Subjects -> ESTATE, HOUSING AND URBAN PLANNING (Total: 304 journals)     - CLEANING AND DYEING (1 journals)    - ESTATE, HOUSING AND URBAN PLANNING (237 journals)    - FIRE PREVENTION (13 journals)    - HEATING, PLUMBING AND REFRIGERATION (6 journals)    - HOME ECONOMICS (9 journals)    - INTERIOR DESIGN AND DECORATION (21 journals)    - REAL ESTATE (17 journals) INTERIOR DESIGN AND DECORATION (21 journals)
 Showing 1 - 20 of 20 Journals sorted alphabetically Architectural Design       (Followers: 31) Artifact : Journal of Design Practice       (Followers: 8) City: analysis of urban trends, culture, theory, policy, action       (Followers: 28) CoDesign: International Journal of CoCreation in Design and the Arts       (Followers: 16) Design Issues       (Followers: 33) Indoor and Built Environment       (Followers: 4) Interiors : Design, Architecture and Culture       (Followers: 21) International Journal of Human Factors and Ergonomics       (Followers: 20) International Journal of Sustainable Design       (Followers: 8) International Journal on Interactive Design and Manufacturing (IJIDeM)       (Followers: 3) Journal of Building Survey, Appraisal & Valuation       (Followers: 4) Journal of Computer-Aided Molecular Design       (Followers: 6) Journal of Design History       (Followers: 21) Journal of Design, Business & Society       (Followers: 1) Journal of Facade Design and Engineering       (Followers: 2) Journal of Interior Design       (Followers: 6) Journal of Urban Design       (Followers: 23) Res Mobilis : Revista internacional de investigación en mobiliario y objetos decorativos Reviews of Human Factors and Ergonomics       (Followers: 16) Zentralblatt für Arbeitsmedizin, Arbeitsschutz und Ergonomie. Mit Beiträgen aus Umweltmedizin und Sozialmedizin       (Followers: 1)
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 Journal of Computer-Aided Molecular DesignJournal Prestige (SJR): 0.941 Citation Impact (citeScore): 3Number of Followers: 6      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-4951 - ISSN (Online) 0920-654X Published by Springer-Verlag  [2469 journals]
• Comprehensive evaluation of end-point free energy techniques in
carboxylated-pillar[6]arene host–guest binding: I. Standard procedure

Abstract: Abstract Despite the massive application of end-point free energy methods in protein–ligand and protein–protein interactions, computational understandings about their performance in relatively simple and prototypical host–guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host–guest dataset containing 13 host–guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host–guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein–ligand binding, the standard end-point calculations cannot produce useful outcomes in host–guest binding and thus are not recommended unless alterations are performed.
PubDate: 2022-09-22

• Predicting octanol/water partition coefficients and pKa for the SAMPL7
challenge using the SM12, SM8 and SMD solvation models

Abstract: Abstract Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. Octanol/water partition coefficients were predicted from solvation free energies computed using electronic structure calculations with the SM12, SM8 and SMD solvation models. Within these calculations we compared the use of gas- and solution-phase optimized geometries of the solute. Based on these calculations we found that in general the use of solution phase-optimized geometries increases the affinity of the solutes for water as compared to octanol, with the use of gas-phase optimized geometries resulting in the better agreement with experiment. The pKa is computed using the direct approach, scaled solvent-accessible surface model, and the inclusion of an explicit water molecule, where the latter two methods have previously been shown to offer improved predictions as compared to the direct approach. We find that the use of an explicit water molecule provides superior predictions, and that the predicted macroscopic pKa is sensitive to the employed microstates.
PubDate: 2022-09-19

• Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary
mentor, colleague, collaborator, and innovator

Abstract: Abstract In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry’s endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.
PubDate: 2022-09-17

• Prot2Prot: a deep learning model for rapid, photorealistic macromolecular
visualization

Abstract: Abstract Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot.
PubDate: 2022-08-26

• FastGrow: on-the-fly growing and its application to DYRK1A

Abstract: Abstract Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow’s features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.
PubDate: 2022-08-22

• Covalent docking in CDOCKER

Abstract: Abstract Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.
PubDate: 2022-08-19

• Unveiling the G4-PAMAM capacity to bind and protect Ang-(1-7) bioactive
peptide by molecular dynamics simulations

Abstract: Abstract Angiotensin-(1-7) re-balance the Renin-Angiotensin system affected during several pathologies, including the new COVID-19; cardiovascular diseases; and cancer. However, one of the limiting factors for its therapeutic use is its short half-life, which might be overcome with the use of dendrimers as nanoprotectors. In this work, we addressed the following issues: (1) the capacity of our computational protocol to reproduce the experimental structural features of the (hydroxyl/amino)-terminated PAMAM dendrimers as well as the Angiotensin-(1-7) peptide; (2) the coupling of Angiotensin-(1-7) to (hydroxyl/amino)-terminated PAMAM dendrimers in order to gain insight into the structural basis of its molecular binding; (3) the capacity of the dendrimers to protect Angiotensin-(1-7); and (4) the effect of pH changes on the peptide binding and covering. Our Molecular-Dynamics/Metadynamics-based computational protocol well modeled the structural experimental features reported in the literature and our double-docking approach was able to provide reasonable initial structures for stable complexes. At neutral pH, PAMAM dendrimers with both terminal types were able to interact stably with 3 Angiotensin-(1-7) peptides through ASP1, TYR4 and PRO7 key amino acids. In general, they bind on the surface in the case of the hydroxyl-terminated compact dendrimer and in the internal zone in the case of the amino-terminated open dendrimer. At acidic pH, PAMAM dendrimers with both terminal groups are still able to interact with peptides either internalized or in its periphery, however, the number of contacts, the percentage of coverage and the number of hydrogen bonds are lesser than at neutral pH, suggesting a state for peptide release. In summary, amino-terminated PAMAM dendrimer showed slightly better features to bind, load and protect Angiotensin-(1-7) peptides.
PubDate: 2022-08-08

• Des3PI: a fragment-based approach to design cyclic peptides targeting
protein–protein interactions

Abstract: Abstract Protein–protein interactions (PPIs) play crucial roles in many cellular processes and their deregulation often leads to cellular dysfunctions. One promising way to modulate PPIs is to use peptide derivatives that bind their protein target with high affinity and high specificity. Peptide modulators are often designed using secondary structure mimics. However, fragment-based design is an alternative emergent approach in the PPI field. Most of the reported computational fragment-based libraries targeting PPIs are composed of small molecules or already approved drugs, but, according to our knowledge, no amino acid based library has been reported yet. In this context, we developed a novel fragment-based approach called Des3PI (design of peptides targeting protein–protein interactions) with a library composed of natural amino acids. All the amino acids are docked into the target surface using Autodock Vina. The resulting binding modes are geometrically clustered, and, in each cluster, the most recurrent amino acids are identified and form the hotspots that will compose the designed peptide. This approach was applied on Ras and Mcl-1 proteins, as well as on A $$\beta$$ protofibril. For each target, at least five peptides generated by Des3PI were tested in silico: the peptides were first blindly docked on their target, and then, the stability of the successfully docked complexes was verified using 200 ns MD simulations. Des3PI shows very encouraging results by yielding at least 3 peptides for each protein target that succeeded in passing the two-step assessment.
PubDate: 2022-08-06

• Modeling receptor flexibility in the structure-based design of KRASG12C
inhibitors

Abstract: Abstract KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRASG12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRASG12C—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRASG12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRASG12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRASG12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.
PubDate: 2022-08-05

• Crystal polymorphism and spectroscopical properties of sulfonamides in
solid state by means of First Principles calculations

Abstract: Abstract Sulfonamides are an important class of therapeutic agents. The increase in the number of new sulfonamide derivatives makes it necessary to study more rationally the chemical structure, because the solid forms often display different mechanical, thermal and physicochemical properties that can influence the bioavailability and stability of the drugs; consequently, the polymorphic structures are of great interest to the pharmaceutical industry because of their ability to modify the physical properties of the active pharmaceutical ingredient. The molecular interactions of these drugs in their crystal lattice are important for the stability of the crystals and polymorphism and for preparing composite complexes for optimizing the use of these drugs. In this work, the crystal structure of these drugs and crystal polymorphism is investigated. So, the crystal forms of antibiotics derivatives of the sulfonamides, sulfamethoxazole, sulfamethazine, sulfachloropyridazine, and sulfacetamide are studied at the molecular and supramolecular level by using computational modeling approach at quantum mechanical level. The spectroscopic properties of these systems are also studied explaining assignments of previous experimental data. The results of DFT calculations reproduce the crystal structures of sulfonamides determined experimentally and the polymorphism in these molecules have been clarified. Likewise, the main intermolecular interactions in all crystal forms of these sulfonamides are H-bonds among the sulfonic and amino groups and SNH groups, and also some π-π interactions. Also, these 3-D periodical models allow the exploration of the intermolecular interactions included in the crystal structures and some of these interactions can alter the vibration modes of the molecules. Therefore, the use of these models can be useful for experimental spectroscopy studies where use actual crystal solids.
PubDate: 2022-07-26

• Insight into the mechanism of molecular recognition between human
Integrin-Linked Kinase and Cpd22 and its implication at atomic level

Abstract: Absract Pseudokinases have received increasing attention over the past decade because of their role in different physiological phenomena. Although pseudokinases lack several active-site residues, thereby hindering their catalytic activity, recent discoveries have shown that these proteins can play a role in intracellular signaling thanks to their non-catalytic functions. Integrin-linked kinase (ILK) was discovered more than two decades ago and was subsequently validated as a promising target for neoplastic diseases. Since then, only a few small-molecule inhibitors have been described, with the V-shaped pyrazole Cpd22 being the most interesting and characterized. However, little is known about its detailed mechanism of action at atomic level. In this study, using a combination of computational chemistry methods including PELE calculations, docking, molecular dynamics and experimental surface plasmon resonance, we were able to prove the direct binding of this molecule to ILK, thus providing the basis of its molecular recognition by the protein and the effect over its architecture. Our breakthroughs show that Cpd22 binding stabilizes the ILK domain by binding to the pseudo-active site in a similar way to the ATP, possibly modulating its scaffolding properties as pseudokinase. Moreover, our results explain the experimental observations obtained during Cpd22 development, thus paving the way to the development of new chemical probes and potential drugs. Graphical abstract
PubDate: 2022-07-23

• On the force field optimisation of $$\beta$$ β -lactam cores using
the force field Toolkit

Abstract: Abstract When employing molecular dynamics (MD) simulations for computer-aided drug design, the quality of the used force fields is highly important. Here we present reparametrisations of the force fields for the core molecules from 9 different $$\beta$$ -lactam classes, for which we utilized the force field Toolkit and Gaussian calculations. We focus on the parametrisation of the dihedral angles, with the goal of reproducing the optimised quantum geometry in MD simulations. Parameters taken from CGenFF turn out to be a good initial guess for the multiplicity of each dihedral angle, but the key to a successful parametrisation is found to lie in the phase shifts. Based on the optimised quantum geometry, we come up with a strategy for predicting the phase shifts prior to the dihedral potential fitting. This allows us to successfully parameterise 8 out of the 11 molecules studied here, while the remaining 3 molecules can also be parameterised with small adjustments. Our work highlights the importance of predicting the dihedral phase shifts in the ligand parametrisation protocol, and provides a simple yet valuable strategy for improving the process of parameterising force fields of drug-like molecules.
PubDate: 2022-07-11
DOI: 10.1007/s10822-022-00464-3

• Identification of HPr kinase/phosphorylase inhibitors: novel
antimicrobials against resistant Enterococcus faecalis

Abstract: Abstract Enterococcus faecalis, a gram-positive bacterium, is among the most common nosocomial pathogens due to its limited susceptibility to antibiotics and its reservoir of the genes coding for virulence factors. Bacterial enzymes such as kinases and phosphorylases play important roles in diverse functions of a bacterial cell and, thus, are potential antibacterial drug targets. In Gram-positive bacteria, HPr Kinase/Phosphorylase (HPrK/P), a bifunctional enzyme is involved in the regulation of carbon catabolite repression by phosphorylating/dephosphorylating the histidine-containing phosphocarrier protein (HPr) at Ser46 residue. Deficiencies in HPrK/P function leads to severe defects in bacterial growth. This study aimed at identifying novel inhibitors of E. faecalis HPrK/P from a commercial compound library using structure-based virtual screening. The hit molecules were purchased and their effect on enzyme activity and growth of resistant E. faecalis was evaluated in vitro. Furthermore, docking and molecular dynamics simulations were performed to study the interactions of the hit compounds with HPrK/P. Among the identified hit molecules, two compounds inhibited the phosphorylation of HPr as well as significantly reduced the growth of resistant E. faecalis in vitro. These identified potential HPrK/P inhibitors open new research avenues towards the development of novel antimicrobials against resistant Gram-positive bacteria.
PubDate: 2022-07-09
DOI: 10.1007/s10822-022-00461-6

• In silico identification and in vitro antiviral validation of potential
inhibitors against Chikungunya virus

Abstract: Abstract The Chikungunya virus (CHIKV) has become endemic in the Africa, Asia and Indian subcontinent, with its continuous re-emergence causing a significant public health crisis. The unavailability of specific antivirals and vaccines against the virus has highlighted an urgent need for novel therapeutics. In the present study, we have identified small molecule inhibitors targeting the envelope proteins of the CHIKV to interfere with the fusion process, eventually inhibiting the cell entry of the virus particles. We employed high throughput computational screening of large datasets against two different binding sites in the E1–E2 dimer to identify potential candidate inhibitors. Among them, four high affinity inhibitors were selected to confirm their anti-CHIKV activity in the in vitro assay. Quercetin derivatives, Taxifolin and Rutin, binds to the E1–E2 dimer at different sites and display inhibition of CHIKV infection with EC50 values 3.6 μM and 87.67 μM, respectively. Another potential inhibitor with ID ChemDiv 8015-3006 binds at both the target sites and shows anti-CHIKV activity at EC50 = 41 μM. The results show dose-dependent inhibitory effects of Taxifolin, Rutin and ChemDiv 8015-3006 against the CHIKV with minimal cytotoxicity. In addition, molecular dynamics studies revealed the structural stability of these inhibitors at their respective binding sites in the E1–E2 protein. In conclusion, our study reports Taxifolin, Rutin and ChemDiv 8015-3006 as potential inhibitors of the CHIKV entry. Also, this study suggests a few potential candidate inhibitors which could serve as a template to design envelope protein specific CHIKV entry inhibitors.
PubDate: 2022-07-05
DOI: 10.1007/s10822-022-00463-4

• Ligand-based and structure-based studies to develop predictive models for
SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

Abstract: The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure–activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models’ goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure–activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design. Graphical abstract
PubDate: 2022-06-18
DOI: 10.1007/s10822-022-00460-7

• Deciphering the conformational transitions of LIMK2 active and inactive
states to ponder specific druggable states through microsecond scale
molecular dynamics simulation

Abstract: Abstract LIMK2 inhibitors are one of the potential therapeutic modalities for treating various diseases. In the current scenario, there is a paucity of effective LIMK inhibitors that are highly specific with minimal off-target effects. To date, the conformational transitions of LIMK2 from DFGinαCin (CIDI) (active) to DFGoutαCout (CODO) (inactive) states are yet to be probed and are essential for capturing the unique, druggable conformations. Therefore, this study was intended to capture the diverse conformational states of LIMK2 for accelerating the rational identification of conformation specific inhibitors through high-end structural bioinformatics protocols. Hence, in this study, molecular modelling followed by an extensive microsecond timescale of molecular dynamics simulation was performed encompassing perturbation response scanning, metapath, and community analysis towards the conformational sampling of LIMK2. Overall this study precisely identifies the conformational ensemble of LIMK2 the intermediate inactive states namely, CIDO, CinterDinter, CIDinter, CinterDI, CinterDO, CODI, CODinter apart from CIDI and CODO. This also facilitated observing that β8 preceding XDFG, αC (F373, L374), and αD (L413) as the major effectors that may facilitate the regulation of varying conformational transitions among the states. Additionally, the conserved β sheets and the loops namely, C.l, b.l, and G/P.loop were observed to be involved in the metapath for allosteric communication among the intermediates with CIDI and CODO state. Moreover, only the CODO state was observed to have closed type A.l, while the CIDI and other intermediate states except for CIDO were observed to have open-DFG out type A.l, thereby enabling the binding of substrate. Apart from these, the druggable site analysis inferred that the CIDI and CODO states harbor prominent druggable sites spanning the conserved N-lobe, while the intermediates were observed to have unraveled allosteric druggable sites distal from the ATP binding site, majorly spanning the C-lobe of LIMK2. Thus, this study provides potential insights into the intermediate conformational druggable states of LIMK2 and also the druggable conformations, especially the inactive states of LIMK2, as a specific therapeutic targeting mode. Thus, providing a widened avenue to ponder the allosteric sites or the isoform selectivity conformations for targeting LIMK2 in various disease conditions.
PubDate: 2022-06-02
DOI: 10.1007/s10822-022-00459-0

• Multi-task convolutional neural networks for predicting in vitro clearance
endpoints from molecular images

Abstract: Abstract Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed up the progression of novel candidate molecules. Herein, we have investigated the question if multiple in vitro clearance endpoints could be accurately predicted from image-based molecular representations. Thus, compound measurements for four commonly investigated clearance endpoints were curated from AstraZeneca internal sources, providing a sound basis for building multi-task convolutional neural network models. Application of several increasingly challenging data splitting strategies confirmed that convolutional neural network models were successful at capturing implicit chemical relationships contained in training and test data, similar to what is commonly observed for structural fingerprints. Furthermore, model benchmarking against state-of-the-art machine learning methods, including deep neural networks and graph convolutional neural networks, trained with structure- and graph-based representations, respectively, revealed on par or increased accuracy of convolutional neural networks with clear benefit of multi-task learning across all clearance endpoints. Our findings indicate that image-based molecular representations can be applied to predict multiple clearance endpoints, suggesting a potential follow-up to investigate model interpretability from molecular images.
PubDate: 2022-05-27
DOI: 10.1007/s10822-022-00458-1

• Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering
the role of MM/GBSA

Abstract: Abstract The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14–22% and 6–11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.
PubDate: 2022-05-18
DOI: 10.1007/s10822-022-00456-3

• RDPSOVina: the random drift particle swarm optimization for
protein–ligand docking

Abstract: Abstract Protein–ligand docking is of great importance to drug design, since it can predict the binding affinity between ligand and protein, and guide the synthesis direction of the lead compounds. Over the past few decades, various docking programs have been developed, some of them employing novel optimization algorithms. However, most of those methods cannot simultaneously achieve both good efficiency and accuracy. Therefore, it is worthwhile to pour the efforts into the development of a docking program with fast speed and high quality of the solutions obtained. The research presented in this paper, based on the docking scheme of Vina, developed a novel docking program called RDPSOVina. The RDPSOVina employes a novel search algorithm but the same scoring function of Vina. It utilizes the random drift particle swarm optimization (RDPSO) algorithm as the global search algorithm, implements the local search with small probability, and applies Markov chain mutation to the particles’ personal best positions in order to harvest more potential-candidates. To prove the outstanding docking performance in RDPSOVina, we performed the re-docking experiments on two PDBbind datasets and cross-docking experiments on the Sutherland-crossdock-set, respectively. The RDPSOVina exhibited superior protein–ligand docking accuracy and better cross-docking prediction with higher operation efficiency than most of the compared methods. It is available at https://github.com/li-jin-xing/RDPSOVina.
PubDate: 2022-05-09
DOI: 10.1007/s10822-022-00455-4

• Is there a common allosteric binding site for G-protein coupled
receptors'

Abstract: Abstract Targeting the allosteric sites on G-protein coupled receptors (GPCRs) for drug discovery is attracting increased interest. Given a GPCR target, identifying the allosteric binding sites in it remains a challenge. Previous works from our and other labs suggest the intracellular region below the middle of the transmembrane (TM) domain that spatially overlaps with the G-protein binding site could contain a common allosteric site for all GPCRs. We performed several bioinformatics analyses on this site for more than 100 representative human GPCR structures. Results of the studies confirmed that the proposed region contains an allosteric site that is druggable for 89% of the GPCRs and is not 100% identical between a GPCR and its most similar homolog for 94% of the GPCRs. The physico-chemical properties and amino acid composition of this site vary among and within GPCR classes. Since this proposed region occupies the space existing in all GPCRs of known structure, it could represent a common host of an allosteric site for all GPCRs that can be targeted for structure-based allosteric drug design.
PubDate: 2022-05-04
DOI: 10.1007/s10822-022-00454-5

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