Gaussian-weighted RMSD superposition of proteins: a structural comparison for flexible proteins and predicted protein structures

Deciphering the role of extracellular polymeric substances in the adsorption and biotransformation of organic micropollutants during anaerobic wastewater treatment

Extracellular polymeric substances (EPS) participate in the removal of organic micropollutants (OMPs), but the primary pathways of removal and detailed mechanisms remain elusive. We evaluated the effect of EPS on removal for 16 distinct chemical classes of OMPs during anaerobic digestion (AD). The results showed that hydrophobic OMPs (HBOMPs) could not be removed by EPS, while hydrophilic OMPs (HLOMPs) were amenable to removal via adsorption and biotransformation of EPS. The adsorption and biotransformation of HLOMPs by EPS accounted up to 19.4 ± 0.9 % and 6.0 ± 0.8 % of total removal, respectively. Further investigations into the adsorption and biotransformation mechanisms of HLOMPs by EPS were conducted utilizing spectral, molecular dynamics simulation, and electrochemical analysis. The results suggested that EPS provided abundant binding sites for the adsorption of HLOMPs. The binding of HLOMPs to tryptophan-like proteins in EPS formed nonfluorescent complexes. Hydrogen bonds, hydrophobic interactions and water bridges were key to the binding processes and helped stabilize the complexes. The biotransformation of HLOMPs by EPS may be attributed to the presence of extracellular redox active components (c-type cytochromes (c-Cyts), c-Cyts-bound flavins). This study enhanced the comprehension for the role of EPS on the OMPs removal in anaerobic wastewater treatment.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

Novel thiazolidin-4-one benzenesulfonamide hybrids as PPARγ agonists: Design, synthesis and in vivo anti-diabetic evaluation

In the current study, two series of novel thiazolidin-4-one benzenesulfonamide arylidene hybrids 9a-l and 10a-f were designed, synthesized and tested in vitro for their PPARɣ agonistic activity. The phenethyl thiazolidin-4-one sulphonamide 9l showed the highest PPARɣ activation % by 41.7%. Whereas, the 3-methoxy- and 4-methyl-4-benzyloxy thiazolidin-4-one sulphonamides 9i, and 9k revealed moderate PPARɣ activation % of 31.7, and 32.8%, respectively, in addition, the 3-methoxy-3-benzyloxy thiazolidin-4-one sulphonamide 10d showed PPARɣ activation % of 33.7% compared to pioglitazone. Compounds 9b, 9i, 9k, 9l, and 10d revealed higher selectivity to PPARɣ over the PPARδ, and PPARα isoforms. An immunohistochemical study was performed in HepG-2 cells to confirm the PPARɣ protein expression for the most active compounds. Compounds 9i, 9k, and 10d showed higher PPARɣ expression than that of pioglitazone. Pharmacological studies were also performed to determine the anti-diabetic activity in rats at a dose of 36 mg/kg, and it was revealed that compounds 9i and 10d improved insulin secretion as well as anti-diabetic effects. The 3-methoxy-4-benzyloxy thiazolidin-4-one sulphonamide 9i showed a better anti-diabetic activity than pioglitazone. Moreover, it showed a rise in blood insulin by 4-folds and C-peptide levels by 48.8%, as well as improved insulin sensitivity. Moreover, compound 9i improved diabetic complications as evidenced by decreasing liver serum enzymes, restoration of total protein and kidney functions. Besides, it combated oxidative stress status and exerted anti-hyperlipidemic effect. Compound 9i showed a superior activity by normalizing some parameters and amelioration of pancreatic, hepatic, and renal histopathological alterations caused by STZ-induction of diabetes. Molecular docking studies, molecular dynamic simulations, and protein ligand interaction analysis were also performed for the newly synthesized compounds to investigate their predicted binding pattern and energies in PPARɣ binding site.

Distinct dynamical features of plasmodial and human HSP70-HSP110 highlight the divergence in their chaperone-assisted protein folding

HSP70 and its evolutionarily diverged co-chaperone HSP110, forms an important node in protein folding cascade. How these proteins maintain the aggregation-prone proteome of malaria parasite in functional state remains underexplored, in contrast to its human orthologs. In this study, we have probed into conformational dynamics of plasmodial HSP70 and HSP110 through multiple μs MD-simulations (ATP-state) and compared with their respective human counterparts. Simulations covered sampling of 3.4 and 2.8 μs for HSP70 and HSP110, respectively, for parasite and human orthologs. We provide a comprehensive description of the dynamic behaviors that characterize the systems and also introduce a parameter for quantifying protein rigidity. For HSP70, the interspecies comparison reveals enhanced flexibility in IA and IB subdomain within the conserved NBD, lesser solvent accessibility of the interdomain linker and distinct dynamics of the SBDβ of Pf HSP70 in comparison to Hs HSP70. In the case of HSP110, notable contrast in the dynamics of NBD, SBDβ and SBDα was observed between parasite and human ortholog. Although HSP70 and HSP110 are members of the same superfamily, we identified specific differences in the subdomain contacts in NBD, linker properties and interdomain movements in their human and parasite orthologs. Our study suggests that differences in conformational dynamics may translate into species-specific differences in the chaperoning activities of HSP70-HSP110 in the parasite and human, respectively. Dynamical features of Pf HSP70-HSP110 may contribute to the maintenance of proteostasis in the parasite during its intracellular survival in the host.

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

Enzyme catalysis is governed by a limited toolkit of residues and organic or inorganic co-factors. Therefore, it is expected that recurring residue arrangements will be found across the enzyme space, which perform a defined catalytic function, are structurally similar and occur in unrelated enzymes. Leveraging the integrated information in the Mechanism and Catalytic Site Atlas (M-CSA) (enzyme structure, sequence, catalytic residue annotations, catalysed reaction, detailed mechanism description), 3D templates were derived to represent compact groups of catalytic residues. A fuzzy template-template search, allowed us to identify those recurring motifs, which are conserved or convergent, that we define as the "modules of enzyme catalysis". We show that a large fraction of these modules facilitate binding of metal ions, co-factors and substrates, and are frequently the result of convergent evolution. A smaller number of convergent modules perform a well-defined catalytic role, such as the variants of the catalytic triad (i.e. Ser-His-Asp/Cys-His-Asp) and the saccharide-cleaving Asp/Glu triad. It is also shown that enzymes whose functions have diverged during evolution preserve regions of their active site unaltered, as shown by modules performing similar or identical steps of the catalytic mechanism. We have compiled a comprehensive library of catalytic modules, that characterise a broad spectrum of enzymes. These modules can be used as templates in enzyme design and for better understanding catalysis in 3D.

Immunoinformatics and MD-simulation data suggest that Omicron spike epitopes are more interacting to IgG via better MHC recognition than Delta variant

BACKGROUND

Recently, in Nov 2021, in South Africa, the SARS CoV-2 variant Omicron was found to be highly infectious and transmissible but with the least fatality. It occupies the nasopharynx-oropharynx and easily spreads. The epidemiological data/reports suggest that several vaccines failed to neutralize Omicron. It has a large number of spike mutations and the RNA/protein vaccines were developed from its predecessors that may justify its escape in most neutralization reactions. Its lower immuno-suppression/cytokine-storming/inflammatory-response effects need exploration.

OBJECTIVES

In the current study, we attempted to delineate the comparative interaction of different variants' spikes with multiple recognition sites on IgG and HLA-typing of MHC class and I and II.

METHODS

All SARS-CoV-2 spike-proteins/human-IgG/MHC-I & II were obtained from the NCBI/ PDB/GISAID database. Initial 3D-structures of the unavailable proteins were constructed by Homology-Modeling (Swissmodel-Expasy) and optimized (PROCHECK). Molecular-docking of spike-IgG/spike- I & MHC-II was performed (HADDOCK2.4/HawkDock) with active-residue screening (CPORT). Antigenicity of epitopes was determined (Vaxigen v2.0-server) and the epitope-model prepared (PEP-FOLD3-server). The binding-affinity/biological-interfaces/visualize were performed (PRODIGY-PyMOL2). We also examined the genesis of feasible transition pathways of functional docked complexes (iMODs) of MHC with different epitopes and antibodies of IgG with different variants. Further, Molecular-Dynamic-Simulation was performed by GROMACS 2023.1 software package. The MD-simulation was run with 100 ns (300 k-heating/1-atm pressure).

RESULTS

Surface-area with interactomes, H-bonding and polar/non-polar bonding were the highest in Omicron spike-IgG interaction. Unlike other variants, both the L and H chains of at least three different recognition sites of IgG interact with the N-terminal and C-terminal RBD of the S1-portion and partially bind to S2. In other cases, binding was observed in either NTD or CTD with a lesser number of bonding-interactomes, especially in Delta spike-Ab interaction. In the case of MHC class-I & II, the highest binding affinity/surface was noticed by Omicron and least by the Delta variant. The MD simulation data of lower RMSD values of the Delta and Omicron variants indicate improved structural stability and less departure from the initial conformation. Better binding to the IgG and MHC molecules explains Omicron's little ability in immune invasion.

Conformational response to ligand binding of TMPRSS2, a protease involved in SARS-CoV-2 infection: Insights through computational modeling

Thanks to the considerable research which has been undertaken in the last few years to improve our understanding of the biology and mechanism of action of SARS-CoV-2, we know how the virus uses its surface spike protein to infect host cells. The transmembrane prosthesis, serine 2 (TMPRSS2) protein, located on the surface of human cells, recognizes the cleavage site in the spike protein, leading to the release of the fusion peptide and entry of the virus into the host cells. Because of its role, TMPRSS2 has been proposed as a drug target to prevent infection by the virus. In this study, we aim to increase our understanding of TMPRSS2 using long scale microsecond atomistic molecular dynamics simulations, focusing on the conformational changes over time. The comparison between simulations conducted on the protein in the native (apo) and inhibited form (holo), has shown that in the holo form the inhibitor stabilizes the catalytic site and induces rearrangements in the extracellular domain of the protein. In turn, it leads to the formation of a new cavity in the vicinity of the ligand binding pocket that is stable in the microsecond time scale. Given the low specificity of known protease inhibitors, these findings suggest a new potential drug target site that can be used to improve TMPRSS2 specific recognition by newly designed inhibitors.

Interactions between carbon nanotubes and external structures of SARS-CoV-2 using molecular docking and molecular dynamics

Molecular modeling techniques are used to describe the process of interaction between nanotubes and the main structures of the Covid-19 virus: the envelope protein, the main protease, and the Spike glycoprotein. Molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics simulations provide information on the mean squared deviation of atomic positions ​​between 0.5 and 3.0 Å. The Gibbs free energy model and solvent accessible surface area approaches are used. Through the results obtained through molecular dynamics simulations, it is noted that the zig-zag nanotube prefers to interact with E-pro, M-pro, and S-gly, respectively. Molecular couplings and free energy showed that the S-gly active site residues strongly interact with zigzag, chiral, and armchair nanotubes, in this order. The interactions demonstrated in this manuscript may predict some promising candidates for virus antagonists, which may be confirmed through experimental approaches.

In silico analysis of substitution mutations in the β-globin gene in Turkish population of β-thalassemia

Beta-thalassemia is a genetic blood disorder represented by anomalies in hemoglobin's beta chain production. Most hemoglobin defects are a result of mutations of the structural β-globin gene. Many diseases, including β-thalassemia, benefit from computational studies that aid researchers in investigating the association of genotype and phenotype. In this study, the alanine substitution mutations of the β-globin protein sub-units in the Turkish population (Hb Ankara, Hb Siirt and Hb Izmir) and the effects of those mutations on the β-globin protein structure and performance are examined using molecular dynamics simulation. While Hb Ankara variant showed a non-conservative mutation, Hb Siirt and Hb Izmir showed a semi-conservative mutation. RMSF values of Hb Siirt, between residues 95 and 99, were higher than wild-type and the other mutant proteins. The residues of Hb Ankara showed lower fluctuation compared to the other structures. The mean ROG values were 1.47 nm, 1.46 nm, 1.49 nm and 1.48 and the average number of the hydrogen bonds were 92, 100, 99, and 89 for Hb Ankara, Hb Siirt and Hb Izmir, respectively. Moreover, a significant increase in overall motion in Hb Siirt was observed based on PCA analysis. Hb Siirt substitution mutation might cause an effect in β-globin proteins which could impact the protein function. This indicates a major role on beta globin subunit's stability for alanine on 27th position. However, Hb Ankara and Hb Izmir variants may act as a silent mutation, since these two mutations did not show a large change in the dynamics of the protein.Communicated by Ramaswamy H. Sarma.

Molecular dynamic simulation suggests stronger interaction of Omicron-spike with ACE2 than wild but weaker than Delta SARS-CoV-2 can be blocked by engineered S1-RBD fraction

The SARS-CoV-2 claimed millions of lives, globally. Occurring from Wuhan (wild type) in December, 2019, it constantly mutated to Omicron (B.1.1.529), the predecessor to Delta. Omicron having ~ 32 spike mutations has variable infectivity-multiplicity-immuno-invasive properties. Understanding of its mutational effect on ACE2-binding/disease severity and developing preventive/therapeutic strategies are important. The binding affinities of Wuhan/Delta/Omicron spikes (PDB/GISAID/SWISS-MODEL) were docked (HADDOCK2.4) with ACE2 and compared by competitive-docking (PRODIGY). The protein structural stability was verified by kinetic-data/Ramachandran-plot (Zlab/UMassMedBioinfo). After several trials, a 59 amino acid (453ARG-510VAL) peptide-cut (Expasy-server) of the wild-type spike RBD with some desired mutants (THR500SER/THR500GLY/THR500ALA/THR500CYS) was blindly/competitively docked (PyMOL-V2.2.2) to block the Omicron-ACE2 binding. We examined molecular dynamic simulation (iMOD-server, with 9000 cycles/300 k-heating/1 atm pressure for system equilibration for 50 ns-run) of ACE2 and two CUTs with different SARS-CoV-2 variants. The binding-affinity of Omicron-ACE2 is slightly higher than the rest two in competitive docking setup. During individual (1:1) docking, Omicron showed little higher than wild type but much weaker binding affinity than Delta. Competitive docking suggests ten H-bonding (1.3–2.4 Å) with highly favorable energy values/Van-der-Walls-force/Haddock score for more stable-binding of Omicron-RBD with ACE2. Blind docking of different CUTs (wild/mutants) and Omicron to ACE2 completely rejected the Omicron-RBD from ACE2-target. The best blocking/binding affinity of −16.4 and −13 kcal/mole were observed in the case of THR500SER and THR500GLY, respectively, with multiple H-bonding 1.9–2.2 Å. These are supported by the MD-simulation results. So, the spike binding affinities were Delta > Omicron > wild in 1:1 docking with ACE2. Considering the wild type is non-existing nowadays, Omicron showed less ACE2 binding properties. The 59 cut of spike-RBD and its mutant THR500SER/THR500GLY may be further screened as universal blockers of this virus.

Argonaute 2 modulates EGFR-RAS signaling to promote mutant HRAS and NRAS-driven malignancies

Activating mutations in RAS GTPases drive nearly 30% of all human cancers. Our prior work described an essential role for Argonaute 2 (AGO2), of the RNA-induced silencing complex, in mutant KRAS-driven cancers. Here, we identified a novel endogenous interaction between AGO2 and RAS in both wild-type (WT) and mutant HRAS/NRAS cells. This interaction was regulated through EGFR-mediated phosphorylation of Y393-AGO2, and utilizing molecular dynamic simulation, we identified a conformational change in pY393-AGO2 protein structure leading to disruption of the RAS binding site. Knockdown of AGO2 led to a profound decrease in proliferation of mutant HRAS/NRAS-driven cell lines but not WT RAS cells. These cells demonstrated oncogene-induced senescence (OIS) as evidenced by β-galactosidase staining and induction of multiple downstream senescence effectors. Mechanistically, we discovered that the senescent phenotype was mediated via induction of reactive oxygen species. Intriguingly, we further identified that loss of AGO2 promoted a novel feed forward pathway leading to inhibition of the PTP1B phosphatase and activation of EGFR-MAPK signaling, consequently resulting in OIS. Taken together, our study demonstrates that the EGFR-AGO2-RAS signaling axis is essential for maintaining mutant HRAS and NRAS-driven malignancies.

Capturing the geometry, function, and evolution of enzymes with 3D templates

Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs, or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template‐based functional site detection will be a powerful tool in the task of characterizing a vast number of new protein models.

A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing

Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant TB (MDR-TB) become a major hurdle in its therapy. These MDR strains utilize the multidrug efflux pump as a decisive weapon to fight against antitubercular drugs. Tap membrane protein was observed as a crucial multidrug efflux pump in M. tuberculosis and its critical implication in MDR-MTB development makes it an effective drug target. In the present study, we have utilized various in silico approaches to predict the applicability of FDA-approved ion channel inhibitors and blockers as therapeutic leads against Tuberculosis by targeting multidrug efflux protein; Tap in MTB. Tap protein structure is predicted by Phyre2 server followed by model refinement, validation, physio-chemical catheterization and target prediction. Further, the interaction between Tap protein and ligands were analysed by molecular docking and MD simulation run of 100 ns. Based on implication and compatibility, 18 FDA-approved ion channel inhibitors and blockers are selected as a ligand against the Tap protein and eventually observed five ligands; Glimepiride, Flecainide, Flupiritine, Nimodipine and Amlodipine as promising compounds which have exhibited the significant stable interaction with Tap protein and are proposed to modulate or interfere with its activity. These compounds illustrated the substantial docking score and total binding enthalpy more than -7 kcal/mol and -42 kcal/mol respectively which implies that the selected FDA-approved compounds can spontaneously interact with the Tap protein to modulate its function. This study proposed Tap protein as a prominent drug target in MTB and investigated compounds that show considerable interaction with the Tap protein as potential therapeutic molecules. These interactions may lead to modulating or inhibit the activity of drug efflux protein thereby making MTB susceptible to antitubercular drugs.

The Inhibitors of CDK4/6 from a Library of Marine Compound Database: A Pharmacophore, ADMET, Molecular Docking and Molecular Dynamics Study

Background: CDK4/6 (Cyclin-dependent kinases 4/6) are the key promoters of cell cycle transition from G1 phase to S phase. Thus, selective inhibition of CDK4/6 is a promising cancer treatment. Methods: A total of 52,765 marine natural products were screened for CDK4/6. To screen out better natural compounds, pharmacophore models were first generated, then the absorption, distribution, metabolism, elimination, and toxicity (ADMET) were tested, followed by molecular docking. Finally, molecular dynamics simulation was carried out to verify the binding characteristics of the selected compounds. Results: Eighty-seven marine small molecules were screened based on the pharmacophore model. Then, compounds 41369 and 50843 were selected according to the ADMET and molecular docking score for further kinetic simulation evaluation. Finally, through molecular dynamics analysis, it was confirmed that compound 50843 maintained a stable conformation with the target protein, so it has the opportunity to become an inhibitor of CDK4/6. Conclusion: Through structure-based pharmacophore modeling, ADMET, the molecular docking method and molecular dynamics (MD) simulation, marine natural compound 50843 was proposed as a promising marine inhibitor of CDK4/6.

Conformational variation in enzyme catalysis: A structural study on catalytic residues

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We introduce a pipeline to compare and contrast active sites from homologous enzymes in 3D.

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Comprehensive structural study covering enzymes from a large functional space.

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High heterogeneity in magnitude of active site flexibililty between enzyme families.

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Diffferent catalytic residue types and functions relate to different degrees of flexibility.

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Four paradigms classify enzymes according to the structural behaviour during catalysis.

Conformational variation in catalytic residues can be captured as alternative snapshots in enzyme crystal structures. Addressing the question of whether active site flexibility is an intrinsic and essential property of enzymes for catalysis, we present a comprehensive study on the 3D variation of active sites of 925 enzyme families, using explicit catalytic residue annotations from the Mechanism and Catalytic Site Atlas and structural data from the Protein Data Bank. Through weighted pairwise superposition of the functional atoms of active sites, we captured structural variability at single-residue level and examined the geometrical changes as ligands bind or as mutations occur. We demonstrate that catalytic centres of enzymes can be inherently rigid or flexible to various degrees according to the function they perform, and structural variability most often involves a subset of the catalytic residues, usually those not directly involved in the formation or cleavage of bonds. Moreover, data suggest that 2/3 of active sites are flexible, and in half of those, flexibility is only observed in the side chain. The goal of this work is to characterise our current knowledge of the extent of flexibility at the heart of catalysis and ultimately place our findings in the context of the evolution of catalysis as enzymes evolve new functions and bind different substrates.

A universal in silico V(D)J recombination strategy for developing humanized monoclonal antibodies

BACKGROUND

Humanization of mouse monoclonal antibodies (mAbs) is crucial for reducing their immunogenicity in humans. However, humanized mAbs often lose their binding affinities. Therefore, an in silico humanization method that can prevent the loss of the binding affinity of mAbs is needed.

METHODS

We developed an in silico V(D)J recombination platform in which we used V(D)J human germline gene sequences to design five humanized candidates of anti-tumor necrosis factor (TNF)-α mAbs (C1-C5) by using different human germline templates. The candidates were subjected to molecular dynamics simulation. In addition, the structural similarities of their complementarity-determining regions (CDRs) to those of original mouse mAbs were estimated to derive the weighted interatomic root mean squared deviation (wRMSDi) value. Subsequently, the correlation of the derived wRMSDi value with the half maximal effective concentration (EC50) and the binding affinity (KD) of the humanized anti-TNF-α candidates was examined. To confirm whether our in silico estimation method can be used for other humanized mAbs, we tested our method using the anti-epidermal growth factor receptor (EGFR) a4.6.1, anti-glypican-3 (GPC3) YP9.1 and anti-α4β1 integrin HP1/2L mAbs.

RESULTS

The R2 value for the correlation between the wRMSDi and log(EC50) of the recombinant Remicade and those of the humanized anti-TNF-α candidates was 0.901, and the R2 value for the correlation between wRMSDi and log(KD) was 0.9921. The results indicated that our in silico V(D)J recombination platform could predict the binding affinity of humanized candidates and successfully identify the high-affinity humanized anti-TNF-α antibody (Ab) C1 with a binding affinity similar to that of the parental chimeric mAb (5.13 × 10-10). For the anti-EGFR a4.6.1, anti-GPC3 YP9.1, and anti-α4β1 integrin HP1/2L mAbs, the wRMSDi and log(EC50) exhibited strong correlations (R2 = 0.9908, 0.9999, and 0.8907, respectively).

CONCLUSIONS

Our in silico V(D)J recombination platform can facilitate the development of humanized mAbs with low immunogenicity and high binding affinities. This platform can directly transform numerous mAbs with therapeutic potential to humanized or even human therapeutic Abs for clinical use.

Structure elucidation and docking analysis of 5M mutant of T1 lipase Geobacillus zalihae

5M mutant lipase was derived through cumulative mutagenesis of amino acid residues (D43E/T118N/E226D/E250L/N304E) of T1 lipase from Geobacillus zalihae. A previous study revealed that cumulative mutations in 5M mutant lipase resulted in decreased thermostability compared to wild-type T1 lipase. Multiple amino acids substitution might cause structural destabilization due to negative cooperation. Hence, the three-dimensional structure of 5M mutant lipase was elucidated to determine the evolution in structural elements caused by amino acids substitution. A suitable crystal for X-ray diffraction was obtained from an optimized formulation containing 0.5 M sodium cacodylate trihydrate, 0.4 M sodium citrate tribasic pH 6.4 and 0.2 M sodium chloride with 2.5 mg/mL protein concentration. The three-dimensional structure of 5M mutant lipase was solved at 2.64 Å with two molecules per asymmetric unit. The detailed analysis of the structure revealed that there was a decrease in the number of molecular interactions, including hydrogen bonds and ion interactions, which are important in maintaining the stability of lipase. This study facilitates understanding of and highlights the importance of hydrogen bonds and ion interactions towards protein stability. Substrate specificity and docking analysis on the open structure of 5M mutant lipase revealed changes in substrate preference. The molecular dynamics simulation of 5M-substrates complexes validated the substrate preference of 5M lipase towards long-chain p-nitrophenyl-esters.

In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors

COVID-19, a new strain of coronavirus family, was identified at the end of 2019 in China. The COVID-19 virus spread rapidly all over the world. Scientists strive to find virus-specific antivirals for the treatment of COVID-19. The present study reports a molecular docking study of the stilbenolignans and SARS-CoV-2 main protease (SARS-CoV-2 Mpro) inhibitors. The detailed interactions between the stilbenolignan analogues and SARS-CoV-2 Mpro inhibitors were determined as hydrophobic bonds, hydrogen bonds and electronic bonds, inhibition activity, ligand efficiency, bonding type and distance and etc. The binding energies of the stilbenolignan analogues were obtained from the molecular docking of SARS-CoV-2 Mpro. Lehmbachol D, Maackolin, Gnetucleistol, Gnetifolin F, Gnetofuran A and Aiphanol were found to be -7.7, -8.2, -7.3, -8.5, -8.0 and -7.3 kcal/mol, respectively. Osirus, Molinspiration and SwissADME chemoinformatic tools were used to examine ADMET properties, pharmacokinetic parameters and toxicological characteristics of the stilbenolignan analogues. All analogues obey the Lipinski's rule of five. Furthermore, stilbenolignan analogues were studied to predict their binding affinities against SARS-CoV-2 Mpro using molecular modeling and simulation techniques, and the binding free energy calculations of all complexes were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) method. With the data presented here it has been observed that these analogues may be a good candidate for SARS-CoV-2 Mpro in vivo studies, so more research can be done on stilbenolignan analogues.

Phytochemical Moieties From Indian Traditional Medicine for Targeting Dual Hotspots on SARS-CoV-2 Spike Protein: An Integrative in-silico Approach

SARS-CoV-2 infection across the world has led to immense turbulence in the treatment modality, thus demanding a swift drug discovery process. Spike protein of SARS-CoV-2 binds to ACE2 receptor of human to initiate host invasion. Plethora of studies demonstrate the inhibition of Spike-ACE2 interactions to impair infection. The ancient Indian traditional medicine has been of great interest of Virologists worldwide to decipher potential antivirals. Hence, in this study, phytochemicals (1,952 compounds) from eight potential medicinal plants used in Indian traditional medicine were meticulously collated, based on their usage in respiratory disorders, along with immunomodulatory and anti-viral potential from contemporary literature. Further, these compounds were virtually screened against Receptor Binding Domain (RBD) of Spike protein. The potential compounds from each plant were prioritized based on the binding affinity, key hotspot interactions at ACE2 binding region and glycosylation sites. Finally, the potential hits in complex with spike protein were subjected to Molecular Dynamics simulation (450 ns), to infer the stability of complex formation. Among the compounds screened, Tellimagrandin-II (binding energy of −8.2 kcal/mol and binding free energy of −32.08 kcal/mol) from Syzygium aromaticum L. and O-Demethyl-demethoxy-curcumin (binding energy of −8.0 kcal/mol and binding free energy of −12.48 kcal/mol) from Curcuma longa L. were found to be highly potential due to their higher binding affinity and significant binding free energy (MM-PBSA), along with favorable ADMET properties and stable intermolecular interactions with hotspots (including the ASN343 glycosylation site). The proposed hits are highly promising, as these are resultant of stringent in silico checkpoints, traditionally used, and are documented through contemporary literature. Hence, could serve as promising leads for subsequent experimental validations.

Predicting binding sites from unbound versus bound protein structures

We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITE_csc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew’s correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.

Screening and insilico analysis of deleterious nsSNPs (missense) in human CSF3 for their effects on protein structure, stability and function

Human granulocyte colony stimulating factor (hG-CSF), known as CSF3, plays an important role in the growth, differentiation, proliferation, survival, and activation of the granulocyte cell lineage such as neutrophils and their precursors. Functional reduction in native CSF3 protein results in reduced proliferation and activation of neutrophils and leads to neutropenia. Single nucleotide polymorphisms (SNPs) in the CSF3 gene may have deleterious effects on the CSF3 protein function. This study was undertaken to find the functional SNPs in the human CSF3 gene. Results suggest that 18.9% of all the SNPs in the dbSNP database for CSF3 gene were present in the coding region. Out of 59 non-synonymous SNPs (nsSNPs), 26 nsSNPs were predicted to be non-tolerable by SIFT whereas 18 and 7 nsSNPs were predicted as probably damaging and possibly damaging, respectively by PolyPhen. Among this 31 nsSNPs, 16 nsSNPs were identified to be potentially deleterious by PANTHER server, and 4 nsSNPs were found to be neutral by PROVEAN. SNPAnalyzer predicted 7 nsSNPs to be neutral phenotype and the remaining 24 nsSNPs to be associated with diseases. Among the predicted nsSNPs, rs144408123, rs144408123, rs145136406, rs145311241, rs373191696, rs762945096, rs763688260, rs767572172, rs775326370, rs777777864, rs777983866, rs781596455, rs139072004, rs757612684, rs772911210, rs139072004, rs746634544, rs749993200, rs763426127, rs772466210 were identified as deleterious and potentially damaging. I-Mutant analysis revealed that th 20 nsSNPs were important for protein stability of CSF3. Therefore, th 20 nsSNPs may be used for the wider population-based genetic studies and also should be taken into account while engineering the recombinant CSF3 protein for clinical use.

Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures

Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.

Combined comparative molecular field analysis, comparative molecular similarity indices analysis, molecular docking and molecular dynamics studies of histone deacetylase 6 inhibitors

Human histone deacetylase isoform 6 (HDAC6) has been shown to have an immense role in cell motility and aggresome formation and is being an attractive selective target for the treatment of multiple tumour types and neurodegenerative conditions. The discovery of selective HDAC6 inhibitors with new chemical functionalities is therefore of utmost interest to researchers. In order to examine the structural requirements for HDAC6-specific inhibitors and to derive predictive model which can be used for designing new selective HDAC6 inhibitors, a three-dimensional quantitative structure-activity relationship study was carried out on a diverse set of ligands using common feature-based pharmacophore alignment followed by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models displayed high correlation of 0.978 and 0.991 for best CoMFA and CoMSIA models, respectively, and a good statistical significance. The model could be used for predicting activities of the test set compounds as well as for deriving useful information regarding steric, electrostatic, hydrophobic properties of the molecules used in this study. Further, the training and test set molecules were docked into the HDAC6 binding site and molecular dynamics was carried out to suggest structural requirements for design of new inhibitors.

Perspectives on Structural Molecular Biology Visualization: From Past to Present

Visualization has been a key technology in the progress of structural molecular biology for as long as the field has existed. This perspective describes the nature of the visualization process in structural studies, how it has evolved over the years, and its relationship to the changes in technology that have supported and driven it. It focuses on how technical advances have changed the way we look at and interact with molecular structure, and how structural biology has fostered and challenged that technology.

In silico mutational analysis and identification of stability centers in human interleukin-4

Interleukin-4 (IL-4) is a multifunctional cytokine that plays a critical role in apoptosis, differentiation and proliferation. The intensity of IL4 response depends upon binding to its receptor, IL-4R. The therapeutic efficiency of interleukins can be increased by generating structural mutants having greater stability. In the present work, attempts were made to increase the stability of human IL-4 using in-silico site directed mutagenesis. Different orthologous sequences of IL4 from Pan troglodytes, Aotusnigriceps, Macacamulatta, Papiohamadryas, Chlorocebusaethiops, Vicugnapacos, Susscrofa and Homo sapiens were aligned using Clustal Omega that revealed the conserved and non-conserved positions. For each non-conserved position, possible favorable and stabilizing mutations were found using CUPSAT with predicted ΔΔG (kcal/mol). The one with highest ΔΔG (kcal/mol) among all possible mutations, for each non-conserved position was selected and introduced manually in human IL-4 sequence resulting in multiple mutants of IL-4. Mutant proteins were modeled using structure of IL4 (PDB ID: 2B8U) as a template by SWISS MODEL. The mutants A49L and Q106T were identified to have stability centre using SCide. Molecular dynamics and docking analysis also confirmed the mutants stability and binding respectively. Mutants A49L and Q106T had -7.580079 kcal/mol and -39.418124 kcal/mol respectively lesser energy value than the wild type IL4. The result suggested that, the stability of human IL-4 has been increased by mutation.

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

Comparing pharmacophore models derived from crystallography and NMR ensembles

NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.

In Silico Screening and Binding Characterization of Small Molecules toward a G-Quadruplex Structure Formed in the Promoter Region of c-MYC Oncogene

Overexpression of c-MYC oncogene is associated with cancer pathology. Expression of c-MYC is regulated by the G-quadruplex structure formed in the G-rich segment of nuclease hypersensitive element (NHE III₁), that is, "Pu27", which is localized in the promoter region. Ligand-induced stabilization of the Pu27 structure has been identified as a novel target for cancer therapeutics. Here, we have explored the library of synthetic compounds against the predefined binding site of Pu27. Three compounds were selected based on the docking analyses; they were further scrutinized using all atom molecular dynamics simulations in an explicit water model. Simulated trajectories were scrutinized for conformational stability and ligand binding free energy estimation; essential dynamic behavior was determined using principal component analysis. One of the molecules, "TPP (1-(3-(4-(1,2,3-thiadiazol-4-yl)phenoxy)-2-hydroxypropyl)-4-carbamoylpiperidinium)", with the best results was considered for further evaluation. The theoretical observations are supported well by biophysical analysis using circular dichroism, isothermal titration calorimetry, and high-resolution NMR spectroscopy indicating association of TPP with Pu27. The in vitro studies were then translated into c-MYC overexpression in the T47D breast cancer cell line. Biological evaluation through the MTT assay, flow cytometric assay, RT-PCR, and reporter luciferase assay suggests that TPP downregulates the expression of c-MYC oncogene by arresting its promoter region. In silico and in vitro observations cumulatively suggest that the novel skeleton of TPP could be a potential anticancer agent by stabilizing the G-quadruplex formed in the Pu27 and consequently downregulating the expression of c-MYC oncogene. Derivation of new molecules on its skeleton may confer anticancer therapeutics for the next generation.

Insights into ligand binding to a glutathione S-transferase from mango: Structure, thermodynamics and kinetics

We studied a mango glutathione S-transferase (GST) (Mangifera indica) bound to glutathione (GSH) and S-hexyl glutathione (GSX). This GST Tau class (MiGSTU) had a molecular mass of 25.5 kDa. MiGSTU Michaelis-Menten kinetic constants were determined for their substrates obtaining a Km, Vmax and kcat for CDNB of 0.792 mM, 80.58 mM min-1 and 68.49 s-1 respectively and 0.693 mM, 105.32 mM min-1 and 89.57 s-1, for reduced GSH respectively. MiGSTU had a micromolar affinity towards GSH (5.2 μM) or GSX (7.8 μM). The crystal structure of the MiGSTU in apo or bound to GSH or GSX generated a model that explains the thermodynamic signatures of binding and showed the importance of enthalpic-entropic compensation in ligand binding to Tau-class GST enzymes.

How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations

The root-mean-square deviation (RMSD) is a similarity measure widely used in analysis of macromolecular structures and dynamics. As increasingly larger macromolecular systems are being studied, dimensionality effects such as the "curse of dimensionality" (a diminishing ability to discriminate pairwise differences between conformations with increasing system size) may exist and significantly impact RMSD-based analyses. For such large bimolecular systems, whether the RMSD or other alternative similarity measures might suffer from this "curse" and lose the ability to discriminate different macromolecular structures had not been explicitly addressed. Here, we show such dimensionality effects for both weighted and nonweighted RMSD schemes. We also provide a mechanism for the emergence of the "curse of dimensionality" for RMSD from the law of large numbers by showing that the conformational distributions from which RMSDs are calculated become increasingly similar as the system size increases. Our findings suggest the use of weighted RMSD schemes for small proteins (less than 200 residues) and nonweighted RMSD for larger proteins when analyzing molecular dynamics trajectories.

Probing the intermolecular interactions of PPARγ-LBD with polyunsaturated fatty acids and their anti-inflammatory metabolites to infer most potential binding moieties

Background

PPARγ is an isoform of peroxisome proliferator-activated receptor (PPAR) belonging to a super family of nuclear receptors. PPARγ receptor is found to play a crucial role in the modulation of lipid and glucose homeostasis. Its commotion has been reported to play a significant role in a broad spectrum of diseases such as type 2 diabetes mellitus, inflammatory diseases, Alzheimer’s disease, and in some cancers. Hence, PPARγ is an important therapeutic target. Polyunsaturated fatty acids (PUFAs) and their metabolites (henceforth referred to as bioactive lipids) are known to function as agonists of PPARγ. However, agonistic binding modes and affinity of these ligands to PPARγ are yet to be deciphered.

Methods

In this study, we performed a comparative molecular docking, binding free energy calculation and molecular dynamics simulation to infer and rank bioactive lipids based on the binding affinities with the ligand binding domain (LBD) of PPARγ.

Results

The results inferred affinity in the order of resolvin E1 > neuroprotectin D1 > hydroxy-linoleic acid > docosahexaenoic acid > lipoxin A4 > gamma-linolenic acid, arachidonic acid > alpha-linolenic acid > eicosapentaenoic acid > linoleic acid. Of all the bioactive lipids studied, resolvin E1, neuroprotectin D1 and hydroxy-linoleic acid showed significant affinity comparable to proven PPARγ agonist namely, rosiglitazone, in terms of Glide XP docking score, H-bond formation with the key residues, binding free energy and stable complex formation with LBD favouring co-activator binding, as inferred through Molecular Dynamics trajectory analysis.

Conclusion

Hence, these three bioactive lipids (resolvin E1, neuroprotectin D1 and hydroxy-linoleic acid) may be favourably considered as ideal drug candidates in therapeutic modulation of clinical conditions such as type 2 DM, Alzheimer’s disease and other instances where PPARγ is a key player.

Strain analysis of protein structures and low dimensionality of mechanical allosteric couplings

In many proteins, especially allosteric proteins that communicate regulatory states from allosteric to active sites, structural deformations are functionally important. To understand these deformations, dynamical experiments are ideal but challenging. Using static structural information, although more limited than dynamical analysis, is much more accessible. Underused for protein analysis, strain is the natural quantity for studying local deformations. We calculate strain tensor fields for proteins deformed by ligands or thermal fluctuations using crystal and NMR structure ensembles. Strains-primarily shears-show deformations around binding sites. These deformations can be induced solely by ligand binding at distant allosteric sites. Shears reveal quasi-2D paths of mechanical coupling between allosteric and active sites that may constitute a widespread mechanism of allostery. We argue that strain-particularly shear-is the most appropriate quantity for analysis of local protein deformations. This analysis can reveal mechanical and biological properties of many proteins.

Ensemble-based evaluation for protein structure models

Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein.

Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models.

Availability and implementation:https://bitbucket.org/mjamroz/flexscore

Contact:dkihara@purdue.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Multilevel Precision-Based Rational Design of Chemical Inhibitors Targeting the Hydrophobic Cleft of Toxoplasma gondii Apical Membrane Antigen 1 (AMA1)

Toxoplasma gondii is an intracellular Apicomplexan parasite and a causative agent of toxoplasmosis in human. It causes encephalitis, uveitis, chorioretinitis, and congenital infection. T. gondii invades the host cell by forming a moving junction (MJ) complex. This complex formation is initiated by intermolecular interactions between the two secretory parasitic proteins-namely, apical membrane antigen 1 (AMA1) and rhoptry neck protein 2 (RON2) and is critically essential for the host invasion process. By this study, we propose two potential leads, NSC95522 and NSC179676 that can efficiently target the AMA1 hydrophobic cleft, which is a hotspot for targeting MJ complex formation. The proposed leads are the result of an exhaustive conformational search-based virtual screen with multilevel precision scoring of the docking affinities. These two compounds surpassed all the precision levels of docking and also the stringent post docking and cumulative molecular dynamics evaluations. Moreover, the backbone flexibility of hotspot residues in the hydrophobic cleft, which has been previously reported to be essential for accommodative binding of RON2 to AMA1, was also highly perturbed by these compounds. Furthermore, binding free energy calculations of these two compounds also revealed a significant affinity to AMA1. Machine learning approaches also predicted these two compounds to possess more relevant activities. Hence, these two leads, NSC95522 and NSC179676, may prove to be potential inhibitors targeting AMA1-RON2 complex formation towards combating toxoplasmosis.

The effects of external electric fields of 900 MHz and 2450 MHz frequencies onαβ-tubulin dimer stabilized by paclitaxel: Molecular dynamics approach

Using molecular dynamics simulation method, the effects of external electric fields of 900[Formula: see text]MHz and 2450 frequencies on [Formula: see text]-tubulin dimer stabilized by paclitaxel, have been modeled. Due to this purpose, two systems, (A) [Formula: see text]-tubulin dimer and (B) [Formula: see text]-tubulin dimer stabilized by paclitaxel, were exposed to an external electric field of 0.01[Formula: see text]V/nm with frequency values of 900[Formula: see text]MHz and 2450[Formula: see text]MHz. It was found that application of these fields, which are in the range of cell phone and microwave frequencies, increased the flexibility of each system. Since paclitaxel, as chemotherapy drug, is used to increase the rigidity of dimer, application of such fields may disturb the effect of paclitaxel on the dimer. Consequently, negative side effects on the chemotherapy process may be observed.

Evolutionary Conserved Positions Define Protein Conformational Diversity

Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.

Methemoglobin Formation and Characterization of Hemoglobin Adducts of Carcinogenic Aromatic Amines and Heterocyclic Aromatic Amines

Arylamines (AAs) and heterocyclic aromatic amines (HAAs) are structurally related carcinogens formed during the combustion of tobacco or cooking of meat. They undergo cytochrome P450 mediated N-hydroxylation to form metabolites which bind to DNA and lead to mutations. The N-hydroxylated metabolites of many AAs also can undergo a co-oxidation reaction with oxy-hemolgobin (HbO2) to form methemoglobin (met-Hb) and the arylnitroso intermediates, which react with the β-Cys(93) chain of Hb to form Hb-arylsulfinamide adducts. The biochemistry of arylamine metabolism has been exploited to biomonitor certain AAs through their Hb arylsulfinamide adducts in humans. We examined the reactivity of HbO2 with the N-hydroxylated metabolites of 4-aminobiphenyl (ABP, HONH-ABP), aniline (ANL, HONH-ANL), and the HAAs 2-amino-9H-pyrido[2,3-b]indole (AαC, HONH-AαC), 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP, HONH-PhIP), and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx, HONH-MeIQx). HONH-ABP, HO-ANL, and HONH-AαC induced methemoglobinemia and formed Hb sulfinamide adducts. However, HONH-MeIQx and HONH-PhIP did not react with the oxy-heme complex, and met-Hb formation and chemical modification of the β-Cys(93) residue were negligible. Molecular modeling studies showed that the distances between the H-ON-AA or H-ON-HAA substrates and the oxy-heme complex of HbO2 were too far away to induce methemoglobinemia. Different conformational changes in flexible helical and loop regions around the heme pocket induced by the H-ON-AA or H-ON-HAAs may explain the different proclivities of these chemicals to induce methemoglobinemia. Hb-Cys(93β) sulfinamide and sulfonamide adducts of ABP, ANL, and AαC were identified, by Orbitrap MS, following the proteolysis of Hb with trypsin, Glu-C, or Lys-C. Hb sulfinamide and sulfonamide adducts of ABP were identified in the blood of mice exposed to ABP, by Orbitrap MS. This is the first report of the identification of intact Hb sulfinamide adducts of carcinogenic AAs in vivo. The high reactivity of HONH-AαC with HbO2 suggests that the Hb sulfinamide adduct of AαC may be a promising biomarker of exposure to this HAA in humans.

Skeleton selectivity in complexation of chelerythrine and chelerythrine-like natural plant alkaloids with the G-quadruplex formed at the promoter of c-MYC oncogene: in silico exploration

Chelerythrine binds at the 5′ end and arrests the G-quadruplex formed in the promoter region ofc-MYConcogene thus restrict thec-MYCexpression. Position of methoxy group over the core skeleton of chelerythrine determines the binding pattern of ligand.

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).

Performance of the WeNMR CS-Rosetta3 web server in CASD-NMR

We present here the performance of the WeNMR CS-Rosetta3 web server in CASD-NMR, the critical assessment of automated structure determination by NMR. The CS-Rosetta server uses only chemical shifts for structure prediction, in combination, when available, with a post-scoring procedure based on unassigned NOE lists (Huang et al. in J Am Chem Soc 127:1665-1674, 2005b, doi: 10.1021/ja047109h). We compare the original submissions using a previous version of the server based on Rosetta version 2.6 with recalculated targets using the new R3FP fragment picker for fragment selection and implementing a new annotation of prediction reliability (van der Schot et al. in J Biomol NMR 57:27-35, 2013, doi: 10.1007/s10858-013-9762-6), both implemented in the CS-Rosetta3 WeNMR server. In this second round of CASD-NMR, the WeNMR CS-Rosetta server has demonstrated a much better performance than in the first round since only converged targets were submitted. Further, recalculation of all CASD-NMR targets using the new version of the server demonstrates that our new annotation of prediction quality is giving reliable results. Predictions annotated as weak are often found to provide useful models, but only for a fraction of the sequence, and should therefore only be used with caution.

UniAlign: protein structure alignment meets evolution

MOTIVATION

During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins.

RESULTS

In this article, we propose a new protein pairwise structure alignment algorithm (UniAlign) that incorporates additional evolutionary information captured in the form of sequence similarity, sequence profiles and residue conservation. We define a per-residue score (UniScore) as a weighted sum of these and other features and develop an iterative optimization procedure to search for an alignment with the best overall UniScore. Our extensive experiments on CDD, HOMSTRAD and BAliBASE benchmark datasets show that UniAlign outperforms commonly used structure alignment methods. We further demonstrate UniAlign's ability to develop family-specific models to drastically improve the quality of the alignments.

AVAILABILITY AND IMPLEMENTATION

UniAlign is available as a web service at: http://sacan.biomed.drexel.edu/unialign

CONTACT

ahmet.sacan@drexel.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Buried ionizable networks are an ancient hallmark of G protein-coupled receptor activation

Seven-transmembrane receptors (7TMRs) have evolved in prokaryotes and eukaryotes over hundreds of millions of years. Comparative structural analysis suggests that these receptors may share a remote evolutionary origin, despite their lack of sequence similarity. Here we used structure-based computations to compare 221 7TMRs from all domains of life. Unexpectedly, we discovered that these receptors contain spatially conserved networks of buried ionizable groups. In microbial 7TMRs these networks are used to pump ions across the cell membrane in response to light. In animal 7TMRs, which include light- and ligand-activated G protein-coupled receptors (GPCRs), homologous networks were found to be characteristic of activated receptor conformations. These networks are likely relevant to receptor function because they connect the ligand-binding pocket of the receptor to the nucleotide-binding pocket of the G protein. We propose that agonist and G protein binding facilitate the formation of these electrostatic networks and promote important structural rearrangements such as the displacement of transmembrane helix-6. We anticipate that robust classification of activated GPCR structures will aid the identification of ligands that target activated GPCR structural states.