Backbone RMSD Research Articles

Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics.

Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.

Exploring Therapeutic Potential of Nutraceutical Compounds from Propolis on MAPK1 Protein Using Bioinformatics Approaches as Anti-Coronavirus Disease 2019 (COVID-19)

This study explores the potential of propolis, a natural substance, as a gene therapy for treating COVID-19. Despite the advent of COVID-19 vaccines, their side effects pose new health challenges. Utilizing network pharmacology, this research identifies propolis compounds through various databases and assesses their ability to target proteins associated with COVID-19. MAPK1 emerges as a potential therapeutic target, and molecular docking reveals Broussoflavonol F, Glyasperin A, and Sulabiroins as promising compounds with strong binding affinities, i.e.,- 9.0, -9.0, and -8.8 kcal/mol, respectively, exceeding the native ligand (-7.2 kcal/mol). Molecular Dynamics displays stable complex behavior, with backbone RMSD values consistently below 4 Angstroms and RMSF simulations showing minimal fluctuations within ±2 Angstroms error. Moreover, MM-PBSA analysis further supports the strong binding of Broussoflavonol F, Glyasperin A, and Sulabiroins A, with relative binding energies of -122.82±89.65, 131.48±95.39, and -155.97±111,37 kJ/mol, respectively. These results indicate that propolis has potential as an anti-COVID-19 agent, primarily through inhibiting the MAPK1 pathway. However, further research is needed to validate these results and develop practical applications for COVID-19 therapy. This study underscores the significance of network pharmacology and computational models in understanding propolis mechanisms, offering potential directions for future research and treatment strategies against COVID-19.

Carbonylbis(hydrazine-1-carbothioamide) derivatives as a new class of α-glucosidase inhibitors and their mechanistic insights via molecular docking and dynamic simulations.

In the past, efforts have been made to find a cure for diabetes, mainly evaluating new classes of compounds to explore their potency. In this study, we present the synthesis and evaluation of carbonylbis(hydrazine-1-carbothioamide) derivatives as potential α-glucosidase inhibitors, employing both in vivo and in silico investigations. The in vitro experiments revealed that all tested compounds were significantly potent for α-glucosidase inhibition, with the lead compound 3a displaying approximately 80 times higher activity than acarbose. To delve deeper, in silico induced fit docking, pharmacokinetics, and molecular dynamics studies were conducted. Significantly, compound 3a exhibited a docking score of -7.87 kcal/mol, surpassing acarbose, which had a docking score of -6.59 kcal/mol. The in silico ADMET indicated that most of the synthesized compounds have properties conducive to drug development. Molecular dynamics analysis demonstrated that, when the ligand 3a was coupled with the target 3TOP, Cα-RMSD backbone RMSD values below 2.4 Å and "Lig_fit_Prot" values below 2.7 Å were observed. QSAR analysis demonstrates that the "fOC8A" descriptor positively correlates with α-glucosidase inhibition activity, while "lipoplus_AbSA" positively contributes and "notringC_notringO_8B" negatively contributes to this activity.

Molecular Dynamics Simulation of Serotonin Transport Protein Complex with 6-Hydroxy-1-Methyl-1,2,3,4-Tetrahydro-β-Carboline Ligand from Chocolate (Theobroma cacao L.) Isolate

6-hydroxy-1-methyl1,2,3,4-tetrahydro-β-carboline (6OHMTHβC) is a chocolate derivate that has antidepressant potency. It can increase dopamine and serotonin secretion, which leads to mood improvement. This method was carried out using computational molecular docking simulations (in silico). The research design was computational-based exploratory descriptive. The results of molecular docking showed the lowest energy score and the backbone RMSD value ≤2Å. The procedure performed was 6AWP receptor docking without ligand, with native ligand (6OHMTHβC), and with reference ligand (fluvoxamine). This study also performed molecular docking simulations of 6OHMTHβC towards 6AWP to find compounds in the receptor binding pocket. This study also performed dynamics simulations and identified the molecular determinants using PyPLIF-HIPPOS and YASARA Structure software 20.1.24.10 with the Windows 10 operating system. This study succeeded in determining the stability of the dynamics simulation of the serotonin transport protein complex with the reference ligand 6OHMTHβC for 50 ns, and this result corresponds to the RMSD value and binding energy. The determination of binding energy (BE) was calculated from the BE calculation available at YASARA and Ubuntu. The binding energy value of the original ligand was -11.6590 kJ/mol, and the reference ligand was -83880 kJ/mol. The highest RMSD value of the original ligand was 1.39292Å, while the RMSD value of the reference ligand was 1.71072Å. The essential amino acid carried out was 438Ser with hydrogen bond interactions, so 6OHMTHβC was considered a competent antidepressant candidate.

Molecular interaction of a putative inhibitor with bacterial SHV, an enzyme associated with antibiotic resistance.

Tackling the ever-looming threat of antibiotic resistance remains a challenge for clinicians and microbiologists across the globe. Sulfhydryl variable (SHV) is a known bacterial enzyme associated with antibiotic resistance. The SHV enzyme has many variants. The present article describes identification and molecular interaction of a putative inhibitor with the bacterial SHV enzyme as a step towards novel antibacterial drug discovery. The MCULE-platform was used for screening a collection of 5 000 000 ligand molecules to evaluate their binding potential to the bacterial SHV-1 enzyme. Estimation of pharmacokinetic features was realized with the aid of the 'SWISS ADME' tool. Toxicity-checks were also performed. The docked complex of 'the top screened out ligand' and 'the bacterial SHV-1 protein' was subjected to molecular dynamics simulation of 101 ns. The obtained ligand molecule, 1,1'-(4H,8H-Bis[1,2,5]oxadiazolo[3,4-b:3',4'-e]pyrazine-4,8-diyl)diethanone, displayed the most favourable binding interactions with bacterial SHV-1. A total of 15 amino acid residues were found to hold the ligand in the binding site of SHV-1. Noticeably, 12 of the 15 residues were found as common to the binding residues of the reference (PDB ID: 4ZAM). The RMSD values plotted against the simulation time showed that nearby 11 ns, equilibrium was reached and, thenceforth, the 'SHV-1-Top ligand' complex remained typically stable. Starting from around 11 ns and straight to 101 ns, the backbone RMSD fluctuations were found to be confined inside a range of 1.0-1.6 Å. The ligand, 1,1'-(4H,8H-Bis[1,2,5]oxadiazolo[3,4-b:3',4'-e]pyrazine-4,8-diyl)diethanone, satisfied ADMET criteria. Furthermore, the practicability of the described 'SHV-1-Top ligand' complex was reinforced by a comprehensive molecular dynamics simulation of 101 ns. This ligand hence can be considered a promising lead for antibiotic design against SHV-1 producing resistant bacteria, and thus warrants wet laboratory evaluation.

Molecular Structure of Cu(II)-Bound Amyloid-β Monomer Implicated in Inhibition of Peptide Self-Assembly in Alzheimer’s Disease

Metal ions, such as copper and zinc ions, have been shown to strongly modulate the self-assembly of the amyloid-β (Aβ) peptide into insoluble fibrils, and elevated concentrations of metal ions have been found in amyloid plaques of Alzheimer's patients. Among the physiological transition metal ions, Cu(II) ions play an outstanding role since they can trigger production of neurotoxic reactive oxygen species. In contrast, structural insights into Cu(II) coordination of Aβ have been challenging due to the paramagnetic nature of Cu(II). Here, we employed specifically tailored paramagnetic NMR experiments to determine NMR structures of Cu(II) bound to monomeric Aβ. We found that monomeric Aβ binds Cu(II) in the N-terminus and combined with molecular dynamics simulations, we could identify two prevalent coordination modes of Cu(II). For these, we report here the NMR structures of the Cu(II)-bound Aβ complex, exhibiting heavy backbone RMSD values of 1.9 and 2.1 Å, respectively. Further, applying aggregation kinetics assays, we identified the specific effect of Cu(II) binding on the Aβ nucleation process. Our results show that Cu(II) efficiently retards Aβ fibrillization by predominately reducing the rate of fibril-end elongation at substoichiometric ratios. A detailed kinetic analysis suggests that this specific effect results in enhanced Aβ oligomer generation promoted by Cu(II). These results can quantitatively be understood by Cu(II) interaction with the Aβ monomer, forming an aggregation inert complex. In fact, this mechanism is strikingly similar to other transition metal ions, suggesting a common mechanism of action of retarding Aβ self-assembly, where the metal ion binding to monomeric Aβ is a key determinant.

In depth molecular interaction analyses of the complex of a proposed CTXM-inhibitor bound to the bacterial enzyme

A ‘Thumb Rule for Antibiotic Design’ against bacteria can be given as, ‘The minimum pace of drug design ought to match the swiftness with which bacteria display cutting-edge resistance mechanisms; thereby outwitting the antibiotics and, in turn, the researchers’. Occurrence of drug resistance attributable to CXTM-variants in bacterial pathogens is widespread. In line with our above proposed thumb rule, the present article employed concatenation of virtual screening, docking and simulation to identify a potent in silico validated anti-CTXM-14 ligand. Specifically, this research used the ‘MCULE’ drug discovery platform to screen a total of 5 million candidate inhibitors to evaluate their binding efficacy with an antibiotic resistance enzyme, CTXM-14 found in bacterial pathogens. A new median approach between ‘structure’ and ‘ligand’-based protocols was employed. Pharmacokinetic profiling was achieved by ‘SWISS ADME’. Safety profile for humans was appraised by ‘Toxicity Checker’. The complex consisting of the ‘Top ligand’ (obtained from the screen) harbored within the active pocket of the bacterial CTXM-14 was subjected to 60 ns molecular dynamics simulation with the aid of licensed YASARA STRUCTURE v.21.8.27. Complex tasks were performed by YANACONDA. Fine resolution figures (notably, plots generated from trajectory analyses) were constructed. Simulation snaps were acquired at every 250 picoseconds of the run. The ligand having the IUPAC name as 1-Amino-3-(4-hydroxyphenyl)pyrido[1,2-a]benzimidazole-2,4-dicarbonitrile demonstrated the overall best binding with CTXM-14. Fifteen amino acid residues were found to line the interacting pocket. Remarkably, all of these interacting residues were found to be present among the interacting residues displayed by the reference complex as well, i.e. CTXM-14:Vaborbactam complex (PDB ID 6V7H). A total of 240 simulation snaps were retrieved. The RMSD plot revealed that a plateau was achieved at 32 ns, after which the backbone RMSD fluctuations remained confined within 1.4-2 Å. Video recording of molecular actions was also achieved. In conclusion, this study provides a fresh lead molecule, 1-Amino-3-(4-hydroxyphenyl)pyrido[1,2-a]benzimidazole-2,4-dicarbonitrile against bacterial CTXM-14 protein. The study utilized a new median approach between ‘structure’ and ‘ligand’-based drug design. The lead molecule passed ADMET conditions and an array of medicinal chemistry filters, and is further supported by a stable molecular dynamics. An acceptable skin permeation supports its probable use in antibiotic creams. Moreover, the study provides a clear ‘Thumb Rule for Antibiotic Design’ against bacteria, which although often assumed, can be clearly stated for the first time. Synthesis of the screening-proposed molecule followed by in-vitro and in-vivo validation is highly recommended. Communicated by Ramaswamy H. Sarma

Extensive Molecular Dynamics Simulations Disclosed the Stability of mPGES-1 Enzyme and the Structural Role of Glutathione (GSH) Cofactor.

A deep in silico investigation of various microsomal prostaglandin E2 synthase-1 (mPGES-1) protein systems is here reported using molecular dynamics (MD) simulations. Firstly, eight different proteins models (Models A-H) were built, starting from the active enzyme trimer system (Model A), namely that bound to three glutathione (GSH) cofactor molecules, and then gradually removing the GSHs (Models B-H), simulating each of them for 100 ns in explicit solvent. The analysis of the obtained data disclosed the structural role of GSH in the chemical architecture of mPGES-1 enzyme, thus suggesting the unlikely displacement of this cofactor, in accordance with experimentally determined protein structures co-complexed with small molecule inhibitors. Afterwards, Model A was submitted to microsecond-scale molecular dynamics simulation (total simulation time=10 μs), in order to shed light about the dynamical behaviour of this enzyme at atomic level and to obtain further structural features and protein function information. We confirmed the structural stability of the enzyme machinery, observing a conformational rigidity of the protein, with a backbone RMSD of ∼3 Å along the simulation time, and highlighting the strong active contribution of GSH molecules due to their active role in packing the protein chains through a tight binding at monomer interfaces. Furthermore, the focused analysis on R73 residue disclosed its role in solvent exchange events, probably excluding its function as route for GSH to enter towards the endoplasmic reticulum membrane, in line with the recently reported function of cap domain residues F44-D66 as gatekeeper for GSH entrance into catalytic site.

Validated determination of NRG1 Ig-like domain structure by mass spectrometry coupled with computational modeling

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

Matching protein surface structural patches for high-resolution blind peptide docking

Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Study on the Characteristics of Small-Molecule Kinase Inhibitors-Related Drug-Induced Liver Injury.

Background and Aim: More than half of the small-molecule kinase inhibitors (KIs) induced liver injury clinically. Meanwhile, studies have shown a close relationship between mitochondrial damage and drug-induced liver injury (DILI). We aimed to study KIs and the binding between drugs and mitochondrial proteins to find factors related to DILI occurrence. Methods: A total of 1,223 oral FDA-approved drugs were collected and analyzed, including 44 KIs. Fisher’s exact test was used to analyze DILI potential and risk of different factors. A total of 187 human mitochondrial proteins were further collected, and high-throughput molecular docking was performed between human mitochondrial proteins and drugs in the data set. The molecular dynamics simulation was used to optimize and evaluate the dynamic binding behavior of the selected mitochondrial protein/KI complexes. Results: The possibility of KIs to produce DILI is much higher than that of other types (OR = 46.89, p = 9.28E-13). A few DILI risk factors were identified, including molecular weight (MW) between 400 and 600, the defined daily dose (DDD) ≥ 100 mg/day, the octanol–water partition coefficient (LogP) ≥ 3, and the degree of liver metabolism (LM) more than 50%. Drugs that met this combination of rules were found to have a higher DILI risk than controls (OR = 8.28, p = 4.82E-05) and were more likely to cause severe DILI (OR = 8.26, p = 5.06E-04). The docking results showed that KIs had a significant higher affinity with human mitochondrial proteins (p = 4.19E-11) than other drug types. Furthermore, the five proteins with the lowest docking score were selected for molecular dynamics simulation, and the smallest fluctuation of the backbone RMSD curve was found in the protein 5FS8/KI complexes, which indicated the best stability of the protein 5FS8 bound to KIs. Conclusions: KIs were found to have the highest odds ratio of causing DILI. MW was significantly related to the production of DILI, and the average docking scores of KI drugs were found to be significantly different from other classes. Further analysis identified the top binding mitochondrial proteins for KIs, and specific binding sites were analyzed. The optimization of molecular docking results by molecular dynamics simulation may contribute to further studying the mechanism of DILI.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

Molecular interaction of inhibitors with human brain butyrylcholinesterase.

Alzheimer's disease is a topic of deep research interest across the global scientific community. Butyrylcholinesterase (BuChE) is an important enzyme, and an interesting anti-Alzheimer's target. Identification or fresh design of promising BuChE-inhibitors is warranted. Virtual screening supported by molecular dynamics simulations has emerged as a key component of present drug-discovery cascades. The research piece aimed at identification of a putative BuChE-inhibitor as a fresh molecular frame that might aid drug design in the context of Alzheimer's disease. The study utilized 'MCULE' to screen a set of 5 million ligands to test their ability to bind to human BuChE. Pharmacokinetic profiling was achieved by the 'SWISS ADME' program. Toxicities were duly assessed. YASARA STRUCTURE version 20.10.4.W.64 was employed to run 133 ns molecular dynamics (MD) simulation for the complex of 'the top screened out inhibitor' and 'the human BuChE enzyme'. The simulation was executed for approx. 4 days (~93 hrs) on an HP ZR30w workstation. YANACONDA, a special language contained in YASARA STRUCTURE was employed to perform complex tasks. Fine resolution figures (notably the RMSD vs time plot) were created. Snapshots were extracted at every 250 ps. The selected ligand, (3-Bromophenyl)[5-(4-chlorophenyl)-5-hydroxy-3-(trifluoromethyl)-4,5-dihydro-1H-pyrazol-1-yl]methanone, exhibited the best overall binding with human BuChE. It interacted with human BuChE through 19 residues. Markedly, 9 of the 19 residues were confirmed to be matching to those of the reference complex (PDB ID 5DYW). Trajectory analysis returned 533 snapshots. The RMSD versus time plot indicated that around 22 ns, equilibrium was achieved and, from then on, the 'BuChE-Top inhibitor' complex remained predominantly stable. From 22 ns and onwards till 133 ns, the backbone RMSD fluctuations were observed to remain limited within a range of 1.2-1.9 Å. The molecule, (3-Bromophenyl)[5-(4-chlorophenyl)-5-hydroxy-3-(trifluoromethyl)-4,5-dihydro-1H-pyrazol-1-yl]methanone, satisfied ADMET requirements. Additionally, the feasibility of the proposed enzyme-inhibitor complex was supported by an adequately extended MD simulation of 133 ns. Hence, the proposed molecule could be a likely lead for designing inhibitor(s) against human BuChE. Scope remains for validatory wet laboratory investigation.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

Computational design of a synthetic PD-1 agonist

Programmed cell death protein-1 (PD-1) expressed on activated T cells inhibits T cell function and proliferation to prevent an excessive immune response, and disease can result if this delicate balance is shifted in either direction. Tumor cells often take advantage of this pathway by overexpressing the PD-1 ligand PD-L1 to evade destruction by the immune system. Alternatively, if there is a decrease in function of the PD-1 pathway, unchecked activation of the immune system and autoimmunity can result. Using a combination of computation and experiment, we designed a hyperstable 40-residue miniprotein, PD-MP1, that specifically binds murine and human PD-1 at the PD-L1 interface with a Kd of ∼100 nM. The apo crystal structure shows that the binder folds as designed with a backbone RMSD of 1.3 Å to the design model. Trimerization of PD-MP1 resulted in a PD-1 agonist that strongly inhibits murine T cell activation. This small, hyperstable PD-1 binding protein was computationally designed with an all-beta interface, and the trimeric agonist could contribute to treatments for autoimmune and inflammatory diseases.

High Throughput Virtual Screening and Molecular Dynamics Simulation for Identifying a Putative Inhibitor of Bacterial CTX-M-15.

Background: Multidrug resistant bacteria are a major therapeutic challenge. CTX-M-type enzymes are an important group of class A extended-spectrum β-lactamases (ESBLs). ESBLs are the enzymes that arm bacterial pathogens with drug resistance to an array of antibiotics, notably the advanced-generation cephalosporins. The current need for an effective CTX-M-inhibitor is high. Objective: The aim of the current study was to identify a promising anti-CTX-M-15 ligand whose chemical skeleton could be used as a ‘seed-molecule’ for future drug design against resistant bacteria. Methods: Virtual screening of 5,000,000 test molecules was performed by ‘MCULE Drug Discovery Platform’. ‘ADME analyses’ was performed by ‘SWISS ADME’. TOXICITY CHECKER of MCULE was employed to predict the safety profile of the test molecules. The complex of the ‘Top inhibitor’ with the ‘bacterial CTX-M-15 enzyme’ was subjected to 102.25 ns molecular dynamics simulation. This simulation was run for 3 days on a HP ZR30w workstation. Trajectory analyses were performed by employing the macro ‘md_analyze.mcr’ of YASARA STRUCTURE version 20.12.24.W.64 using AMBER14 force field. YANACONDA macro language was used for complex tasks. Figures, including RMSD and RMSF plots, were generated. Snapshots were acquired after every 250 ps. Finally, two short videos of ‘41 s’ and ‘1 min and 22 s’ duration were recorded. Results: 5-Amino-1-(2H-[1,2,4]triazino[5,6-b]indol-3-yl)-1H-pyrazole-4-carbonitrile, denoted by the MCULE-1352214421-0-56, displayed the most efficient binding with bacterial CTX-M-15 enzyme. This screened molecule significantly interacted with CTX-M-15 via 13 amino acid residues. Notably, nine amino acid residues were found common to avibactam binding (the reference ligand). Trajectory analysis yielded 410 snapshots. The RMSD plot revealed that around 26 ns, equilibrium was achieved and, thereafter, the complex remained reasonably stable. After a duration of 26 ns and onwards until 102.25 ns, the backbone RMSD fluctuations were found to be confined within a range of 0.8–1.4 Å. Conclusion: 5-Amino-1-(2H-[1,2,4]triazino[5,6-b]indol-3-yl)-1H-pyrazole-4-carbonitrile could emerge as a promising seed molecule for CTX-M-15-inhibitor design. It satisfied ADMET features and displayed encouraging ‘simulation results’. Advanced plots obtained by trajectory analyses predicted the stability of the proposed protein-ligand complex. ‘Hands on’ wet laboratory validation is warranted.

NMR structure and dynamics of Q4DY78, a conserved kinetoplasid-specific protein from Trypanosoma cruzi

The 106-residue protein Q4DY78 (UniProt accession number) from Trypanosoma cruzi is highly conserved in the related kinetoplastid pathogens Trypanosoma brucei and Leishmania major. Given the essentiality of its orthologue in T. brucei, the high sequence conservation with other trypanosomatid proteins, and the low sequence similarity with mammalian proteins, Q4DY78 is an attractive protein for structural characterization. Here, we solved the structure of Q4DY78 by solution NMR and evaluated its backbone dynamics. Q4DY78 is composed of five α -helices and a small, two-stranded antiparallel β-sheet. The backbone RMSD is 0.22 ± 0.05 Å for the representative ensemble of the 20 lowest-energy structures. Q4DY78 is overall rigid, except for N-terminal residues (V8 to I10), residues at loop 4 (K57 to G65) and residues at the C-terminus (F89 to F112). Q4DY78 has a short motif FPCAP that could potentially mediate interactions with the host cytoskeleton via interaction with EVH1 (Drosophila Enabled (Ena)/Vasodilator-stimulated phosphoprotein (VASP) homology 1) domains. Albeit Q4DY78 lacks calcium-binding motifs, its fold resembles that of eukaryotic calcium-binding proteins such as calcitracin, calmodulin, and polcacin Bet V4. We characterized this novel protein with a calcium binding fold without the capacity to bind calcium.

Multiobjective heuristic algorithm for de novo protein design in a quantified continuous sequence space

Protein design usually involves sequence search process and evaluation criteria. Commonly used methods primarily implement the Monte Carlo or simulated annealing algorithm with a single-energy function to obtain ideal solutions, which is often highly time-consuming and limited by the accuracy of the energy function. In this report, we introduce a multiobjective algorithm named Hydra for protein design, which employs two different energy functions to optimize solutions simultaneously and makes use of the latent quantitative relationship between different amino acid types to facilitate the search process. The framework uses two kinds of prior information to transform the original disordered discrete sequence space into a relatively ordered space, and decoy sequences are searched in this ordered space through a multiobjective swarm intelligence algorithm. This algorithm features high accuracy and a high-speed search process. Our method was tested on 40 targets covering different fold classes, which were computationally verified to be well folded, and it experimentally solved the 1UBQ fold by NMR in excellent agreement with the native structure with a backbone RMSD deviation of 1.074 Å. The Hydra software package can be downloaded from: http://www.csbio.sjtu.edu.cn/bioinf/HYDRA/ for academic use.

Identification of Selective Novel Hits against Plasmodium falciparum Prolyl tRNA Synthetase Active Site and a Predicted Allosteric Site Using in silico Approaches.

Recently, there has been increased interest in aminoacyl tRNA synthetases (aaRSs) as potential malarial drug targets. These enzymes play a key role in protein translation by the addition of amino acids to their cognate tRNA. The aaRSs are present in all Plasmodium life cycle stages, and thus present an attractive malarial drug target. Prolyl tRNA synthetase is a class II aaRS that functions in charging tRNA with proline. Various inhibitors against Plasmodium falciparum ProRS (PfProRS) active site have been designed. However, none have gone through clinical trials as they have been found to be highly toxic to human cells. Recently, a possible allosteric site was reported in PfProRS with two possible allosteric modulators: glyburide and TCMDC-124506. In this study, we sought to identify novel selective inhibitors targeting PfProRS active site and possible novel allosteric modulators of this enzyme. To achieve this, virtual screening of South African natural compounds against PfProRS and the human homologue was carried out using AutoDock Vina. The modulation of protein motions by ligand binding was studied by molecular dynamics (MD) using the GROningen MAchine for Chemical Simulations (GROMACS) tool. To further analyse the protein global motions and energetic changes upon ligand binding, principal component analysis (PCA), and free energy landscape (FEL) calculations were performed. Further, to understand the effect of ligand binding on the protein communication, dynamic residue network (DRN) analysis of the MD trajectories was carried out using the MD-TASK tool. A total of ten potential natural hit compounds were identified with strong binding energy scores. Binding of ligands to the protein caused observable global and residue level changes. Dynamic residue network calculations showed increase in betweenness centrality (BC) metric of residues at the allosteric site implying these residues are important in protein communication. A loop region at the catalytic domain between residues 300 and 350 and the anticodon binding domain showed significant contributions to both PC1 and PC2. Large motions were observed at a loop in the Z-domain between residues 697 and 710 which was also in agreement with RMSF calculations that showed increase in flexibility of residues in this region. Residues in this loop region are implicated in ATP binding and thus a change in dynamics may affect ATP binding affinity. Free energy landscape (FEL) calculations showed that the holo protein (protein-ADN complex) and PfProRS-SANC184 complexes were stable, as shown by the low energy with very few intermediates and hardly distinguishable low energy barriers. In addition, FEL results agreed with backbone RMSD distribution plots where stable complexes showed a normal RMSD distribution while unstable complexes had multimodal RMSD distribution. The betweenness centrality metric showed a loss of functional importance of key ATP binding site residues upon allosteric ligand binding. The deep basins in average L observed at the allosteric region imply that there is high accessibility of residues at this region. To further analyse BC and average L metrics data, we calculated the ΔBC and ΔL values by taking each value in the holo protein BC or L matrix less the corresponding value in the ligand-bound complex BC or L matrix. Interestingly, in allosteric complexes, residues located in a loop region implicated in ATP binding had negative ΔL values while in orthosteric complexes these residues had positive ΔL values. An increase in contact frequency between residues Ser263, Thr267, Tyr285, and Leu707 at the allosteric site and residues Thr397, Pro398, Thr402, and Gln395 at the ATP binding TXE loop was observed. In summary, this study identified five potential orthosteric inhibitors and five allosteric modulators against PfProRS. Allosteric modulators changed ATP binding site dynamics, as shown by RMSF, PCA, and DRN calculations. Changes in dynamics of the ATP binding site and increased contact frequency between residues at the proposed allosteric site and the ATP binding site may explain how allosteric modulators distort the ATP binding site and thus might inhibit PfProRS. The scaffolds of the identified hits in the study can be used as a starting point for antimalarial inhibitor development with low human cytotoxicity.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set.

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .