Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling

High Prevalence of Terbinafine Resistance Among Trichophyton mentagrophytes/T. interdigitale Species Complex, a Cross-Sectional Study from 2021 to 2022 in Northern Parts of Iran

Treatment-resistant dermatophytosis caused by the members of the Trichophyton mentagrophytes/Trichophyton interdigitale species group (TMTISG) is increasing worldwide. We aimed to determine the prevalence of TMTISG in patients with dermatophytosis in two centers from north of Iran and detect the possible mutations in the squalene epoxidase (SQLE) gene in relevant terbinafine (TRB) resistant pathogenic isolates. From November 2021 to December 2022, 1960 patients suspected to dermatophytosis and referred to two mycology referral laboratories in the north of Iran were included in the study. Identification of all dermatophyte isolates was confirmed by RFLP of rDNA internal transcribed spacer (ITS) regions. Antifungal susceptibility testing against five common antifungals using the CLSI-M38-A3 protocol was performed. The TMTISG isolates resistant to TRB, were further analyzed to determine the possible mutations in the SQLE gene. Totally, 647 cases (33%) were positive for dermatophytosis of which 280 cases (43.3%) were identified as members of TMTISG. These were more frequently isolated from tinea corporis 131 (44.56%) and tinea cruris 116 (39.46%). Of 280 TMTISG isolates, 40 (14.3%) were resistant to TRB (MIC ≥ 4 µg/mL), all found to be T. indotineae in ITS sequencing. In SQLE sequencing 34 (85%) of TRB-resistant isolates had coincident mutations of Phe397Leu and Ala448Thr whereas four and two isolates had single mutations of Phe397Leu and Leu393Ser, respectively. Overall, the resistance of Iranian TMTISG isolates to TRB greatly occurred by a mutation of Phe397Leu in the SQLE gene as alone or in combination with Ala448Thr. Nevertheless, for the occurrence of in vitro resistance, only the presence of Phe397Leu mutation seems to be decisive.

Structure of the dopamine D3 receptor bound to a bitopic agonist reveals a new specificity site in an expanded allosteric pocket

Although aminergic GPCRs are the target for ~25% of approved drugs, developing subtype selective drugs is a major challenge due to the high sequence conservation at their orthosteric binding site. Bitopic ligands are covalently joined orthosteric and allosteric pharmacophores with the potential to boost receptor selectivity, driven by the binding of the secondary pharmacophore to non-conserved regions of the receptor. Although bitopic ligands have great potential to improve current medications by reducing off-target side effects, the lack of structural information on their binding mode impedes rational design. Here we determine the cryo-EM structure of the hD3R coupled to a GO heterotrimer and bound to the D3R selective bitopic agonist FOB02-04A. Structural, functional and computational analyses provide new insights into its binding mode and point to a new TM2-ECL1-TM1 region, which requires the N-terminal ordering of TM1, as a major determinant of subtype selectivity in aminergic GPCRs. This region is underexploited in drug development, expands the established secondary binding pocket in aminergic GPCRs and could potentially be used to design novel and subtype selective drugs.

The Influence of KE and EW Dipeptides in the Composition of the Thymalin Drug on Gene Expression and Protein Synthesis Involved in the Pathogenesis of COVID-19

Thymalin is an immunomodulatory drug containing a polypeptide extract of thymus that has demonstrated efficacy in the therapy of acute respiratory distress syndrome and chronic obstructive pulmonary disease, as well as in complex therapy related to severe COVID-19 in middle-aged and elderly patients.. KE and EW dipeptides are active substances of Thymalin. There is evidence that KE stimulates cellular immunity and nonspecific resistance in organisms, exerting an activating effect on macrophages, blood lymphocytes, thymocytes, and neutrophils, while EW reduces angiotensin-induced vasoconstriction and preserves endothelium-dependent vascular relaxation by inhibiting ACE2, the target protein of SARS-CoV-2. However, the mechanism of the immunomodulatory action of Thymalin, KE, and EW during COVID-19 remains unclear. To identify the potential mechanism of action underlying the immunomodulatory activity of Thymalin and its active components, EW and KE dipeptides, we assessed inflammatory response in the context of COVID-19. Interactions between EW and KE dipeptides and double-stranded DNA (dsDNA) were investigated by molecular modeling and docking using ICM-Pro. Analysis of the possible effect of EW and KE dipeptides on gene expression and protein synthesis involved in the pathogenesis of COVID-19 was conducted through the use of bioinformatics methods, including a search for promoter sequences in the Eukaryotic Promoter Database, the determination of genes associated with the development of COVID-19 using the PathCards database of human biological pathways (pathway unification database), identification of the relationship between proteins through cluster analysis in the STRING database ('Search Tool for Retrieval of Interacting Genes/Proteins'), and assessment of the functional enrichment of protein-protein interaction (PPI) using the terms of gene ontology (GO) and the Markov cluster algorithm (MCL). After that, in vitro studying of a lipopolysaccharide (LPS)-induced model of inflammation using human peripheral blood mononuclear cells was performed. ELISA was applied to assess the level of cytokines (IL-1β, IL-6, TNFα) in the supernatant of cells with or without the impact of EW and KE peptides. Blood samples were obtained from four donors; for each cytokine, ELISA was performed 2-4 times, with two parallel experimental or control samples for each experiment (experiments to assess the effects of peptides on LPS-stimulated cells were repeated four times, while additional experiments with unstimulated cells were performed two times). Using molecular docking, GGAG was found to be the best dsDNA sequence in the classical B-form for binding the EW dipeptide, while GCGC is the preferred dsDNA sequence in the curved nucleosomal form for the KE dipeptide. Cluster analysis revealed that potential target genes for the EW and KE peptides encode the AKT1 and AKT2 proteins involved in the development of the cytokine storm. The specific targets for the EW peptide are the ACE2 and CYSLTR1 genes, and specific target for the KE peptide is the CHUK gene. Protein products of the ACE2, CYSLTR1, and CHUK genes are functionally associated with IL-1β, IL-6, TNF-α, IL-4, and IL-10 cytokines. An in vitro model of an inflammatory reaction demonstrated that Thymalin and EW and KE dipeptides reduced the synthesis of IL-1β, IL-6, and TNF-α cytokines in human peripheral blood mononuclear cells by 1.4-6.0 times. The immunomodulatory effect of Thymalin under the inflammatory response conditions in COVID-19 is based on the potential ability of its active components, EW and KE dipeptides, to regulate protein synthesis involved in the development of the cytokine storm.

Ligand and G-protein selectivity in the κ-opioid receptor

The κ-opioid receptor (KOR) represents a highly desirable therapeutic target for treating not only pain but also addiction and affective disorders1. However, the development of KOR analgesics has been hindered by the associated hallucinogenic side effects2. The initiation of KOR signalling requires the Gi/o-family proteins including the conventional (Gi1, Gi2, Gi3, GoA and GoB) and nonconventional (Gz and Gg) subtypes. How hallucinogens exert their actions through KOR and how KOR determines G-protein subtype selectivity are not well understood. Here we determined the active-state structures of KOR in a complex with multiple G-protein heterotrimers-Gi1, GoA, Gz and Gg-using cryo-electron microscopy. The KOR-G-protein complexes are bound to hallucinogenic salvinorins or highly selective KOR agonists. Comparisons of these structures reveal molecular determinants critical for KOR-G-protein interactions as well as key elements governing Gi/o-family subtype selectivity and KOR ligand selectivity. Furthermore, the four G-protein subtypes display an intrinsically different binding affinity and allosteric activity on agonist binding at KOR. These results provide insights into the actions of opioids and G-protein-coupling specificity at KOR and establish a foundation to examine the therapeutic potential of pathway-selective agonists of KOR.

Allosteric regulation of CAD modulates de novo pyrimidine synthesis during the cell cycle

Metabolism is a fundamental cellular process that is coordinated with cell cycle progression. Despite this association, a mechanistic understanding of cell cycle phase-dependent metabolic pathway regulation remains elusive. Here we report the mechanism by which human de novo pyrimidine biosynthesis is allosterically regulated during the cell cycle. Combining traditional synchronization methods and metabolomics, we characterize metabolites by their accumulation pattern during cell cycle phases and identify cell cycle phase-dependent regulation of carbamoyl-phosphate synthetase 2, aspartate transcarbamylase and dihydroorotase (CAD), the first, rate-limiting enzyme in de novo pyrimidine biosynthesis. Through systematic mutational scanning and structural modelling, we find allostery as a major regulatory mechanism that controls the activity change of CAD during the cell cycle. Specifically, we report evidence of two Animalia-specific loops in the CAD allosteric domain that involve sensing and binding of uridine 5'-triphosphate, a CAD allosteric inhibitor. Based on homology with a mitochondrial carbamoyl-phosphate synthetase homologue, we identify a critical role for a signal transmission loop in regulating the formation of a substrate channel, thereby controlling CAD activity.

Molecular Docking and Dynamic Simulation Revealed the Potential Inhibitory Activity of Opioid Compounds Targeting the Main Protease of SARS-CoV-2

Opioids are a class of chemicals, naturally occurring in the opium poppy plant, and act on the brain to cause a range of impacts, notably analgesic and anti-inflammatory actions. Moreover, an overview was taken in consideration for SARS-CoV-2 incidence and complications, as well as the medicinal uses of opioids were discussed being a safe analgesic and anti-inflammatory drug in a specific dose. Also, our article focused on utilization of opioids in the medication of SARS-CoV-2. Therefore, the major objective of this study was to investigate the antiviral effect of opioids throughout an in silico study by molecular docking study to fifteen opioid compounds against SARS-CoV-2 main protease (PDB ID 6LU7, M^pro). The docking results revealed that opioid complexes potentially inhibit the M^pro active site and exhibiting binding energy (-11.0 kcal/mol), which is comparably higher than the ligand. Furthermore, ADMET prediction indicated that all the tested compounds have good oral absorption and bioavailability and can transport via biological membranes. Finally, M^pro-pholcodine complex was subjected to five MD (RMSD, RMSF, SASA, Rg, and hydrogen bonding) and two MM-PBSA, and conformational change studies, for 100 ns, confirmed the stability of pholcodine, as a representative example, inside the active site of M^pro.

Metal complexes of thiosemicarbazones derived by 2-quinolones with Cu(I), Cu(II) and Ni(II); Identification by NMR, IR, ESI mass spectra and in silico approach as potential tools against SARS-CoV-2

Substituted thiosemicarbazones derived by 2-quinolone were synthesized to investigate their complexation capability towards Cu(I), Cu(II) and Ni(II) salts. The structure of the complexes was established by ESI, IR and NMR spectra in addition to elemental analyses. Monodetate Cu(I) quinoloyl-substituted ligands were observed, whereas Ni(II) and Cu(II) formed bidentate-thiosemicarbazone derived by 2-quinolones. Subsequently, molecular docking was used to evaluate each analog's binding affinity as well as the inhibition constant (ki) to RdRp complex of SARS-CoV-2. Docking results supported the ability of the tested complexes that potentially inhibit the RdRp of SARSCov-2 show binding energy higher than their corresponding ligands. Additionally, ADMET prediction revealed that some compounds stratify to Lipinski's rule, indicating a good oral absorption, high bioavailability good permeability, and transport via biological membranes. Therefore, these metals-based complexes are suggested to be potentially good candidates as anti-covid agents.

Evaluation of the Binding Relationship of the RdRp Enzyme to Novel Thiazole/Acid Hydrazone Hybrids Obtainable through Green Synthetic Procedure

The viral RNA-dependent RNA polymerase (RdRp) complex is used by SARS-CoV-2 for genome replication and transcription, making RdRp an interesting target for developing the antiviral treatment. Hence the current work is concerned with the green synthesis, characterization and docking study with the RdRp enzyme of the series of novel and diverse hydrazones and pyrazoles. 4-Methyl-2-(2-(1-phenylethylidene)hydrazineyl)thiazole-5-carbohydrazide was prepared and then condensed with different carbonyl compounds (aldehydes and ketones either carbocyclic aromatic or heterocyclic) afforded the corresponding hydrazide-hydrazones. The combination of the acid hydrazide with bifunctional reagents such as acetylacetone, β-ketoesters (ethyl acetoacetate and ethyl benzoylacetate) resulted in the formation of pyrazole derivatives. The synthesized compounds were all obtained through grinding method using drops of AcOH. Various analytical and spectral analyses were used to determine the structures of the prepared compounds. Molecular Operating Environment (MOE^®) version 2014.09 was used to estimate interactions between the prepared thiazole/hydrazone hybrids and RdRp obtained from the protein data bank (PDB: 7bv2) using enzyme-ligand docking for all synthesized derivatives and Remdesivir as a reference. Docking results with the RdRp enzyme revealed that the majority of the investigated drugs bind well to the enzyme via various types of interactions in comparison with the reference drug.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson-Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

A de novo protein structure prediction by iterative partition sampling, topology adjustment and residue-level distance deviation optimization

MOTIVATION

With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method.

RESULTS

In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Finally, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta.

AVAILABILITYAND IMPLEMENTATION

The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Riproximin Exhibits Diversity in Sugar Binding, and Modulates some Metastasis-Related Proteins with Lectin like Properties in Pancreatic Ductal Adenocarcinoma

Riproximin (Rpx) is a type II ribosome-inactivating protein with specific anti-proliferative activity. It was purified from Ximenia americana by affinity chromatography using a resin coupled with lactosyl residues. The same technique facilitated isolation of proteins with lectin-like properties from human Suit2-007 and rat ASML pancreatic cancer cells, which were termed lactosyl-sepharose binding proteins (LSBPs). The role of these proteins in cancer progression was investigated at mRNA level using chip array data of Suit2-007 and ASML cells re-isolated from nude rats. These data compared significant mRNA expression changes when relating primary (pancreas) and metastatic (liver) sites following orthotopic and intraportal implantation of Pancreatic Ductal Adenocarcinoma (PDAC) cells, respectively. The affinity of Rpx to 13 simple sugar structures was modeled by docking experiments, the ranking of which was principally confirmed by NMR-spectroscopy. In addition, Rpx and LSBPs were evaluated for anti-proliferative activity and their cellular uptake was assessed by fluorescence microscopy. From 13 monosaccharides evaluated, open-chain rhamnose, β-d-galactose, and α-l-galactopyranose showed the highest affinities for site 1 of Rpx’s B-chain. NMR evaluation yielded a similar ranking, as galactose was among the best binders. Both, Rpx and LSBPs reduced cell proliferation in vitro, but their anti-proliferative effects were decreased by 15–20% in the presence of galactose. The program “Ingenuity Pathway Analysis” identified 2,415 genes showing significantly modulated mRNA expression following exposure of Suit2-007 cells to Rpx in vitro. These genes were then matched to those 1,639 genes, which were significantly modulated in the rat model when comparing primary and metastatic growth of Suit2-007 cells. In this overlap analysis, LSBP genes were considered separately. The potential suitability of Rpx for treating metastatic Suit2-007 PDAC cells was reflected by those genes, which were modulated by Rpx in a way opposite to that observed in cancer progression. Remarkably, these were 14% of all genes modulated during cancer progression, but 71% of the respective LSBP gene subgroup. Based on these findings, we predict that Rpx has the potential to treat PDAC metastasis by modulating genes involved in metastatic progression, especially by targeting LSBPs.

CGLFold: a contact-assisted de novo protein structure prediction using global exploration and loop perturbation sampling algorithm

MOTIVATION

Regions that connect secondary structure elements in a protein are known as loops, whose slight change will produce dramatic effect on the entire topology. This study investigates whether the accuracy of protein structure prediction can be improved using a loop-specific sampling strategy.

RESULTS

A novel de novo protein structure prediction method that combines global exploration and loop perturbation is proposed in this study. In the global exploration phase, the fragment recombination and assembly are used to explore the massive conformational space and generate native-like topology. In the loop perturbation phase, a loop-specific local perturbation model is designed to improve the accuracy of the conformation and is solved by differential evolution algorithm. These two phases enable a cooperation between global exploration and local exploitation. The filtered contact information is used to construct the conformation selection model for guiding the sampling. The proposed CGLFold is tested on 145 benchmark proteins, 14 free modeling (FM) targets of CASP13 and 29 FM targets of CASP12. The experimental results show that the loop-specific local perturbation can increase the structure diversity and success rate of conformational update and gradually improve conformation accuracy. CGLFold obtains template modeling score ≥ 0.5 models on 95 standard test proteins, 7 FM targets of CASP13 and 9 FM targets of CASP12.

AVAILABILITY AND IMPLEMENTATION

The source code and executable versions are freely available at https://github.com/iobio-zjut/CGLFold.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-performance transformation of protein structure representation from internal to Cartesian coordinates

We present a highly parallel algorithm to convert internal coordinates of a polymeric molecule into Cartesian coordinates. Traditionally, converting the structures of polymers (e.g., proteins) from internal to Cartesian coordinates has been performed serially, due to an inherent linear dependency along the polymer chain. We show this dependency can be removed using a tree-based concatenation of coordinate transforms between segments, and then parallelized efficiently on graphics processing units (GPUs). The conversion algorithm is applicable to protein engineering and fitting protein structures to experimental data, and we observe an order of magnitude speedup using parallel processing on a GPU compared to serial execution on a CPU.

Systematic search for structural motifs of peptide binding to double-stranded DNA

A large variety of short biologically active peptides possesses antioxidant, antibacterial, antitumour, anti-ageing and anti-inflammatory activity, involved in the regulation of neuro-immuno-endocrine system functions, cell apoptosis, proliferation and differentiation. Therefore, the mechanisms of their biological activity are attracting increasing attention not only in modern molecular biology, biochemistry and biophysics, but also in pharmacology and medicine. In this work, we systematically analysed the ability of dipeptides (all possible combinations of the 20 standard amino acids) to bind all possible combinations of tetra-nucleotides in the central part of dsDNA in the classic B-form using molecular docking and molecular dynamics. The vast majority of the dipeptides were found to be unable to bind dsDNA. However, we were able to identify 57 low-energy dipeptide complexes with peptide-dsDNA possessing high selectivity for DNA binding. The analysis of the dsDNA complexes with dipeptides with free and blocked N- and C-terminus showed that selective peptide binding to dsDNA can increase dramatically with the peptide length.

Crosslinking-guided geometry of a complete CXC receptor-chemokine complex and the basis of chemokine subfamily selectivity

Chemokines and their receptors are orchestrators of cell migration in humans. Because dysregulation of the receptor-chemokine system leads to inflammation and cancer, both chemokines and receptors are highly sought therapeutic targets. Yet one of the barriers for their therapeutic targeting is the limited understanding of the structural principles behind receptor-chemokine recognition and selectivity. The existing structures do not include CXC subfamily complexes and lack information about the receptor distal N-termini, despite the importance of the latter in signaling, regulation, and bias. Here, we report the discovery of the geometry of the complex between full-length CXCR4, a prototypical CXC receptor and driver of cancer metastasis, and its endogenous ligand CXCL12. By comprehensive disulfide cross-linking, we establish the existence and the structure of a novel interface between the CXCR4 distal N-terminus and CXCL12 β1-strand, while also recapitulating earlier findings from nuclear magnetic resonance, modeling and crystallography of homologous receptors. A cross-linking-informed high-resolution model of the CXCR4-CXCL12 complex pinpoints the interaction determinants and reveals the occupancy of the receptor major subpocket by the CXCL12 proximal N terminus. This newly found positioning of the chemokine proximal N-terminus provides a structural explanation of CXC receptor-chemokine selectivity against other subfamilies. Our findings challenge the traditional two-site understanding of receptor-chemokine recognition, suggest the possibility of new affinity and signaling determinants, and fill a critical void on the structural map of an important class of therapeutic targets. These results will aid the rational design of selective chemokine-receptor targeting small molecules and biologics with novel pharmacology.

Understanding the potency of malarial ligand (D44) in plasmodium FKBP35 and modelled halogen atom (Br, Cl, F) functional groups

The present study clearly depicts the understanding of the D44 in Plasmodium FKBP35 around the hinge region. To analyse the binding stability of D44 ligand and to understand the role of halogen bond, hydrogen bond interaction formed between the hinge region amino acids: Isoleucine (Ile74), Phenylalanine (Phe54), Aspartic acid (Asp55) Phenylalanine (Phe64),Tyrosine (Tyr100), Tryptophan (TRP 77) and ligand D44 was portrayed specifically through interaction energy calculations at HF, M062X, MP2 level of theories for different basis set (6-311G**, 6-31+G*, LANL2DZ). The investigation will provide an apparent picture regarding the non-covalent interaction that hold the contact of ligand and amino acids in the hinge region and the implication of modelled functional groups (Br, Cl, F, OSO and NH₂) on ligand, which will help chemist in synthesizing new novel ligands. HOMO, LUMO chart calculated for D44 ligands reveals graphic illustration of orbital's that stimulate for contact. The aim and natural bond orbital analysis identified key contribution of individual hydrogen/halogen bonds that contribute for the binding strength through stabilization energy, ρ and ∇²ρ values. Overall this study finds out that the Stability of D44 in Plasmodium FKBP35 was enhanced by the Halogen atom (Br, Cl, F) functional groups; which provide an innovative pathway for the selection of functional groups that opt for the hinge region side chains on the ligand.

Advances in Docking

BACKGROUND

Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for finding the most active compounds for the given target protein. This short review aims to give a concise description of distinctive features of docking programs focusing on computation methods and approximations influencing their accuracy.

METHODS

This review is based on the peer-reviewed research literature including author's own publications. The main features of several representative docking programs are briefly described focusing on their characteristics influencing docking accuracy: force fields, energy calculations, solvent models, algorithms of the best ligand pose search, global and local optimizations, ligand and target protein flexibility, and the simplifications made for the docking accelerating. Apart from other recent reviews focused mainly on the performance of different docking programs, in this work, an attempt is made to extract the most important functional characteristics defining the docking accuracy. Also a roadmap for increasing the docking accuracy is proposed. This is based on the new generation of docking programs which have been realized recently. These programs and respective new global optimization algorithms are described shortly.

RESULTS

Several popular conventional docking programs are considered. Their search of the best ligand pose is based explicitly or implicitly on the global optimization problem. Several algorithms are used to solve this problem, and among them, the heuristic genetic algorithm is distinguished by its popularity and an elaborate design. All conventional docking programs for their acceleration use the preliminary calculated grids of protein-ligand interaction potentials or preferable points of protein and ligand conjugation. These approaches and commonly used fitting parameters restrict strongly the docking accuracy. Solvent is considered in exceedingly simplified approaches in the course of the global optimization and the search for the best ligand poses. More accurate approaches on the base of implicit solvent models are used frequently for more careful binding energy calculations after docking. The new generation of docking programs are developed recently. They find the spectrum of low energy minima of a protein-ligand complex including the global minimum. These programs should be more accurate because they do not use a preliminary calculated grid of protein-ligand interaction potentials and other simplifications, the energy of any conformation of the molecular system is calculated in the frame of a given force field and there are no fitting parameters. A new docking algorithm is developed and fulfilled specially for the new docking programs. This algorithm allows docking a flexible ligand into a flexible protein with several dozen mobile atoms on the base of the global energy minimum search. Such docking results in improving the accuracy of ligand positioning in the docking process. The adequate choice of the method of molecular energy calculations also results in the better docking positioning accuracy. An advancement in the application of quantum chemistry methods to docking and scoring is revealed.

CONCLUSION

The findings of this review confirm the great demand in docking programs for discovery of new medicine substances with the help of molecular modeling. New trends in docking programs design are revealed. These trends are focused on the increase of the docking accuracy at the expense of more accurate molecular energy calculations without any fitting parameters, including quantum-chemical methods and implicit solvent models, and by using new global optimization algorithms which make it possible to treat flexibility of ligands and mobility of protein atoms simultaneously. Finally, it is shown that all the necessary prerequisites for increasing the docking accuracy can be accomplished in practice.

Genetic analyses uncover pleiotropic compensatory roles for Drosophila Nucleobindin-1 in inositol trisphosphate-mediated intracellular calcium homeostasis

Nucleobindin-1 is an EF-hand calcium-binding protein with a distinctive profile, predominantly localized to the Golgi in insect and wide-ranging vertebrate cell types, alike. Its putative involvements in intracellular calcium (Ca²⁺) homeostasis have never been phenotypically characterized in any model organism. We have analyzed an adult-viable mutant that completely disrupts the G protein α-subunit binding and activating (GBA) motif of Drosophila Nucleobindin-1 (dmNUCB1). Such disruption does not manifest any obvious fitness-related, morphological/developmental, or behavioral abnormalities. A single copy of this mutation or the knockdown of dmnucb1 in restricted sets of cells variously rescues pleiotropic mutant phenotypes arising from impaired inositol 1,4,5-trisphosphate receptor (IP3R) activity (in turn depleting cytoplasmic Ca²⁺ levels across diverse tissue types). Additionally, altered dmNUCB1 expression or function considerably reverses lifespan and mobility improvements effected by IP3R mutants, in a Drosophila model of amyotrophic lateral sclerosis. Homology modeling-based analyses further predict a high degree of conformational conservation in Drosophila, of biochemically validated structural determinants in the GBA motif that specify in vertebrates, the unconventional Ca²⁺-regulated interaction of NUCB1 with Gαi subunits. The broad implications of our findings are hypothetically discussed, regarding potential roles for NUCB1 in GBA-mediated, Golgi-associated Ca²⁺ signaling, in health and disease.

New Deep Learning Methods for Protein Loop Modeling

Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.

Novel Point Mutations in cyp51A and cyp51B Genes Associated with Itraconazole and Posaconazole Resistance in Aspergillus clavatus Isolates

Aspergillus clavatus is a common environmental species known to cause occupational allergic disease in grain handlers. We have recently observed azole-resistant isolates of this fungus as a cause of onychomycosis. To further characterize the cause of resistance, the genes encoding 14 α-sterol demethylase enzyme (cyp51A and cyp51B) were characterized and analyzed in 9 ITC-susceptible isolates and 6 isolates with high minimum inhibitory concentrations (MICs) of clinical (nail and sputum) and environmental A. clavatus strains. We found that six isolates with itraconazole MIC >16 mg/L demonstrated nonsynonymous mutations, including V51I, L378P, E483K, and E506G, and synonymous mutations, including F53F, A186A, Q276Q, and H359H. Moreover, P486S was detected in five strains with ITR MIC >16 mg/L. One mutation, F324S, was detected in an isolate with posaconazole MIC >16 mg/L. The effect of E483K and P486S mutations of CYP51A on azole resistance was further investigated using homology modeling and molecular dynamics. We found that E483K and P486S mutations were located near the ligand access channel of CYP51A that could partly lead to narrowing the entry of the ligand access channels. Therefore, we concluded that E483K and P486S mutations may potentially contribute to the limited access of inhibitors to the binding pocket and therefore confer resistance to azole agents.

Substrate Specificity Analysis of Dihydrofolate/Dihydromethanopterin Reductase Homologs in Methylotrophic α-Proteobacteria

Methane-producing archaea and methylotrophic bacteria use tetrahydromethanopterin (H₄MPT) and/or tetrahydrofolate (H₄F) as coenzymes in one-carbon (C1) transfer pathways. The α-proteobacterium Methylobacterium extorquens AM1 contains a dihydromethanopterin reductase (DmrA) and two annotated dihydrofolate reductases (DfrA and DfrB). DmrA has been shown to catalyze the final step of H₄MPT biosynthesis; however, the functions of DfrA and DfrB have not been examined biochemically. Moreover, sequence alignment (BLAST) searches have recognized scores of proteins that share up to 99% identity with DmrA but are annotated as diacylglycerol kinases (DAGK). In this work, we used bioinformatics and enzyme assays to provide insight into the phylogeny and substrate specificity of selected Dfr and DmrA homologs. In a phylogenetic tree, DmrA and homologs annotated as DAGKs grouped together in one clade. Purified histidine-tagged versions of the annotated DAGKs from Hyphomicrobium nitrativorans and M. nodulans (respectively, sharing 69 and 84% identity with DmrA) showed only low activity in phosphorylating 1,2-dihexanoyl-sn-glycerol when compared with a commercial DAGK from Escherichia coli. However, the annotated DAGKs successfully reduced a dihydromethanopterin analog (dihydrosarcinapterin, H₂SPT) with kinetic values similar to those determined for M. extorquens AM1 DmrA. DfrA and DfrB showed little or no ability to reduce H₂SPT under the conditions studied; however, both catalyzed the NADPH-dependent reduction of dihydrofolate. These results provide the first evidence that DfrA and DfrB function as authentic dihydrofolate reductases, while DAGKs with greater than 69% identity to DmrA may be misannotated and are likely to function in H₄MPT biosynthesis.

Protein-RNA Docking Using ICM

Protein-RNA interactions play an important role in many biological processes. Computational methods such as docking have been developed to complement existing biophysical and structural biology techniques. Computational prediction of protein-RNA complex structures includes two steps: generating candidate structures from the individual protein and RNA parts and scoring the generated poses to pick out the correct one. In this work, we considered three recently developed data sets of protein-RNA complexes to evaluate and improve the performance of the FFT-based rigid-body docking algorithm implemented in the ICM package. An electrostatic term describing interactions between negatively charged phosphate groups and positively charged protein residues was added to the energy function used during the docking step to take into account the greater role that electrostatic interactions play in protein-RNA complexes. Next, the docking results were used to optimize a scoring function including van der Waals, electrostatic, and solvation terms. This optimization yielded a much smaller weight for the solvation term indicating that solvation energy may be less important for the scoring of protein-RNA structures. Rescoring of the generated poses with the new scoring function led to much higher success rates, while pose clustering by contact fingerprints produced further improvements, achieving a success rate of 0.66 for the top 100 structures.

Molecular Modeling and Structural Stability of Wild-Type and Mutant CYP51 from Leishmania major: In Vitro and In Silico Analysis of a Laboratory Strain

Cutaneous leishmaniasis is a neglected tropical disease and a major public health in the most countries. Leishmania major is the most common cause of cutaneous leishmaniasis. In the Leishmania parasites, sterol 14α-demethylase (CYP51), which is involved in the biosynthesis of sterols, has been identified as an attractive target for development of new therapeutic agents. In this study, the sequence and structure of CYP51 in a laboratory strain (MRHO/IR/75/ER) of L. major were determined and compared to the wild-type strain. The results showed 19 mutations including seven non-synonymous and 12 synonymous ones in the CYP51 sequence of strain MRHO/IR/75/ER. Importantly, an arginine to lysine substitution at position of 474 resulted in destabilization of CYP51 (ΔΔG = 1.17 kcal/mol) in the laboratory strain; however, when the overall effects of all substitutions were evaluated by 100 ns molecular dynamics simulation, the final structure did not show any significant changes (p-value < 0.05) in stability parameter of the strain MRHO/IR/75/ER compared to the wild-type protein. The energy level for the CYP51 of wild-type and MRHO/IR/75/ER strain were −40,027.1 and −39,706.48 Kcal/mol respectively. The overall Root-mean-square deviation (RMSD) deviation between two proteins was less than 1 Å throughout the simulation and Root-mean-square fluctuation (RMSF) plot also showed no substantial differences between amino acids fluctuation of the both protein. The results also showed that, these mutations were located on the protein periphery that neither interferes with protein folding nor with substrate/inhibitor binding. Therefore, L. major strain MRHO/IR/75/ER is suggested as a suitable laboratory model for studying biological role of CYP51 and inhibitory effects of sterol 14α-demethylase inhibitors.

Determinants of HIV-1 CD4-Independent Brain Adaptation

BACKGROUND

HIV-1 is known to adapt to the local environment in its usage of receptors, and it can become CD4 independent in the brain where the receptor is scarce. This adaptation is through amino acid variations, but the patterns of such variation are not yet well understood. Given that infection of long-lived CD4-low and CD4-negative cells in anatomical compartments such as the brain expands cell tropism in vivo and may serve as potential viral reservoirs that pose challenge for HIV eradication, understanding the evolution to CD4 independence and envelope conformation associated with infection in the absence of CD4 will not only broaden our insights into HIV pathogenesis but may guide functional cure strategies as well.

METHODS

We characterize, by site-directed mutagenesis, neutralization assay, and structural analysis, a pair of CD4-dependent (cl2) and CD4-independent (cl20) envelopes concurrently isolated from the cerebral spinal fluid of an SHIV-infected macaque with neurological AIDS and with minimum sequence differences.

RESULTS

Residues different between cl2 and cl20 are mapped to the V1V2 and surrounding regions. Mutations of these residues in cl2 increased its CD4 independence in infection, and the effects are cumulative and likely structural.

CONCLUSIONS

Our data suggested that the determinants of CD4 independence in vivo mapped principally to V1V2 of gp120 that can destabilize the apex of the envelope spike, with an additional change in V4 that abrogated a potential N-linked glycan to facilitate movement of the V1V2 domain and further expose the coreceptor-binding site.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs

OBJECTIVE

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin).

MATERIALS AND METHODS

During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server.

RESULTS

Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively.

CONCLUSION

Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field.

RCD+: Fast loop modeling server

Modeling loops is a critical and challenging step in protein modeling and prediction. We have developed a quick online service (http://rcd.chaconlab.org) for ab initio loop modeling combining a coarse-grained conformational search with a full-atom refinement. Our original Random Coordinate Descent (RCD) loop closure algorithm has been greatly improved to enrich the sampling distribution towards near-native conformations. These improvements include a new workflow optimization, MPI-parallelization and fast backbone angle sampling based on neighbor-dependent Ramachandran probability distributions. The server starts by efficiently searching the vast conformational space from only the loop sequence information and the environment atomic coordinates. The generated closed loop models are subsequently ranked using a fast distance-orientation dependent energy filter. Top ranked loops are refined with the Rosetta energy function to obtain accurate all-atom predictions that can be interactively inspected in an user-friendly web interface. Using standard benchmarks, the average root mean squared deviation (RMSD) is 0.8 and 1.4 Å for 8 and 12 residues loops, respectively, in the challenging modeling scenario in where the side chains of the loop environment are fully remodeled. These results are not only very competitive compared to those obtained with public state of the art methods, but also they are obtained ∼10-fold faster.

Toward the identification of molecular cogs

Computer simulations of molecular systems allow determination of microscopic interactions between individual atoms or groups of atoms, as well as studies of intramolecular motions. Nevertheless, description of structural transformations at the mezoscopic level and identification of causal relations associated with these transformations is very difficult. Structural and functional properties are related to free energy changes. Therefore, to better understand structural and functional properties of molecular systems, it is required to deepen our knowledge of free energy contributions arising from molecular subsystems in the course of structural transformations. The method presented in this work quantifies the energetic contribution of each pair of atoms to the total free energy change along a given collective variable. Next, with the help of a genetic clustering algorithm, the method proposes a division of the system into two groups of atoms referred to as molecular cogs. Atoms which cooperate to push the system forward along a collective variable are referred to as forward cogs, and those which work in the opposite direction as reverse cogs. The procedure was tested on several small molecules for which the genetic clustering algorithm successfully found optimal partitionings into molecular cogs. The primary result of the method is a plot depicting the energetic contributions of the identified molecular cogs to the total Potential of Mean Force (PMF) change. Case-studies presented in this work should help better understand the implications of our approach, and were intended to pave the way to a future, publicly available implementation.

Overcoming potential energy distortions in constrained internal coordinate molecular dynamics simulations

The Internal Coordinate Molecular Dynamics (ICMD) method is an attractive molecular dynamics (MD) method for studying the dynamics of bonded systems such as proteins and polymers. It offers a simple venue for coarsening the dynamics model of a system at multiple hierarchical levels. For example, large scale protein dynamics can be studied using torsional dynamics, where large domains or helical structures can be treated as rigid bodies and the loops connecting them as flexible torsions. ICMD with such a dynamic model of the protein, combined with enhanced conformational sampling method such as temperature replica exchange, allows the sampling of large scale domain motion involving high energy barrier transitions. Once these large scale conformational transitions are sampled, all-torsion, or even all-atom, MD simulations can be carried out for the low energy conformations sampled via coarse grained ICMD to calculate the energetics of distinct conformations. Such hierarchical MD simulations can be carried out with standard all-atom forcefields without the need for compromising on the accuracy of the forces. Using constraints to treat bond lengths and bond angles as rigid can, however, distort the potential energy landscape of the system and reduce the number of dihedral transitions as well as conformational sampling. We present here a two-part solution to overcome such distortions of the potential energy landscape with ICMD models. To alleviate the intrinsic distortion that stems from the reduced phase space in torsional MD, we use the Fixman compensating potential. To additionally alleviate the extrinsic distortion that arises from the coupling between the dihedral angles and bond angles within a force field, we propose a hybrid ICMD method that allows the selective relaxing of bond angles. This hybrid ICMD method bridges the gap between all-atom MD and torsional MD. We demonstrate with examples that these methods together offer a solution to eliminate the potential energy distortions encountered in constrained ICMD simulations of peptide molecules.

Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser

G-protein-coupled receptors (GPCRs) signal primarily through G proteins or arrestins. Arrestin binding to GPCRs blocks G protein interaction and redirects signalling to numerous G-protein-independent pathways. Here we report the crystal structure of a constitutively active form of human rhodopsin bound to a pre-activated form of the mouse visual arrestin, determined by serial femtosecond X-ray laser crystallography. Together with extensive biochemical and mutagenesis data, the structure reveals an overall architecture of the rhodopsin-arrestin assembly in which rhodopsin uses distinct structural elements, including transmembrane helix 7 and helix 8, to recruit arrestin. Correspondingly, arrestin adopts the pre-activated conformation, with a ∼20° rotation between the amino and carboxy domains, which opens up a cleft in arrestin to accommodate a short helix formed by the second intracellular loop of rhodopsin. This structure provides a basis for understanding GPCR-mediated arrestin-biased signalling and demonstrates the power of X-ray lasers for advancing the frontiers of structural biology.

All-Atom Internal Coordinate Mechanics (ICM) Force Field for Hexopyranoses and Glycoproteins

We present an extension of the all-atom internal-coordinate force field, ICMFF, that allows for simulation of heterogeneous systems including hexopyranose saccharides and glycan chains in addition to proteins. A library of standard glycan geometries containing α- and β-anomers of the most common hexapyranoses, i.e., d-galactose, d-glucose, d-mannose, d-xylose, l-fucose, N-acetylglucosamine, N-acetylgalactosamine, sialic, and glucuronic acids, is created based on the analysis of the saccharide structures reported in the Cambridge Structural Database. The new force field parameters include molecular electrostatic potential-derived partial atomic charges and the torsional parameters derived from quantum mechanical data for a collection of minimal molecular fragments and related molecules. The ϕ/ψ torsional parameters for different types of glycosidic linkages are developed using model compounds containing the key atoms in the full carbohydrates, i.e., glycosidic-linked tetrahydropyran-cyclohexane dimers. Target data for parameter optimization include two-dimensional energy surfaces corresponding to the ϕ/ψ glycosidic dihedral angles in the disaccharide analogues, as determined by quantum mechanical MP2/6-31G** single-point energies on HF/6-31G** optimized structures. To achieve better agreement with the observed geometries of glycosidic linkages, the bond angles at the O-linkage atoms are added to the internal variable set and the corresponding bond bending energy term is parametrized using quantum mechanical data. The resulting force field is validated on glycan chains of 1-12 residues from a set of high-resolution X-ray glycoprotein structures based on heavy atom root-mean-square deviations of the lowest-energy glycan conformations generated by the biased probability Monte Carlo (BPMC) molecular mechanics simulations from the native structures. The appropriate BPMC distributions for monosaccharide-monosaccharide and protein-glycan linkages are derived from the extensive analysis of conformational properties of glycoprotein structures reported in the Protein Data Bank. Use of the BPMC search leads to significant improvements in sampling efficiency for glycan simulations. Moreover, good agreement with the X-ray glycoprotein structures is achieved for all glycan chain lengths. Thus, average/median RMSDs are 0.81/0.68 Å for one-residue glycans and 1.32/1.47 Å for three-residue glycans. RMSD from the native structure for the lowest-energy conformation of the 12-residue glycan chain (PDB ID 3og2) is 1.53 Å. Additionally, results obtained for free short oligosaccharides using the new force field are in line with the available experimental data, i.e., the most populated conformations in solution are predicted to be the lowest energy ones. The newly developed parameters allow for the accurate modeling of linear and branched hexopyranose glycosides in heterogeneous systems.

Structure-based predictions of activity cliffs

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

Modulation of Macrophage Gene Expression via Liver X Receptor α Serine 198 Phosphorylation

In mouse models of atherosclerosis, normalization of hyperlipidemia promotes macrophage emigration and regression of atherosclerotic plaques in part by liver X receptor (LXR)-mediated induction of the chemokine receptor CCR7. Here we report that LXRα serine 198 (S198) phosphorylation modulates CCR7 expression. Low levels of S198 phosphorylation are observed in plaque macrophages in the regression environment where high levels of CCR7 expression are observed. Consistent with these findings, CCR7 gene expression in human and mouse macrophages cell lines is induced when LXRα at S198 is nonphosphorylated. In bone marrow-derived macrophages (BMDMs), we also observed induction of CCR7 by ligands that promote nonphosphorylated LXRα S198, and this was lost in LXR-deficient BMDMs. LXRα occupancy at the CCR7 promoter is enhanced and histone modifications associated with gene repression are reduced in RAW264.7 cells expressing nonphosphorylated LXRα (RAW-LXRα S198A) compared to RAW264.7 cells expressing wild-type (WT) phosphorylated LXRα (RAW-LXRα WT). Expression profiling of ligand-treated RAW-LXRα S198A cells compared to RAW-LXRα WT cells revealed induction of cell migratory and anti-inflammatory genes and repression of proinflammatory genes. Modeling of LXRα S198 in the nonphosphorylated and phosphorylated states identified phosphorylation-dependent conformational changes in the hinge region commensurate with the presence of sites for protein interaction. Therefore, gene transcription is regulated by LXRα S198 phosphorylation, including that of antiatherogenic genes such as CCR7.

Internal coordinate molecular dynamics: a foundation for multiscale dynamics

Internal coordinates such as bond lengths, bond angles, and torsion angles (BAT) are natural coordinates for describing a bonded molecular system. However, the molecular dynamics (MD) simulation methods that are widely used for proteins, DNA, and polymers are based on Cartesian coordinates owing to the mathematical simplicity of the equations of motion. However, constraints are often needed with Cartesian MD simulations to enhance the conformational sampling. This makes the equations of motion in the Cartesian coordinates differential-algebraic, which adversely impacts the complexity and the robustness of the simulations. On the other hand, constraints can be easily placed in BAT coordinates by removing the degrees of freedom that need to be constrained. Thus, the internal coordinate MD (ICMD) offers an attractive alternative to Cartesian coordinate MD for developing multiscale MD method. The torsional MD method is a special adaptation of the ICMD method, where all the bond lengths and bond angles are kept rigid. The advantages of ICMD simulation methods are the longer time step size afforded by freezing high frequency degrees of freedom and performing a conformational search in the more important low frequency torsional degrees of freedom. However, the advancements in the ICMD simulations have been slow and stifled by long-standing mathematical bottlenecks. In this review, we summarize the recent mathematical advancements we have made based on spatial operator algebra, in developing a robust long time scale ICMD simulation toolkit useful for various applications. We also present the applications of ICMD simulations to study conformational changes in proteins and protein structure refinement. We review the advantages of the ICMD simulations over the Cartesian simulations when used with enhanced sampling methods and project the future use of ICMD simulations in protein dynamics.

Experiment-Guided Molecular Modeling of Protein-Protein Complexes Involving GPCRs

Experimental structure determination for G protein-coupled receptors (GPCRs) and especially their complexes with protein and peptide ligands is at its infancy. In the absence of complex structures, molecular modeling and docking play a large role not only by providing a proper 3D context for interpretation of biochemical and biophysical data, but also by prospectively guiding experiments. Experimentally confirmed restraints may help improve the accuracy and information content of the computational models. Here we present a hybrid molecular modeling protocol that integrates heterogeneous experimental data with force field-based calculations in the stochastic global optimization of the conformations and relative orientations of binding partners. Some experimental data, such as pharmacophore-like chemical fields or disulfide-trapping restraints, can be seamlessly incorporated in the protocol, while other types of data are more useful at the stage of solution filtering. The protocol was successfully applied to modeling and design of a stable construct that resulted in crystallization of the first complex between a chemokine and its receptor. Examples from this work are used to illustrate the steps of the protocol. The utility of different types of experimental data for modeling and docking is discussed and caveats associated with data misinterpretation are highlighted.

GneimoSim: a modular internal coordinates molecular dynamics simulation package

The generalized Newton-Euler inverse mass operator (GNEIMO) method is an advanced method for internal coordinates molecular dynamics (ICMD). GNEIMO includes several theoretical and algorithmic advancements that address longstanding challenges with ICMD simulations. In this article, we describe the GneimoSim ICMD software package that implements the GNEIMO method. We believe that GneimoSim is the first software package to include advanced features such as the equipartition principle derived for internal coordinates, and a method for including the Fixman potential to eliminate systematic statistical biases introduced by the use of hard constraints. Moreover, by design, GneimoSim is extensible and can be easily interfaced with third party force field packages for ICMD simulations. Currently, GneimoSim includes interfaces to LAMMPS, OpenMM, and Rosetta force field calculation packages. The availability of a comprehensive Python interface to the underlying C++ classes and their methods provides a powerful and versatile mechanism for users to develop simulation scripts to configure the simulation and control the simulation flow. GneimoSim has been used extensively for studying the dynamics of protein structures, refinement of protein homology models, and for simulating large scale protein conformational changes with enhanced sampling methods. GneimoSim is not limited to proteins and can also be used for the simulation of polymeric materials.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

Cryptic determinant of α4β7 binding in the V2 loop of HIV-1 gp120

The peptide segment of the second variable loop of HIV-1 spanning positions 166–181 harbors two functionally important sites. The first, spanning positions 179–181, engages the human α4β7 integrin receptor which is involved in T-cell gut-homing and may play a role in human immunodeficiency virus (HIV)-host cell interactions. The second, at positions 166–178, is a major target of anti-V2 antibodies elicited by the ALVAC/AIDSVAX vaccine used in the RV144 clinical trial. Notably, these two sites are directly adjacent, but do not overlap. Here, we report the identity of a second determinant of α4β7 binding located at positions 170–172 of the V2 loop. This segment – tripeptide QRV^170–172– is located within the second site, yet functionally affects the first site. The absence of this segment abrogates α4β7 binding in peptides bearing the same sequence from position 173–185 as the V2 loops of the RV144 vaccines. However, peptides exhibiting V2 loop sequences from heterologous HIV-1 strains that include this QRV^170–172 motif bind the α4β7 receptor on cells. Therefore, the peptide segment at positions 166–178 of the V2 loop of HIV-1 viruses appears to harbor a cryptic determinant of α4β7 binding. Prior studies show that the anti-V2 antibody response elicited by the RV144 vaccine, along with immune pressure inferred from a sieve analysis, is directed to this same region of the V2 loop. Accordingly, the anti-V2 antibodies that apparently reduced the risk of infection in the RV144 trial may have functioned by blocking α4β7-mediated HIV-host cell interactions via this cryptic determinant.

Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method

Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DISGRO). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is 1:53 A° , with a lowest energy RMSD of 2:99 A° , and an average ensembleRMSD of 5:23 A° . A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about 10 cpu minutes for 12-residue loops, compared to ca 180 cpu minutes using the FALCm method. Test results on benchmark datasets show that DISGRO performs comparably or better than previous successful methods, while requiring far less computing time. DISGRO is especially effective in modeling longer loops (10-17 residues).

Human variants in the neuronal basic helix-loop-helix/Per-Arnt-Sim (bHLH/PAS) transcription factor complex NPAS4/ARNT2 disrupt function

Neuronal Per-Arnt-Sim homology (PAS) Factor 4 (NPAS4) is a neuronal activity-dependent transcription factor which heterodimerises with ARNT2 to regulate genes involved in inhibitory synapse formation. NPAS4 functions to maintain excitatory/inhibitory balance in neurons, while mouse models have shown it to play roles in memory formation, social interaction and neurodegeneration. NPAS4 has therefore been implicated in a number of neuropsychiatric or neurodegenerative diseases which are underpinned by defects in excitatory/inhibitory balance. Here we have explored a broad set of non-synonymous human variants in NPAS4 and ARNT2 for disruption of NPAS4 function. We found two variants in NPAS4 (F147S and E257K) and two variants in ARNT2 (R46W and R107H) which significantly reduced transcriptional activity of the heterodimer on a luciferase reporter gene. Furthermore, we found that NPAS4.F147S was unable to activate expression of the NPAS4 target gene BDNF due to reduced dimerisation with ARNT2. Homology modelling predicts F147 in NPAS4 to lie at the dimer interface, where it appears to directly contribute to protein/protein interaction. We also found that reduced transcriptional activation by ARNT2 R46W was due to disruption of nuclear localisation. These results provide insight into the mechanisms of NPAS4/ARNT dimerisation and transcriptional activation and have potential implications for cognitive phenotypic variation and diseases such as autism, schizophrenia and dementia.

Genetically encoded chemical probes in cells reveal the binding path of urocortin-I to CRF class B GPCR

Molecular determinants regulating the activation of class B G-protein-coupled receptors (GPCRs) by native peptide agonists are largely unknown. We have investigated here the interaction between the corticotropin releasing factor receptor type 1 (CRF1R) and its native 40-mer peptide ligand Urocortin-I directly in mammalian cells. By incorporating unnatural amino acid photochemical and new click-chemical probes into the intact receptor expressed in the native membrane of live cells, 44 intermolecular spatial constraints have been derived for the ligand-receptor interaction. The data were analyzed in the context of the recently resolved crystal structure of CRF1R transmembrane domain and existing extracellular domain structures, yielding a complete conformational model for the peptide-receptor complex. Structural features of the receptor-ligand complex yield molecular insights on the mechanism of receptor activation and the basis for discrimination between agonist and antagonist function.

Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.

Structure of the human glucagon class B G-protein-coupled receptor

Binding of the glucagon peptide to the glucagon receptor (GCGR) triggers the release of glucose from the liver during fasting; thus GCGR plays an important role in glucose homeostasis. Here we report the crystal structure of the seven transmembrane helical domain of human GCGR at 3.4 Å resolution, complemented by extensive site-specific mutagenesis, and a hybrid model of glucagon bound to GCGR to understand the molecular recognition of the receptor for its native ligand. Beyond the shared seven transmembrane fold, the GCGR transmembrane domain deviates from class A G-protein-coupled receptors with a large ligand-binding pocket and the first transmembrane helix having a 'stalk' region that extends three alpha-helical turns above the plane of the membrane. The stalk positions the extracellular domain (~12 kilodaltons) relative to the membrane to form the glucagon-binding site that captures the peptide and facilitates the insertion of glucagon's amino terminus into the seven transmembrane domain.