AhR) with the AhR nuclear translocator (ARNT) is crucial for understanding the mechanisms underlying the functional activity of AhR, including mediation of the toxicity of environmental contaminants. In this work, for the first time a structural model of the AhR:ARNT dimer encompassing the entire bHLH-PASA-PASB domain region is proposed. It is developed by using a template-based modeling approach, relying on the recently available crystallographic structures of two dimers of homologous systems in the bHLH-PAS family of proteins: the CLOCK:BMAL1 and the HIF2α:ARNT heterodimers. The structural and energetic characteristics of the modeled AhR:ARNT protein-protein interface are determined by evaluating the variations in solvent accessible surface area, the total binding free energy and the per-residue free energy contributions obtained by the MM-GBSA method and the Energy Decomposition Analysis. The analyses of the intricate network of inter-domain interactions at the dimerization interfaces provide insights into the key determinants of dimerization. These are confirmed by comparison of the computational findings with the available experimental mutagenesis and functional analysis data. The results presented here on the AhR:ARNT dimer structure and interactions provide a framework to start analyzing the mechanism of AhR transformation into its functional DNA binding form.

Large-scale molecular dynamics simulation: Effect of polarization on thrombin-ligand binding energy

Molecular dynamics (MD) simulations lasting 500 ns were performed in explicit water to investigate the effect of polarization on the binding of ligands to human α-thrombin based on the standard nonpolarizable AMBER force field and the quantum-derived polarized protein-specific charge (PPC). The PPC includes the electronic polarization effect of the thrombin-ligand complex, which is absent in the standard force field. A detailed analysis and comparison of the results of the MD simulation with experimental data provided strong evidence that intra-protein, protein-ligand hydrogen bonds and the root-mean-square deviation of backbone atoms were significantly stabilized through electronic polarization. Specifically, two critical hydrogen bonds between thrombin and the ligand were broken at approximately 190 ns when AMBER force field was used and the number of intra-protein backbone hydrogen bonds was higher under PPC than under AMBER. The thrombin-ligand binding energy was computed using the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method, and the results were consistent with the experimental value obtained using PPC. Because hydrogen bonds were unstable, it was failed to predict the binding affinity under the AMBER force field. Furthermore, the results of the present study revealed that differences in the binding free energy between AMBER and PPC almost comes from the electrostatic interaction. Thus, this study provides evidence that protein polarization is critical to accurately describe protein-ligand binding.

Structure prediction and functional analyses of a thermostable lipase obtained from Shewanella putrefaciens

Previous experimental studies on thermostable lipase from Shewanella putrefaciens suggested the maximum activity at higher temperatures, but with little information on its conformational profile. In this study, the three-dimensional structure of lipase was predicted and a 60 ns molecular dynamics (MD) simulation was carried out at temperatures ranging from 300 to 400 K to better understand its thermostable nature at the molecular level. MD simulations were performed in order to predict the optimal activity of thermostable lipase. The results suggested strong conformational temperature dependence. The thermostable lipase maintained its bio-active conformation at 350 K during the 60 ns MD simulations.

Deciphering Dimerization Modes of PAS Domains: Computational and Experimental Analyses of the AhR:ARNT Complex Reveal New Insights Into the Mechanisms of AhR Transformation

The Aryl hydrocarbon Receptor (AhR) is a transcription factor that mediates the biochemical response to xenobiotics and the toxic effects of a number of environmental contaminants, including dioxins. Recently, endogenous regulatory roles for the AhR in normal physiology and development have also been reported, thus extending the interest in understanding its molecular mechanisms of activation. Since dimerization with the AhR Nuclear Translocator (ARNT) protein, occurring through the Helix-Loop-Helix (HLH) and PER-ARNT-SIM (PAS) domains, is needed to convert the AhR into its transcriptionally active form, deciphering the AhR:ARNT dimerization mode would provide insights into the mechanisms of AhR transformation. Here we present homology models of the murine AhR:ARNT PAS domain dimer developed using recently available X-ray structures of other bHLH-PAS protein dimers. Due to the different reciprocal orientation and interaction surfaces in the different template dimers, two alternative models were developed for both the PAS-A and PAS-B dimers and they were characterized by combining a number of computational evaluations. Both well-established hot spot prediction methods and new approaches to analyze individual residue and residue-pairwise contributions to the MM-GBSA binding free energies were adopted to predict residues critical for dimer stabilization. On this basis, a mutagenesis strategy for both the murine AhR and ARNT proteins was designed and ligand-dependent DNA binding ability of the AhR:ARNT heterodimer mutants was evaluated. While functional analysis disfavored the HIF2α:ARNT heterodimer-based PAS-B model, most mutants derived from the CLOCK:BMAL1-based AhR:ARNT dimer models of both the PAS-A and the PAS-B dramatically decreased the levels of DNA binding, suggesting this latter model as the most suitable for describing AhR:ARNT dimerization. These novel results open new research directions focused at elucidating basic molecular mechanisms underlying the functional activity of the AhR.

Computational modeling combined with experimental validation may give insight into structural and functional properties of protein systems. The basic Helix-Loop-Helix PER-ARNT-SIM (bHLH-PAS) proteins show conserved functional domains despite the broad range of functions exerted by the different systems. Within this protein family, the Aryl hydrocarbon Receptor (AhR) is known to mediate the toxic effects of a number of environmental contaminants, including dioxins and dioxin-like chemicals, and it also exerts other biochemical and physiological effects. Despite the absence of experimentally determined structures, theoretical models of the AhR PAS domains developed on the basis of homologous systems have allowed understanding of some aspects of the molecular mechanisms underlying its function. In this work we present alternative structural models of the transcriptionally active complex of AhR with the AhR Nuclear Translocator (ARNT) protein. Computational characterization of the modeled protein-protein interaction interfaces guided the design of mutagenesis experiments, and evaluation of the DNA binding ability of the resulting AhR:ARNT dimer mutants allowed validation of the models and selection of the most reliable one. These findings open new research directions for understanding the molecular mechanisms underlying the functional activity of the AhR.

DNA Polymerase Conformational Dynamics and the Role of Fidelity-Conferring Residues: Insights from Computational Simulations

Herein we investigate the molecular bases of DNA polymerase I conformational dynamics that underlie the replication fidelity of the enzyme. Such fidelity is determined by conformational changes that promote the rejection of incorrect nucleotides before the chemical ligation step. We report a comprehensive atomic resolution study of wild type and mutant enzymes in different bound states and starting from different crystal structures, using extensive molecular dynamics (MD) simulations that cover a total timespan of ~5 ms. The resulting trajectories are examined via a combination of novel methods of internal dynamics and energetics analysis, aimed to reveal the principal molecular determinants for the (de)stabilization of a certain conformational state. Our results show that the presence of fidelity-decreasing mutations or the binding of incorrect nucleotides in ternary complexes tend to favor transitions from closed toward open structures, passing through an ensemble of semi-closed intermediates. The latter ensemble includes the experimentally observed ajar conformation which, consistent with previous experimental observations, emerges as a molecular checkpoint for the selection of the correct nucleotide to incorporate. We discuss the implications of our results for the understanding of the relationships between the structure, dynamics, and function of DNA polymerase I at the atomistic level.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

Mechanisms of Differential Allosteric Modulation in Homologous Proteins: Insights from the Analysis of Internal Dynamics and Energetics of PDZ Domains

Allostery is a general phenomenon in proteins whereby a perturbation at one site reverberates into a functional change at another one, through modulation of its conformational dynamics. Herein, we address the problem of how the molecular signal encoded by a ligand is differentially transmitted through the structures of two homologous PDZ proteins: PDZ2, which responds to binding with structural and dynamical changes in regions distal from the ligand site, and PDZ3, which is characterized by less-intense dynamical variations. We use novel methods of analysis of MD simulations in the unbound and bound states to investigate the determinants of the differential allosteric behavior of the two proteins. The analysis of the correlations between the redistribution of stabilization energy and local fluctuation patterns highlights the nucleus of residues responsible for the stabilization of the 3D fold, the stability core, as the substructure that defines the difference in the allosteric response: in PDZ2, it undergoes a consistent dynamic and energetic reorganization, whereas in PDZ3, it remains largely unperturbed. Specifically, we observe for PDZ2 a significant anticorrelation between the motions of distal loops and residues of the stability core and differences in the correlation patterns between the bound and unbound states. Such variation is not observed in PDZ3, indicating that its energetics and internal dynamics are less affected by the presence/absence of the ligand. Finally, we propose a model with a direct link between the modulation of the structural, energetic and dynamic properties of a protein, and its allosteric response to a perturbation.

Sequence- and Structure-Based Immunoreactive Epitope Discovery for Burkholderia pseudomallei Flagellin

Burkholderia pseudomallei is a Gram-negative bacterium responsible for melioidosis, a serious and often fatal infectious disease that is poorly controlled by existing treatments. Due to its inherent resistance to the major antibiotic classes and its facultative intracellular pathogenicity, an effective vaccine would be extremely desirable, along with appropriate prevention and therapeutic management. One of the main subunit vaccine candidates is flagellin of Burkholderia pseudomallei (FliC_Bp). Here, we present the high resolution crystal structure of FliC_Bp and report the synthesis and characterization of three peptides predicted to be both B and T cell FliC_Bp epitopes, by both structure-based in silico methods, and sequence-based epitope prediction tools. All three epitopes were shown to be immunoreactive against human IgG antibodies and to elicit cytokine production from human peripheral blood mononuclear cells. Furthermore, two of the peptides (F51-69 and F270-288) were found to be dominant immunoreactive epitopes, and their antibodies enhanced the bactericidal activities of purified human neutrophils. The epitopes derived from this study may represent potential melioidosis vaccine components.

Melioidosis is an infectious disease caused by Burkolderia pseudomallei that poses a major public health problem in Southeast Asia and northern Australia. This bacterium is difficult to treat due to its intrinsic resistance to antibiotics, poor diagnosis, and the lack of a licensed vaccine. Vaccine safety is a prime concern, therefore recombinant protein subunit and/or peptide vaccine components, may represent safer alternatives. In this context, we targeted one of the main subunit vaccine candidates tested to date, flagellin from B. pseudomallei (FliC_Bp) that comprises the flagellar filament that mediates bacterial motility. Based on the knowledge that activation of both cell-mediated and antibody-mediated responses must be addressed in a melioidosis vaccine, we identified B and T cell immunoreactive peptides from FliC_Bp, using both sequence-based and structure-based computational prediction programs, for further in vitro immunological testing. Our data confirm the accuracy of sequence-based epitope prediction tools, and two structure-based methods applied to the FliC_Bp crystal structure (here-described), in predicting both T- and B-cell epitopes. Moreover, we identified two epitope peptides with significant joint T-cell and B-cell activities for further development as melioidosis vaccine components.

Structural Stability and Flexibility Direct the Selection of Activating Mutations in Epidermal Growth Factor Receptor Kinase

Herein we investigate the potential of novel methods of molecular dynamics analysis to provide information on the key factors that underlie the preferential localization and the effects of mutations modulating protein activities. Epidermal growth factor receptor (EGFR) kinases are selected as a test case. The combined analysis of protein energetics and internal dynamics indicates a clear polarization in the native protein, whereby a highly stable and ordered scaffold in one domain, namely the C-lobe, is combined to a flexible and loosely stabilized domain, the N-lobe. The subdivision in two portions with different properties directs the presence of point mutations mainly to the N-lobe. This allows modulating protein flexibility so that the protein can more efficiently sample the conformations necessary for substrate recognition, while leaving the stability of the protein unperturbed. In this context, comparative simulations of EGFR in the wild type sequence and in the presence of the activating oncogenic mutation G719S reveal flexibility changes in several key regions, involving in particular the part of the kinase devoted to the regulation of substrate recognition (regulatory core) and an increase in the number of stabilizing interactions in the N-lobe for the activated mutant. Our approaches represent a promising and simple strategy toward rationalizing the effects of mutations in modulating enzymatic activities.

Prediction of Antigenic B and T Cell Epitopes via Energy Decomposition Analysis: Description of the Web-Based Prediction Tool BEPPE

Unraveling the molecular basis of immune recognition still represents a challenging task for current biological sciences, both in terms of theoretical knowledge and practical implications. Here, we describe the physical-chemistry methods and computational protocols for the prediction of antibody-binding epitopes and MHC-II loaded epitopes, starting from the atomic coordinates of antigenic proteins (PDB file). These concepts are the base of the Web tool BEPPE (Binding Epitope Prediction from Protein Energetics), a free service that returns a list of putative epitope sequences and related blast searches against the Uniprot human complete proteome. BEPPE can be employed for the study of the biophysical processes at the basis of the immune recognition, as well as for immunological purposes such as the rational design of biomarkers and targets for diagnostics, therapeutics, and vaccine discovery.

Energetic and dynamic aspects of the affinity maturation process: characterizing improved variants from the bevacizumab antibody with molecular simulations

Antibody affinity maturation is one of the fundamental processes of immune defense against invading pathogens. From the biological point of view, the clonal selection hypothesis represents the most accepted mechanism to explain how mutations increasing the affinity for target antigens are introduced and selected in antibody molecules. However, understanding at the molecular level how protein modifications, such as point mutation, can modify and modulate the affinity of an antibody for its antigen is still a major open issue in molecular biology. In this paper, we address various aspects of this problem by analyzing and comparing atomistic simulations of 17 variants of the bevacizumab antibody, all directed against the common target protein VEGF-A. In particular, we examine MD-based descriptors of the internal energetics and dynamics of mutated antibodies and their possible correlations with experimentally determined affinities for the antigens. Our results show that affinity improvement is correlated with a variation of the internal stabilization energy of the antibody molecule when bound to the antigen, compensated by the variation in the interaction energy between the antigen and the antibody, paralleled by an overall modulation of internal coordination within the antibody molecular structure. A possible model of the mechanism of rigidification and of the main residues involved is proposed. Overall, our results can help in understanding the molecular determinants of antigen recognition and have implications in the rational design of new antibodies with optimized affinities.

A Hamiltonian replica exchange molecular dynamics (MD) method for the study of folding, based on the analysis of the stabilization determinants of proteins

Herein, we present a novel Hamiltonian replica exchange protocol for classical molecular dynamics simulations of protein folding/unfolding. The scheme starts from the analysis of the energy-networks responsible for the stabilization of the folded conformation, by means of the energy-decomposition approach. In this framework, the compact energetic map of the native state is generated by a preliminary short molecular dynamics (MD) simulation of the protein in explicit solvent. This map is simplified by means of an eigenvalue decomposition. The highest components of the eigenvector associated with the lowest eigenvalue indicate which sites, named "hot spots", are likely to be responsible for the stability and correct folding of the protein. In the Hamiltonian replica exchange protocol, we use modified force-field parameters to treat the interparticle non-bonded potentials of the hot spots within the protein and between protein and solvent atoms, leaving unperturbed those relative to all other residues, as well as solvent-solvent interactions. We show that it is possible to reversibly simulate the folding/unfolding behavior of two test proteins, namely Villin HeadPiece HP35 (35 residues) and Protein A (62 residues), using a limited number of replicas. We next discuss possible implications for the study of folding mechanisms via all atom simulations.

Rational epitope design for protein targeting

We present a new multidisciplinary strategy integrating computational biology with high-throughput microarray analysis aimed to translate molecular understanding of protein-antibody recognition into the design of efficient and selective protein-based analytical and diagnostic tools. The structures of two proteins with different folds and secondary structure contents, namely, the beta-barrel FABP and the α-helical S100B, were used as the basis for the prediction and design of potential antibody-binding epitopes using the recently developed MLCE computational method. Starting from the idea that the structure, dynamics, and stability of a protein-antigen play a key role in the interaction with antibodies, MLCE integrates the analysis of the dynamical and energetic properties of proteins to identify nonoptimized, low-intensity energetic interaction-networks on the surface of the isolated antigens, which correspond to substructures that can aptly be recognized by a binding partner. The identified epitopes were next synthesized as free peptides and used to elicit specific antibodies in rabbits. Importantly, the resulting antibodies were proven to specifically and selectively recognize the original, full-length proteins in microarray-based tests. Competition experiments further demonstrated the specificity of the molecular recognition between the target immobilized proteins and the generated antibodies. Our integrated computational and microarray-based results demonstrate the possibility to rationally discover and design synthetic epitopes able to elicit antibodies specific for full-length proteins starting only from three-dimensional structural information on the target. We discuss implications for diagnosis and vaccine development purposes.

Investigating allostery in molecular recognition: insights from a computational study of multiple antibody-antigen complexes

Antibody-antigen recognition plays a key role in the immune response against pathogens. Here, we have investigated various aspects of this problem by analyzing a large and diverse set of antibodies and their respective complexes with protein antigens through atomistic simulations. Common features of antibody response to the presence of antigens are elucidated by the analysis of the proteins' internal dynamics and coordination in different ligand states, combined with the analysis of the interaction networks implicated in the stabilization of functional structures. The use of a common structural reference reveals preferential changes in the dynamic coordination and intramolecular interaction networks induced by antigen binding and shared by all antibodies. Such changes propagate from the binding region through the whole immunoglobulin domains. Overall, complexed antibodies show more diffuse networks of nonbonded interactions and a general higher internal dynamic coordination, which preferentially involve the immunoglobulin (Ig) domains of the heavy chain. The combined results provide atomistic insights into the correlations between the modulation of conformational dynamics, structural stability, and allosteric signal transduction. In particular, the results suggest that specific networks of residues, shared among all the analyzed proteins, define the molecular pathways by which antibody structures respond to antigen binding. Our studies may have implications in practical use, such as the rational design of antibodies with specifically modulated antigen-binding affinities.

A structure-based strategy for epitope discovery in Burkholderia pseudomallei OppA antigen

We present an approach integrating structural and computational biology with immunological tests to identify epitopes in the OppA antigen from the Gram-negative pathogen Burkholderia pseudomallei, the etiological agent of melioidosis. The crystal structure of OppA(Bp), reported here at 2.1 Å resolution, was the basis for a computational analysis that identified three potential epitopes. In parallel, antigen proteolysis and immunocapturing allowed us to identify three additional peptides. All six potential epitopes were synthesized as free peptides and tested for their immunoreactivity against sera from healthy seronegative, healthy seropositive, and recovered melioidosis patients. Three synthetic peptides allowed the different patient groups to be distinguished, underlining the potential of this approach. Extension of the computational analysis, including energy-based decomposition methods, allowed rationalizing results of the predictive analyses and the immunocapture epitope mapping. Our results illustrate a structure-based epitope discovery process, whose application may expand our perspectives in the diagnostic and vaccine design fields.