RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

A mechanistic study on the tolerance of PAM distal end mismatch by SpCas9.

The therapeutic application of CRISPR-Cas9 is limited due to its off-target activity. To have a better understanding of this off-target effect, we focused on its mismatch-prone PAM distal end. The off-target activity of SpCas9 depends directly on the nature of mismatches, which in turn results in deviation of the active site of SpCas9 due to structural instability in the RNA-DNA duplex strand. In order to test the hypothesis, we designed an array of mismatched target sites at the PAM distal end and performed in vitro and cell line-based experiments, which showed a strong correlation for Cas9 activity. We found that target sites having multiple mismatches in the 18th to 15th position upstream of the PAM showed no to little activity. For further mechanistic validation, Molecular Dynamics simulations were performed, which revealed that certain mismatches showed elevated root mean square deviation values that can be attributed to conformational instability within the RNA-DNA duplex. Therefore, for successful prediction of the off-target effect of SpCas9, along with complementation-derived energy, the RNA-DNA duplex stability should be taken into account.

West Nile virus-associated HLA-DRB1 alleles in the Greek population: A structural perspective.

The HLA system plays a significant role via the regulation of the immune system and contributes to the progression and protection of many diseases. In our previous study, several HLA-DRB1 alleles were found to have a susceptible or protective role toward infection and neuroinvasion of West Nile Virus (WNV) in the Greek population. As expected, the majority of polymorphic positions are located in the peptide-binding region of the molecule. In the present work, the structure of these alleles was studied in silico, to examine the effect of polymorphism on the conformation of DRB1 proteins, with the aspect of WNV association. More specifically, molecular dynamics simulations were used for structural prediction of 23 available alleles. These modeled alleles were evaluated using root-mean-square deviation (RMSD) and root-mean-square fluctuation analysis. Low RMSD values indicate that different alleles have similar structures. Furthermore, low fluctuation was observed in the peptide-binding region between alleles with the higher and the lowest RMSD values. These findings indicate that probably variable residues do not affect the behavior of DRB1 alleles in WNV disease, by causing structural differences between them.

Structural analysis and inhibition capacity of dioscorin protein yam: Theoretical study by molecular docking and dynamic simulations.

Proteases such as trypsins in the gut of Spodoptera frugiperda are responsible for breaking down dietary proteins into amino acids necessary for insect growth and development. In this study, we characterized the insecticidal potential of dioscorin, the storage protein of yam (Dioscorea alata), using molecular docking and molecular dynamics simulations to determine the interactions between trypsin enzymes and the protein inhibitor dioscorin. To achieve this, we used the three-dimensional structures of the trypsin-like digestive enzymes of S. frugiperda, a pest of corn and cotton, as receptors or target molecules. We performed protein-protein docking using Cluspro software, estimation of the binding free energy, and information on the dynamic and time-dependent behavior of dioscorin-trypsin complexes using the NAMD package. Our computational analysis showed that dioscorin can bind to the digestive trypsins of S. frugiperda, as confirmed by the affinity energy values (-1022.4 to -1236.9), stability of the complexes during the simulation trajectory, and binding free energy values between -57.3 and -66.9 kcal/mol. Additionally, dioscorin uses two reactive sites to bind trypsin, but the largest contribution to the interaction energy is made by amino acid residues between amino acid backbone positions 8-14 by hydrogen bonds, hydrophobic, and Van der Waals (VdW) interactions. VdW is the energy that makes the greatest contribution to the binding energy. Collectively, our findings demonstrate, for the first time, the binding capacity of the yam protein dioscorin to the digestive trypsin of S. frugiperda. These promising results suggest a possible bioinsecticide action of dioscorin.

Synthesis of Some Benzothiazole Derivatives Based on 3-Hydroxypyridine-4-one and Benzaldehyde and Evaluation of Their ß-Amyloid Aggregation Inhibition Using both Experimental Methods and Molecular Dynamic Simulation.

Some novel inhibitors based on the (benzo[d]thiazol-2-yl)-1-phenylmethanimine derivatives were designed to reduce the aggregation process in Alzheimer's disease. These structures seem to mimic stilbene-like scaffold, while the benzothiazole moiety "locks" the thioflavin T binding site. Other inhibitors were designed based on 2-((benzo[d]thiazol-2-ylimino)methyl)-5-(benzyloxy)-1-methylpyridin-4(H)-one derivatives. Benzo[d]thiazol-2-amine derivatives were prepared by the reaction of aniline derivatives with ammonium thiocyanate in the presence of bromine/acetic acid. Then, the reaction of amines with benzaldehyde derivatives and 5-(benzyloxy)-1-methyl-4-oxo-1,4-dihydropyridine-2-carbaldehyde gave the desired compounds. The plate reader-based fibrillation assay was done to evaluate the inhibition of Aß aggregation. Also, molecular dynamic simulation was carried out to clarify the interaction manner of the designed compounds with Aß formation. The biological evaluation proved 5a and 7e as the best inhibitor of the Aß aggregation. compound 5a in the concentration of 50 µM inhibited Aß fibril formation better than 7e. MD simulation elucidated that the Aß aggregation inhibitors in different concentrations represented different binding conformations throughout the entire or in one area of Aß. MD showed the ligands in lower concentrations accumulate in an area of Aß aggregations and separate one fibril from the aggregated Aß. On the contrary, in higher concentrations, the ligands tend to be located through the entire Aß.

Rational design of an anti-cancer peptide inhibiting CD147 / Cyp A interaction.

The CD147 / Cyp A interaction is a critical pathway in cancer types and an essential factor in entering the COVID-19 virus into the host cell. Melittin acts as an inhibitory peptide in cancer types by blocking the CD147/ Cyp A interaction. The clinical application of Melittin is limited due to weak penetration into cancer cells. TAT is an arginine-rich peptide with high penetration ability into cells widely used in drug delivery systems. This study aimed to design a hybrid peptide derived from Melittin and TAT to inhibit CD147 /Cyp A interaction. An amino acid region with high anti-cancer activity in Melittin was selected based on the physicochemical properties. Based on the results, a truncated Melittin peptide with 15 amino acids by the GGGS linker was fused to a TAT peptide (nine amino acids) to increase the penetration rate into the cell. A new hybrid peptide analog(TM) was selected by replacing the glycine with serine based on random point mutation. Docking results indicated that the TM peptide acts as an inhibitory peptide with high binding energy when interacting with CD147 and the CypA proteins. RMSD and RMSF results confirmed the high stability of the TM peptide in interaction with CD147. Also, the coarse-grained simulation showed the penetration potential of TM peptide into the DOPS-DOPC model membrane. Our findings indicated that the designed multifunctional peptide could be an attractive therapeutic candidate to halter tumor types and COVID-19 infection.

Glycyrrhizin as a promising kryptonite against SARS-CoV-2: Clinical, experimental, and theoretical evidences.

COVID-19 is the most devastating disease in recent times affecting most people globally. The higher rate of transmissibility and mutations of SARS-CoV-2 along with the lack of potential therapeutics has made it a global crisis. Potential molecules from natural sources could be a fruitful remedy to combat COVID-19. This systematic review highlights the detailed therapeutic implication of naturally occurring glycyrrhizin and its related derivatives against COVID-19. Glycyrrhizin has already been established for blocking different biomolecular targets related to the SARS-CoV-2 replication cycle. In this article, several experimental and theoretical evidences of glycyrrhizin and related derivatives have been discussed in detail to evaluate their potential as a promising therapeutic strategy against COVID-19. Moreover, the implication of glycyrrhizin in traditional Chinese medicines for alleviating the symptoms of COVID-19 has been reviewed. The potential role of glycyrrhizin and related compounds in affecting various stages of the SARS-CoV-2 life cycle has also been discussed in detail. Derivatization of glycyrrhizin for designing potential lead compounds along with combination therapy with other anti-SARS-CoV-2 agents followed by extensive evaluation may assist in the formulation of novel anti-coronaviral therapy for better treatment to combat COVID-19.

Screening and identification of potential MERS-CoV papain-like protease (PLpro) inhibitors; Steady-state kinetic and Molecular dynamic studies.

MERS-CoV belongs to the coronavirus group. Recent years have seen a rash of coronavirus epidemics. In June 2012, MERS-CoV was discovered in the Kingdom of Saudi Arabia, with 2,591 MERSA cases confirmed by lab tests by the end of August 2022 and 894 deaths at a case-fatality ratio (CFR) of 34.5% documented worldwide. Saudi Arabia reported the majority of these cases, with 2,184 cases and 813 deaths (CFR: 37.2%), necessitating a thorough understanding of the molecular machinery of MERS-CoV. To develop antiviral medicines, illustrative investigation of the protein in coronavirus subunits are required to increase our understanding of the subject. In this study, recombinant expression and purification of MERS-CoV (PLpro), a primary goal for the development of 22 new inhibitors, were completed using a high throughput screening methodology that employed fragment-based libraries in conjunction with structure-based virtual screening. Compounds 2, 7, and 20, showed significant biological activity. Moreover, a docking analysis revealed that the three compounds had favorable binding mood and binding free energy. Molecular dynamic simulation demonstrated the stability of compound 2 (2-((Benzimidazol-2-yl) thio)-1-arylethan-1-ones) the strongest inhibitory activity against the PLpro enzyme. In addition, disubstitutions at the meta and para locations are the only substitutions that may boost the inhibitory action against PLpro. Compound 2 was chosen as a MERS-CoV PLpro inhibitor after passing absorption, distribution, metabolism, and excretion studies; however, further investigations are required.

Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro.

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is Mpro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 Mpro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 Mpro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein-ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein-ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein-ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 Mpro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 Mpro target.

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

BACKGROUND: Humanization of mouse monoclonal antibodies (mAbs) is crucial for reducing their immunogenicity in humans. However, humanized mAbs often lose their binding affinities. Therefore, an in silico humanization method that can prevent the loss of the binding affinity of mAbs is needed. METHODS: We developed an in silico V(D)J recombination platform in which we used V(D)J human germline gene sequences to design five humanized candidates of anti-tumor necrosis factor (TNF)-α mAbs (C1-C5) by using different human germline templates. The candidates were subjected to molecular dynamics simulation. In addition, the structural similarities of their complementarity-determining regions (CDRs) to those of original mouse mAbs were estimated to derive the weighted interatomic root mean squared deviation (wRMSDi) value. Subsequently, the correlation of the derived wRMSDi value with the half maximal effective concentration (EC50) and the binding affinity (KD) of the humanized anti-TNF-α candidates was examined. To confirm whether our in silico estimation method can be used for other humanized mAbs, we tested our method using the anti-epidermal growth factor receptor (EGFR) a4.6.1, anti-glypican-3 (GPC3) YP9.1 and anti-α4ß1 integrin HP1/2L mAbs. RESULTS: The R2 value for the correlation between the wRMSDi and log(EC50) of the recombinant Remicade and those of the humanized anti-TNF-α candidates was 0.901, and the R2 value for the correlation between wRMSDi and log(KD) was 0.9921. The results indicated that our in silico V(D)J recombination platform could predict the binding affinity of humanized candidates and successfully identify the high-affinity humanized anti-TNF-α antibody (Ab) C1 with a binding affinity similar to that of the parental chimeric mAb (5.13 × 10-10). For the anti-EGFR a4.6.1, anti-GPC3 YP9.1, and anti-α4ß1 integrin HP1/2L mAbs, the wRMSDi and log(EC50) exhibited strong correlations (R2 = 0.9908, 0.9999, and 0.8907, respectively). CONCLUSIONS: Our in silico V(D)J recombination platform can facilitate the development of humanized mAbs with low immunogenicity and high binding affinities. This platform can directly transform numerous mAbs with therapeutic potential to humanized or even human therapeutic Abs for clinical use.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results.

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor-ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Identification of Papain-Like Protease inhibitors of SARS CoV-2 through HTVS, Molecular docking, MMGBSA and Molecular dynamics approach.

Coronaviruses (CoVs) are a large group of enveloped positive sense single-stranded RNA viruses that can cause disease to humans. These are zoonotic having potential to cause large-scale outbreaks of infections widely causing morbidity and mortality. Papain-Like Protease (PLpro) is a cysteine protease, essential for viral replication and proliferation, as a highly conserved enzyme it cleaves peptide linkage between Nsp1, Nsp2, Nsp3, and Nsp4. As a valid therapeutic target, it stops viral reproduction and boosts host immune response thereby halting further spread of infection. In the purpose of identifying inhibitors targeting Papain-Like Proteases (PLpro) we initiated a high throughput virtual screening (HTVS) protocol using a SuperNatural Database. The XP docking results revealed that two compounds SN00334175 and SN00162745 exhibited docking scores of -10.58 kcal/mol and -9.93 kcal/mol respectively. The Further PRIME MMGB-SA studies revealed Van der Waal energy and hydrophobic energy terms as major contributors for total binding free energy. The 100 ns molecular dynamics simulation of SN00334175/7JN2 and SN00162745/7JN2 revealed that these complexes were stabilized with ligand binding forming interactions with Gly266, Asn267, Tyr268, Tyr273, Thr301 and Asp302, Lys157, Leu162, Asp164, Arg166, Glu167, Pro248 and Tyr264.

Identification of alkaloids from Justicia adhatoda as potent SARS CoV-2 main protease inhibitors: An in silico perspective.

The COVID-19 pandemic, caused by SARS CoV-2, is responsible for millions of death worldwide. No approved/proper therapeutics is currently available which can effectively combat this outbreak. Several attempts have been undertaken in the search of effective drugs to control the spread of SARS CoV-2 infection. The main protease (Mpro), key component for the cleavage of the viral polyprotein, is considered to be one of the important drug targets for treating COVID-19. Various phytochemicals, including polyphenols and alkaloids, have been proposed as potent inhibitors of Mpro. The alkaloids from leaf extracts of Justicia adhatoda have also been reported to possess anti-viral activity. But whether these alkaloids exhibit any inhibitory effect on SARS CoV-2 Mpro is far from clear. To explore this in detail, we have adopted computational approaches. Justicia adhatoda alkaloids possessing proper drug-likeness properties and two anti-HIV drugs (lopinavir and darunavir; having binding affinity -7.3 to -7.4 kcal/mol) were docked against SARS CoV-2 Mpro to study their binding properties. Only one alkaloid (anisotine) had interaction with both the catalytic residues (His41 and Cys145) of Mpro and exhibited good binding affinity (-7.9 kcal/mol). Molecular dynamic simulations (100 ns) revealed that Mpro-anisotine complex is more stable, conformationally less fluctuated; slightly less compact and marginally expanded than Mpro-darunavir/lopinavir complex. Even the number of intermolecular H-bonds and MM-GBSA analysis suggested that anisotine is a more potent Mpro inhibitor than the two previously recommended antiviral drugs (lopinavir and darunavir) and may evolve as a promising anti-COVID-19 drug if proven in animal experiments and on patients.

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis.

Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus (SARS CoV-2) has been declared a worldwide pandemic by WHO recently. The complete understanding of the complex genomic structure of SARS CoV-2 has enabled the use of computational tools in search of SARS CoV-2 inhibitors against the multiple proteins responsible for its entry and multiplication in human cells. With this endeavor, 177 natural, anti-viral chemical entities and their derivatives, selected through the critical analysis of the literatures, were studied using pharmacophore screening followed by molecular docking against RNA dependent RNA polymerase and main protease. The identified hits have been subjected to molecular dynamic simulations to study the stability of ligand-protein complexes followed by ADMET analysis and Lipinski filters to confirm their drug likeliness. It has led to an important start point in the drug discovery and development of therapeutic agents against SARS CoV-2.

Neuroprotective immunity by essential nutrient "Choline" for the prevention of SARS CoV2 infections: An in silico study by molecular dynamics approach.

Prenatal COVID infection is one of the worst affected and least attended aspects of the COVID-19 disease. Like other coronaviruses, CoV2 infection is anticipated to affect fetal development by maternal inflammatory response on the fetus and placenta. Studies showed that higher prenatal choline level in mother's body can safeguard the developing brain of the fetus from the adverse effects of CoV2 infection. Choline is commonly used as food supplement. By virtual screening, molecular docking and molecular dynamics techniques, we have established a strong inhibitory possibility of choline for SARS 3CLpro protease which may provide a lead for prenatal COVID-19 treatment.

Study of Caspase 8 Inhibition for the Management of Alzheimer's Disease: A Molecular Docking and Dynamics Simulation.

Alzheimer's disease (AD) is the most common type of dementia and usually manifests as diminished episodic memory and cognitive functions. Caspases are crucial mediators of neuronal death in a number of neurodegenerative diseases, and caspase 8 is considered a major therapeutic target in the context of AD. In the present study, we performed a virtual screening of 200 natural compounds by molecular docking with respect to their abilities to bind with caspase 8. Among them, rutaecarpine was found to have the highest (negative) binding energy (-6.5 kcal/mol) and was further subjected to molecular dynamics (MD) simulation analysis. Caspase 8 was determined to interact with rutaecarpine through five amino acid residues, specifically Thr337, Lys353, Val354, Phe355, and Phe356, and two hydrogen bonds (ligand: H35-A: LYS353:O and A:PHE355: N-ligand: N5). Furthermore, a 50 ns MD simulation was conducted to optimize the interaction, to predict complex flexibility, and to investigate the stability of the caspase 8-rutaecarpine complex, which appeared to be quite stable. The obtained results propose that rutaecarpine could be a lead compound that bears remarkable anti-Alzheimer's potential against caspase 8.

Assessment of the transmembrane domain structures in GPCR Dock 2013 models.

The community-wide blind prediction of G-protein coupled receptor (GPCR) structures and ligand docking has been conducted three times and the quality of the models was primarily assessed by the accuracy of ligand binding modes. The seven transmembrane (TM) helices of the receptors were taken as a whole; thus the model quality within the 7TM domains has not been evaluated. Here we evaluate the 7TM domain structures in the models submitted for the last round of prediction - GPCR Dock 2013. Applying the 7 × 7 RMSD matrix analysis described in our prior work, we show that the models vary widely in prediction accuracy of the 7TM structures, exhibiting diverse structural differences from the targets. For the prediction of the 5-hydroxytryptamine receptors, the top 7TM models are rather close to the targets, which however are not ranked top by ligand-docking. On the other hand, notable deviations of the TMs are found in in the previously identified top docking models that closely resemble other receptors. We further reveal reasons of success and failure in ligand docking for the models. This current assessment not only complements the previous assessment, but also provides important insights into the current status of GPCR modeling and ligand docking.

Exploring the conformational landscapes of HIV protease structural ensembles using principal component analysis.

HIV protease, an essential enzyme for viral particle maturation, is an important drug target of HIV. Its structural conformation is a key determinant of both biological function as well as efficient binding of protease inhibitor molecules. In the present study we analyzed 471 crystal structures of HIV-1 protease to understand the conformational changes induced by mutations or binding of various ligands and substrates. We performed principal component analysis on the ensembles of the HIV-1 protease structures to explore the conformational landscapes. The study identified structural differences between drug resistant and drug sensitive protease structures. Conformational changes were identified in the A and B chains of homo-dimeric HIV protease structures having different combinations of mutations, and also rigidity in the binding conformation of HIV drugs within the active site of the protein.© 2018 Wiley Periodicals, Inc.

Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the "best" solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

7×7 RMSD matrix: A new method for quantitative comparison of the transmembrane domain structures in the G-protein coupled receptors.

The G-protein coupled receptors (GPCRs) share a conserved heptahelical fold in the transmembrane (TM) region, but the exact arrangements of the seven TM helices vary with receptors and their activation states. The differences or the changes have been observed in the experimentally solved structures, but have not been systematically and quantitatively investigated due to lack of suitable methods. In this work, we describe a novel method, called 7×7 RMSD matrix that is proposed specifically for comparing the characteristic 7TM bundle structures of GPCRs. Compared to the commonly used overall TM bundle RMSD as a single parameter, a 7×7 RMSD matrix contains 49 parameters, which reveal changes of the relative orientations of the seven TMs. We demonstrate the novelty and advantages of this method by tackling two problems that are challenging for the existing methods. With this method, we are able to identify and quantify the helix movements in the activated receptor structures and reveal structural conservation and divergence as well as the structural relationships of different GPCRs in terms of the relative orientations of the seven TMs.