Psi Angles Research Articles

A second view on the X-ray polarization of NGC 4151 with IXPE

We report on the second observing program of the active galactic nucleus NGC 4151 with simultaneous Imaging X-ray Polarimetry Explorer (IXPE; 750 ks) (sim 60 ks) XMM-Newton (sim 75 ks), and NICER (sim 65 ks) pointings. NGC 4151 is the first Type-1 radio-quiet Seyfert galaxy with constrained polarization properties for the X-ray corona. Despite the lower flux state in which the source was re-observed and the resulting higher contribution of the constant reflection component in the energy band, our results are in agreement with the first detection. From polarimetric analysis, a polarization degree Pi = 4.7 pm 1.3<!PCT!> and polarization angle Psi = 77 pm 8 east of north (68<!PCT!> c.l.) were derived in the 2.0 -- 8.0 keV energy range. Combining the two observations leads to polarization properties that are more constrained than those of the individual detections, showing Pi = 4.5 pm 0.9<!PCT!> and Psi = 81 pm 6 (with a detection significance of sim 4.6sigma ). The observed polarization angle aligns very well with the radio emission in this source, supporting, together with the significant polarization degree, a slab or wedge geometry for the X-ray corona. However, a switch in the polarization angle at low energies ($37 in the 2-3.5 keV bin) suggests the presence of another component. When it is included in the spectro-polarimetric fit, a high polarization degree disfavors an interpretation in terms of a leakage through the absorbers, instead pointing to scattering from some kind of mirror.

Deciphering the Impact of Cyclization and Lysine Charges on Antimicrobial Peptides Using Molecular Dynamics Simulations and Density Functional Theory

AbstractThis study utilized molecular dynamics simulations (MD) to explore the impact of cyclization and lysine charges on antimicrobial peptides (AMPs) across eight simulation boxes designed with water and water‐sodium dodecyl sulfate (SDS) micelles. Cyclic AMPs with positively charged lysine residues (c‐AMP+) displayed increased stability in both environments compared to linear AMPs (l‐AMP+), with removal of positive charges notably destabilizing the linear form in water‐SDS. Findings revealed enhanced mobility due to cyclization and charged lysine residues, further boosted by SDS presence. Notably, lysine residues exhibited a stronger affinity for SDS than tryptophan and phenylalanine, especially lysine number 5. Analysis extended to peptide backbone conformation and electronic features, showing cyclization's greater impact on psi and phi angles over charge presence, with linear AMPs displaying lower reactivity than cyclic counterparts. This research underscores the significance of cyclization and lysine charges in AMP behavior, suggesting the potential of c‐AMP+ for further development owing to their improved stability, mobility, and membrane interactions.

On The Preparation Of Training Data For 3D Tracking Of Position And Orientation Of Microswimmers Using Defocusing And Deep Learning

The use of image analysis algorithms based on deep convolutional neural networks (CNNs) is opening new possibilities in experimental techniques for fluid dynamics. In the field of microscopic 3D particle tracking based on defocusing, it has been shown on synthetic images that CNNs can be used to determine not only the depth position but also the orientation of non-spherical microscopic particles or specimens. However, the translation of such approaches to experimental setups is challenging, especially due to the difficulty of obtaining reliable labeled images to train the neural networks. In this work, we propose a method to obtain labeled images of microswimmers from conventional microscopy images, exploiting the properties of the swimming motion at low Reynolds numbers, where inertial effects are negligible. This method allows us to obtain ground truth values of orientation and depth position of a microorganism swimming at a regular pace that can be used as training data for CNNs. The proposed methodology was used to obtain labeled images from experimental videos of the micro-organism Euplotes Vannus. The labeled images were used to train a VGG16 network, providing an estimated uncertainty in the determination of the orientation angles psi, theta, phi, and the depth position Z of 2.4%, 1.4%, 2.3%, and 3.5%, respectively.

NMR detection and conformational dependence of two, three, and four-bond isotope shifts due to deuteration of backbone amides.

NMR isotope shifts occur due to small differences in nuclear shielding when nearby atoms are different isotopes. For molecules dissolved in 1:1 H2O:D2O, the resulting mixture of N-H and N-D isotopes leads to a small splitting of resonances from adjacent nuclei. We used multidimensional NMR to measure isotope shifts for the proteins CUS-3iD and CspA. We observed four-bond 4∆N(ND) isotope shifts in high-resolution 2D 15N-TROSY experiments of the perdeuterated proteins that correlate with the torsional angle psi. Three-bond 3∆C'(ND) isotope shifts detected in H(N)CO spectra correlate with the intraresidue H-O distance, and to a lesser extent with the dihedral angle phi. The conformational dependence of the isotope shifts agree with those previously reported in the literature. Both the 4∆N(ND) and 3∆C'(ND) isotope shifts are sensitive to distances between the atoms giving rise to the isotope shifts and the atoms experiencing the splitting, however, these distances are strongly correlated with backbone dihedral angles making it difficult to resolve distance from stereochemical contributions to the isotope shift. H(NCA)CO spectra were used to measure two-bond 2∆C'(ND) isotope shifts and [D]/[H] fractionation factors. Neither parameter showed significant differences for hydrogen-bonded sites, or changes over a 25° temperature range, suggesting they are not sensitive to hydrogen bonding. Finally, the quartet that arises from the combination of 2∆C'(ND) and 3∆C'(ND) isotope shifts in H(CA)CO spectra was used to measure synchronized hydrogen exchange for the sequence neighbors A315-S316 in the protein CUS-3iD. In many of our experiments we observed minor resonances due to the 10% D2O used for the sample deuterium lock, indicating isotope shifts can be a source of spectral heterogeneity in standard NMR experiments. We suggest that applications ofisotope shifts such as conformational analysis and correlated hydrogen exchange could benefit from the larger magnetic fields becoming available.

Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states.

In the efforts to identify structure-function relationships of protein machinery, computational methods have developed rapidly as tools to assist the scientific community in both structural prediction as well as functional relationships in context of biological pathways. Although solving high resolution structural information has been clear for stable folded macromolecules, similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating and analyzing large diverse ensembles of disordered protein states. IDPConformerGenerator builds conformers that obey geometric, steric, and other physical restraints on the input sequence by sampling backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank. All-atom sidechains can be added using robust Monte Carlo algorithms with expanded rotamer libraries. IDPConformerGenerator has many user-configurable options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to provide structural insights into these states that have key biological functions. Ultimately, a better structural understanding of disordered protein ensembles can open up avenues for future research on pharmaceuticals targeting diseases mediated by IDPs.

Secondary Structure in Amyloids in Relation to Their Wild Type Forms.

The amyloid structures and their wild type forms, available in the PDB database, provide the basis for comparative analyses. Globular proteins are characterised by a 3D spatial structure, while a chain in any amyloid fibril has a 2D structure. Another difference lies in the structuring of the hydrogen bond network. Amyloid forms theoretically engage all the NH and C=O groups of the peptide bonds in a chain with two hydrogen bonds each. In addition, the hydrogen bond network is highly ordered-as perpendicular to the plane of the chain. The β-structure segments provide the hydrogen bond system with an anti-parallel system. The folds appearing in the rectilinear propagation of the segment with the β-structure are caused by just by one of the residues in the sequence-residues with a Rα-helical or Lα-helical conformation. The antiparallel system of the hydrogen bonds in the β-structure sections at the site of the amino acid with a Rα- or Lα-helical conformation changes into a parallel system locally. This system also ensures that the involvement of the C=O and H-N groups in the construction of the interchain hydrogen bond, while maintaining a perpendicular orientation towards the plane of the chain. Conformational analysis at the level of the Phi and Psi angles indicates the presence of the conditions for the structures observed in the amyloids. The specificity of amyloid structures with the dominant conformation expressed as |Psi| = |Phi| reveals the system of organisation present in amyloid fibrils. The Phi, Psi angles, as present in this particular structure, transformed to form |Psi| = |Phi| appear to be ordered co-linearly. Therefore, the calculation of the correlation coefficient may express the distribution around this idealised localisation on the Ramachandran map. Additionally, when the outstanding points are eliminated, the part of amyloid chain can be classified as fulfilling the defined conditions. In addition, the presentation of the chain structure using geometric parameters, V-angle-the angle between the planes of the adjacent peptide bonds (angle versus the virtual axis Cα-Cα) and the radius of the curvature R, depending on the size of the angle V, allows for a quantitative assessment of changes during amyloid transformation.

Interaction models between peptide substrate and Alphavirus Protease nsP2 of Chikungunya and Mayaro and implications to the mechanism of action

The Arbovirus (Arthropod-borne virus) is a group which comprises viruses whose transmission is carried out by arthropod vectors infecting vertebrates. Some arboviruses related to human diseases have been given considerable relevance as Chikungunya and Mayaro of the family Togaviridae, genus Alphavirus. The lack of proper specific treatment has prompted the requirement for deeper structural studies that could unveil leads to new drugs. Among possible targets, viral proteases are recognized as proteins with big potential. These proteins, termed nsP2 in Alphavirus, have the function of cleaving certain regions of the viral polyprotein, being vital to the viral cycle. In this research, we used docking and molecular dynamics to analyze the contact between the protease nsP2 of Alphavirus Chikungunya and Mayaro and substrates formed by peptides with ten amino acid residues. A model of the Mayaro nsP2 was constructed based on homologous proteases. Our study suggests that the glycine specificity motif, a region where a highly conserved glycine residue in position P2 of the protease substrate is positioned, facilitates the nucleophilic attack by assisting in placing the P1 carbonyl group carbon. Stabilization of different substrate regions maybe explained by relevant contacts with the enzyme. Besides that, the phi and psi angles in the outlier region of the Ramachandran plot found for the P2 glycine of the Chikungunya substrate seems to indicate the necessity of this residue that can accommodate angles not allowed to other residues. Communicated by Ramaswamy H. Sarma

Weak gravitational lensing in dark matter and plasma mediums for wormhole-like static aether solution

In this paper, we study the deflection angle for wormhole-like static aether solution by using Gibbons and Werner technique in non-plasma, plasma, and dark matter mediums. For this purpose, we use optical spacetime geometry to calculate the Gaussian optical curvature, then implement the Gauss–Bonnet theorem in weak field limits. Moreover, we compute the deflection angle by using a technique known as Keeton and Petters technique. Furthermore, we analyze the graphical behavior of the bending angle psi with respect to the impact parameter b, mass m as an integration constant, and parameter q in non-plasma and plasma mediums. We examine that the deflection angle is exponentially increasing as direct with charge. Also, we observe that for small values of b, psi increases, and for large values of b the angle decreases. We also considered analysis to the shadow cast of the wormhole relative to an observer at various locations. Comparing it the Schwarzschild shadow, shadow cast is possible for wormhole as r<2m. At r>2m, the Schwarzschild is larger. As rrightarrow infty , we have seen that the behavior of the shadow, as well as the weak deflection angle, approaches that of the Schwarzschild black hole. Overall, the effect of plasma tends to decrease the value of the observables due to the wormhole geometry.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States.

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

The formation of three-grain junctions during solidification. Part II: theory

We provide a simple geometric theory of crystal growth which predicts the shape and final dihedral angle of three-grain junctions of an augite crystal with two plagioclase grains. The predicted dihedral angle Delta depends on the initial impingement angle psi formed by the plagioclase grains, and also on the relative growth rates of the augite and the plagioclase, and shows reasonable agreement with data obtained from natural samples. We show that the two augite-plagioclase grain boundaries will normally curve towards each other, which is consistent with the first two types of junction described in the companion paper. However, the third type, the eagle’s beak, is formed by the meeting of grain boundaries which curve in the same direction. Although it is possible to account for this type of junction by invoking the localised dissolution of one of the plagioclase grains, this is unlikely to occur. A more plausible explanation involves the late impingement of the two plagioclase grains, consistent with the observation that eagles’ beaks are common in gabbros and strongly orthocumulate troctolites, in which the plagioclase framework has not been established by the time augite is growing in substantial quantities. An observed flattening of the curve of Delta values at high values of psi can be explained by taking into account the importance of interfacial energy in late-stage crystallisation.

Secondary structure specific simpler prediction models for protein backbone angles

MotivationProtein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way.ResultsThe new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles phi, psi, theta, and tau. Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results.AvailabilitySAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss.

Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics

Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.

Less (Learning How to Learn) &amp;[prop] More (Learning): Teaching structure‐function with 3D‐printed models in large, undergraduate biochemistry classes

For students to master core biochemical concepts, they must integrate background knowledge from previous courses in biology, chemistry, physics, and math. Students are often inadequately prepared to make the logical jump from chemical structure representations to biochemical macromolecules, leading to multiple misconceptions about structure-function relationships (Villafañe et al., 2011). These misconceptions often lead students to inaccurately describe basic structural details of macromolecules even after their biochemistry education (Harle and Towns, 2013). Biochemistry educators can bridge this gap, and multiple routes have been demonstrated. However, most of these take significant course development. Thus, there is a need for simple, developed structure-function instructional modules scaffolded for students and instructors. Our team of biochemistry educators, 3D-printing experts, and professional assessment designers collectively developed six interactive learning modules teaching structure-function relationships though cheap, 3D-printed macromolecular models. There first three modules cover protein structure, and begin with students connecting amino acids to physically demonstrate phi and psi angles, through secondary, tertiary and quaternary protein structures, and end with the dynamics of product inhibition. The second three modules cover nucleic acids, and ask students to investigate chemical and structural differences between major and minor grooves, transcription-factor/DNA interactions and supercoiling dynamics. Pre- post-module assessments of intervention or control classes show students working with the models made dramatic and significant learning gains over control students. Pre- post-course assessments show the knowledge is retained. We provide all the materials necessary for interested instructors to modify the modules to work in their courses. We also provide an all-virtual module that can be deployed during the pandemic. Harle M, Towns MH (2013) Students' understanding of primary and secondary protein structure: Drawing secondary protein structure reveals student understanding better than simple recognition of structures. Biochemistry and Molecular Biology Education 41: 369-376 Villafañe SM, Bailey CP, Loertscher J, Minderhout V, Lewis JE (2011) Development and analysis of an instrument to assess student understanding of foundational concepts before biochemistry coursework*. Biochemistry and Molecular Biology Education 39: 102-109

Radiated Susceptibility Analysis of Multiconductor Transmission Lines Based on Polynomial Chaos

To address the uncertainties of the radiated susceptibility of multiconductor transmission lines (MTLs), a surrogate model of the MTLs radiated susceptibility is established based on generalized polynomial chaos (gPC), and the gPC is made sparser by combining the adaptive hyperbolic truncation (AHT) scheme and the least angle regression (LAR) method. The uncertainties of the radiated susceptibility of transmission lines are calculated using the adaptive-sparse polynomial chaos (AS-PC) scheme. The parameters related to the incident field, such as elevation angle theta, azimuth angle psi, polarization angle eta, and field amplitude E, are inevitably random. Therefore, these four variables are taken as random input variables, and each of them is subject to different variable distributions. The MTLs model with infinite ground as the reference conductor is adopted, different impedances are used and the AS-PC scheme is combined with transmission line theory to calculate the average, standard deviation and probability distribution of the radiated susceptibility of MTLs. Sobol global sensitivity analysis based on variance decomposition is adopted to calculate the influence of random input variables on the MTLs radiated susceptibility model. The calculation results are compared with the results of the Monte Carlo (MC) method, proving that the proposed method is correct and feasible.

Ion-induced free energy landscapes of Aβ33–42 peptide dimer in wet ionic liquids

The transitions between α-helix and β-sheet conformation of Aβ33–42 peptide dimer in water and an aqueous solution of three ionic liquids (ILs) at two concentrations are investigated using umbrella sampling-based classical molecular dynamics simulations. The ionic liquids chosen for this study are Ethylammonium Mesylate (EAM), Ethylammonium Nitrate (EAN), and Triethylammonium Mesylate (TEAM). The average psi angle of the peptide backbone was used as the reaction coordinate to explore the free energy pathway of the transition. The secondary structure element values were calculated for peptide dimer along the reaction coordinate to probe the conformational changes during the transition between α-helix and β-sheet conformations. We observe the α-helix conformation as the global minimum at low concentration of EAM and EAN ILs and β-sheet conformation as the global minimum at low concentration of TEAM as well as the high concentration of EAM, EAN, and TEAM ILs. The average numbers of intramolecular and intermolecular hydrogen bonds were calculated for the conformations corresponding to the free-energy minima; these numbers are in correlation with the secondary structure element values. The atom-atom radial distribution functions of cation, anion, and water around amide hydrogen and oxygen atoms of the peptide backbone were calculated to understand the peptide-IL interactions. The solvent-accessible surface area of the peptide was calculated to understand the exposure to the solvent during the transition. Finally, to understand the stability of conformations on the free-energy landscape, we calculated van der Waals and Coulomb interaction energies between peptide-cation, peptide anion, and peptide-water molecules. We find that the TEA cation in TEAM contributes more towards the vdWs and less towards Coulomb interactions with the peptide as compared to EA cation in EAM and EAN ILs, which results in destabilization of α-helix conformation in TEAM ILs.

Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.

The prediction of ion ligand–binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters. We then performed a statistical analysis and reclassification for dihedral angle and proposed new methods on the extraction of feature parameters. The methods mainly included applying information entropy on the extraction of polarization charge and hydrophilic–hydrophobic information of amino acids and using position weight matrices on the extraction of position conservation information. In the prediction model, we used the random forest algorithm and obtained better prediction results than previous works. With the independent test, the Matthew's correlation coefficient and accuracy of 10 metal ion ligand–binding residues were larger than 0.07 and 52%, respectively; the corresponding evaluation values of four acid radical ion ligand–binding residues were larger than 0.15 and 86%, respectively. Further, we classified and combined the phi and psi angles and optimized prediction model for each ion ligand–binding residue.

A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform.

Background: Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. However, due to the low quality of cryo-EM density maps, many protein structures derived from cryo-EM contain outliers introduced during the modeling process. The current protein model validation system lacks identification features for cryo-EM proteins making it not enough to identify outliers in cryo-EM proteins. Methods: This study introduces an efficient unsupervised outlier detection model for validating protein models built from cryo-EM technique. The current model uses a high-resolution X-ray dataset (<1.5 Å) as the reference dataset. The distal block distance, side-chain length, phi, psi, and first chi angle of the residues in the reference dataset are collected and saved as a database of the histogram-based outlier score (HBOS). The HBOS value of the residues in target cryo-EM proteins can be read from this HBOS database. Results: Protein residues with a HBOS value greater than ten are labeled as outliers by default. Four datasets containing proteins derived from cryo-EM density maps were tested with this probabilistic anomaly detection model. Conclusions: According to the proposed model, a visualization assistant tool was designed for Chimera, a protein visualization platform.

Flexible and modular 3D-printed peptide models.

A flexible and modular peptide modeling set was designed using freely available software tools. The set consists of space-filling models of all 20 naturally occurring amino acid side chains and a modular kit for constructing peptides employing C-alpha carbons and amide bond groups. Connectors that allow free rotation about phi and psi angles on the peptide, together with explicit representation of peptide backbone hydrogen bond donor and acceptors allows for the construction of a wide range of protein secondary structure motifs. The space-filling side chain models highlight the steric, acid-base, and polarity of these groups. These models were printed using relatively affordable commercially available fused filament fabrication printers with minimal postprinting processing. Use of these models in student group activities focused on amino acids and protein secondary structural features is described. © 2019 International Union of Biochemistry and Molecular Biology, 47(4):432-437, 2019.

Modeling drug-drug interactions of AZD1208 with Vincristine and Daunorubicin on ligand-extrusion binding TMD-domains of multidrug resistance P-glycoprotein (ABCB1)

In the present study, the molecular docking mechanism based on pharmacodynamic interactions between the ligands AZD1208 and recognized chemotherapy agents (Vincristine and Daunorubicin) with human ATP-binding cassette (ABC) transporters (ABCB1) was investigated. For the first time, were combined an in silico approaches like molecular docking and ab initio computational simulation based on Density Functional Theory (DFT) to explain the drug-drug interaction mechanism of aforementioned chemotherapy ligands with the transmembrane ligand extrusion binding domains (TMDs) of ABCB1. In this regard, the theoretical pharmacodynamic interactions were characterized by using the Gibbs free energy (FEB, kcal/mol) from the best ABCB1-ligand docking complexes. The molecular docking results pointing that for the three chemotherapy ABCB1-ligand complexes are mainly based in non-covalent hydrophobic and hydrogen-bond interactions showing a similar toxicodynamic behavior in terms of strength of interaction (FEB, kcal/mol) and very close free binding energies when compared with the FEB-values of the ABCB1 specific-inhibitor (Rhodamine B) = −6.0 kcal/mol used as theoretical docking control to compare with FEB (AZD1208-ABCB1) ∼ FEB (Vincristine-ABCB1) ∼ FEB (Daunorubicin-ABCB1) −6.2 kcal/mol as average. Ramachandran plot suggests that the 3D-crystallographic structure from ABCB1 transporter can be efficiently-modeled with conformationally-favored Psi versus Phi dihedral angles for all key TMDs-residues. Though, the results of DFT-simulation corroborate the existence of drug-drug interaction between (AZD1208/Vincristine) > (AZD1208/Daunorubicin). These theoretical pieces of evidence have preclinical relevance potential in the design of the new drugs to understand the polypharmacology influence in the molecular mechanism of multiple-drugs resistance, contributing with a higher success in chemotherapy and prognosis of cancer patients.

Using Combined Features to Analyze Atomic Structures Derived from Cryo-EM Density Maps.

Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. Many atomic structures have been derived from cryo-EM density maps of about 3Å resolution. Side-chain conformations are well determined in density maps with super-resolutions such as 1-2Å. It is desirable to have a statistical method to detect anomalous side-chains without a super-resolution density map. In this study, we analyzed structures derived from X-ray density maps with higher than 1.5Å resolution and those from cryo-EM density maps with 2-4 Å and 4-6 Å resolutions respectively. We introduce a histogram-based outlier score (HBOS) for anomaly detection in protein models built from cryo-EM density maps. This method uses the statistics derived from X-ray dataset (<1.5Å) as the reference and combines five features involving the distal block distance, side-chain length, phi, psi, and first chi angle of the residue. Higher percentages of anomalies were observed in the cryo-EM models than in the super-resolution X-ray models. Lower percentages of anomalies were observed in cryo-EM models derived after January 2017 than those derived before 2017.